Top medical minds join forces with leading cognitive AI provider to guide artificial intelligence powered innovation for the healthcare industry

“The healthcare industry is eager for AI innovations to help medical professionals care for people,” says AJ Abdallat, Beyond Limits CEO. “With the guidance of our esteemed advisory board, we are ready to contribute advanced technology solutions for this all-important mission.”
Beyond Limits Healthcare Advisory Board

Dr. Wael Barsoum, CEO and President, Cleveland Clinic Florida Region, Cleveland Clinic Board of Governors Member, Fellow of the American Board of Orthopaedic Surgeons and the AAOS.

Dr. Bala Manian, successful Silicon Valley serial entrepreneur, founder of multiple companies including ReaMetrix, Quantum Dot Corp., SurroMed, Biometric Imaging, Lumisys, and Molecular Dynamics.

Dr. Sanjiv Narayan, Professor of Medicine, Stanford University; Cardiologist, Bioengineer, Fellow of the American College of Cardiology. Founder of several medical technology startups. In addition to his MD, Dr. Narayan holds an MSc in Computer Science.

Dr. Manish Kohli, Global Board Chair and Fellow, HIMSS; Former CMIO and Head of Healthcare Informatics, Dubai Health Care City/ University Hospital (Dubai), CMIO, Cleveland Clinic (Abu Dhabi), CMIO, Johns Hopkins Community Physicians (Baltimore); Board Certified – Clinical Informatics, Family Medicine. Member, Standards Advisory Panel, Joint Commission International (JCI)

Dr. Steven Tucker, Preventive Medical Oncologist & Founder of Singapore-based group practice, Tucker Medical; Chief Medical Officer at insurtech start-up, CXA Group; and Director of Oncology & Genomics at MetLife Asia.

Dr. Douglas Johnston, Program Director for Thoracic Surgery, Cleveland Clinic, Committee Member, Society of Thoracic Surgeons, Cleveland Clinic Accountable Care Organization Board Member.

“We are honored to have the benefit of six extremely knowledgeable healthcare industry experts on our team,” says Dr. Manikanda Arunachalam, MD, Beyond Limits Head of Healthcare and SVP Corporate Development & Investments. “This is a world class team that fully understands the power that Beyond Limits cognitive AI can apply to solve difficult healthcare problems.”
Beyond Limits builds cognitive AI systems that interpret vast amounts of data from disparate sources to produce actionable information. For example, historical patient data, lab results, chart notes, real-time sensor monitoring, evidence-based clinical guidelines, and drug interactions, etc., can be interpreted by the system to better understand and personalize treatment suggestions.
Because medical decisions are important and frequently expensive, an AI system must be able to explain its thought process and conclusions. Unlike conventional “black box” approaches like machine learning, deep learning or neural networks that cannot explain their reasoning, Beyond Limits cognitive AI delivers clear explanations of its cognitive reasoning in transparent, evidence-based audit trails, including risk and uncertainties.
Beyond Limits cognitive AI technology combines conventional numeric AI with advanced symbolic logic for human-like reasoning to improve insights, inform decision-making, and reduce risk at the point of care. The company’s technology is considered to be a cognitive leap beyond conventional AI to a human-like ability to perceive, understand, correlate, learn, teach, reason and solve problems faster than existing AI solutions.

Beyond Limits CEO, AJ Abdallat was recently interviewed by the Oil & Gas Council in anticipation of his keynote address at the World Energy Capital Assembly. The 10th World Energy Capital Assembly is held from December 3-4, 2018 in London, UK and is Europe’s largest gathering of international energy executives, investors, and financiers. AJ Abdallat will also join a panel of influential leaders in the industry to discuss the topic of  “Digitalization 70 – is the Oil & Gas Sector keeping up?” Read Full Article here

Can you tell us about your journey that ended up in CEO of Beyond Limits?
I started in 1998 working with Caltech, which manages the Jet Propulsion Laboratory (JPL), which is a NASA center. We wanted to commercialize technologies that were developed for the space program, including smart sensors and artificial intelligence. Since then, I have founded several companies to bring innovative NASA/JPL space technology to market, the latest of which is Beyond Limits, which was launched in 2014 and focuses on Artificial Intelligence, specifically human-like reasoning.
The AI solutions developed by Beyond Limits magnify human talent, enabling people to apply their attention, experience, and their passions to solving problems that truly matter. We focus on solving big problems for large scale industries that touch millions of people.
In energy, we’re partnering with a giant energy production company. We’re trying to help them increase and expand all production with mature subsurface reservoirs. Ultimately the goal is to provide a cognitive ability, so they can understand where to drill next. This technology is going to lower the cost of production. It’s going to make oil and gas less expensive. It’s going to help everyone.
What, in your view, has been the key to Beyond Limits’ success?
Exploring space is about being able to handle the unknown. Real-world, industrial-grade AI needs to do the same to handle business issues at scale, across different industries — that’s exactly what we’re doing at Beyond Limits.
Our approach goes beyond conventional AI by bringing human-like reasoning and cognitive reasoning to the equation and by explaining the rationale and evidence behind the recommended course of action. Conventional AI systems are what we refer to as black box implementations, where systems are trained based on data, but cannot explain how they got the answer. At Beyond Limits we can explain how we got the answer because our systems provide an audit trail that justifies the rationale and provides evidence for the answer.
Beyond Limits’ cognitive Intelligence has strong roots in bio-inspired algorithms, which mimics the functions of the human brain. Our systems are both educated and trained using captured data. But unlike most of the competition, our systems are also educated by facts, situational awareness, and human knowledge. This means we can solve problems that neural nets cannot, especially when the data is sparse or unreliable.
Digitalisation has been a hot topic for many years but seems to have lower penetration in the oil and gas industry versus other industries – do you agree or disagree with this statement and why?
Oil and gas companies were pioneers in leveraging the first digital age in 1980’s making use of seismic analysis to boost operational efficiencies. Unfortunately, they have not fully taken advantage of the second digital revolution. Digital readiness will be a key factor for organization to take advantage of advancement in technologies and AI to significantly boost productivity and performance. Companies that will make their organization more digital will have a huge advantage.
What can the Oil & Gas industry and those that finance, and invest in, the industry learn from other industries that you have worked in?
Beyond Limits is used to dealing with space where even when things go wrong, or data is missing, you need systems that can think through the situation and come up with a solution, so the mission can continue. For Oil & Gas explorers with remote installations in the North Sea, the Arctic circle, or in the middle of the desert, circumstances are similarly unpredictable.
AI has been used to develop and advance numerous fields and industries, but we need to go beyond conventional AI in industries such as Oil & Gas exploration where there could be sparse or unreliable data. Cognitive intelligence goes beyond conventional AI by bringing human-like reasoning and cognitive reasoning to the equation. Industries like Oil & Gas should be using cognitive intelligence like Beyond Limits solutions which utilize human knowledge to learn context and meaning so they can make good recommendations to people for faster, better, decision-making that reduces risk and lowers production costs.
What will the market’s best performing companies of tomorrow look like?
The best performing companies of tomorrow will embrace digitalization. Gartner predicts AI will create 2.3 million jobs by 2020. The question is no longer whether AI will fundamentally change the workplace. It’s happening. The true question is how companies can successfully use technologies and AI in ways that enables the human workforce, helping to make humans faster, more efficient and more productive.

The Tech Tribune staff has compiled the very best tech startups in Glendale, California. In doing their research, they considered several factors including but not limited to:
1. Revenue Potential
2. Leadership Team
3. Brand/Product Traction
4. Competitive Landscape
Additionally, all companies must be independent (un-acquired), privately owned, at most 10 years old, and have received at least one round of funding in order to qualify.
Click here to see the full list.

Read full article from Total Prestige here.

AJ Abdallat is a serial entrepreneur. He loves to create and the businesses he puts his hands on tend to succeed. Abdallat’s latest company is Beyond Limits, a leading developer of advanced artificial intelligence solutions and leading the way in developing the technology in innovative ways. Abdallat took over the CEO reins of Beyond Limits in 2014. During his time with the company, the entrepreneur has pushed the brand to tackle industrial and enterprise challenges.

A graduate of the University of California, Berkeley, Abdallat has been working with dynamic companies since 1988 as co-founder or CEO of several Caltech/Jet Propulsion Laboratory (JPL) startups. With a long track record in AI, Abdallat hopes to steer Beyond Limits — and the world — into a future that finally fulfills the promises of technology.

Please tell us how corporations and industries can benefit from Beyond Limits?
Beyond Limits is an AI company, but we are taking a different approach to AI. Today’s artificial intelligence market is not easy to quantify. Besides the lack of consensus on a coherent definition for “artificial intelligence” as a term, the field’s nascent stage of development makes it difficult to carve out silos or hard barriers of where one industry or application ends, and another begins. What makes us unique is our approach goes beyond conventional AI by bringing human-like reasoning and cognitive reasoning to the equation. AI enable companies to grapple with gargantuan amounts of data and do diagnostic and prognostic analysis to predict what’s going to happen and what should happen. AI’s good at that. Our systems go beyond conventional AI by explaining the rationale and evidence behind the recommended course of action.
How did your professional background prepare you for your current position?
I worked for both large and startup companies. Learned a great deal from both environments. I learned about prioritizing and focusing on what’s really important. How to effectively manage teams by setting a clear mission and directions that you rally your team around. Success does not come true in one day, years of hard work is needed.
Please tell us how the company began and what some of the highlights have been?
In 1998, Dr. Carl Kukkonen and I started working with Caltech, which manages the Jet Propulsion Laboratory for NASA. With the support of Caltech president, Dr. David Baltimore, we set on a course to commercialize technologies that were developed for the space program. One example was a technology called the tunable diode laser which was developed to accurately detect water vapor on the surface of Mars. Mars has a very harsh and unforgiving environment. We created a company to apply our technology to the energy industry, because moisture is a big contaminant for natural gas pipelines. The company became very successful and a leader in its field. Now, I am focusing on our latest company, Beyond Limits, which focuses on Artificial Intelligence and specifically human-like reasoning.
Currently Beyond Limits focuses on three verticals. In energy, we’re partnering with a giant energy production company. We’re trying to help them increase and expand all production with mature subsurface reservoirs. Ultimately the goal is to deliver a cognitive ability, so they can understand what to drill next. That’s going to lower the cost of production. It’s going to make oil and gas less expensive. It’s going to help everyone.
We’re working with healthcare providers to accurately diagnose patient conditions with a high degree of confidence. Eventually that will save lives and produce a more efficient system. We’re also working with major financial institutions on applying cognitive reasoning to Fintech. One of the big highlights for me is that we’ve proved that our cognitive approach works for very tough and demanding industrial applications. What we said we could do, we did. In one opportunity, we were the fourth AI company to be engaged. But where the others hadn’t panned out, we won the business and proved that our methods work. We won the opportunity, but more than that, the ultimate compliment was when our customer became our strategic investor.
What really sets Beyond Limits apart from competitors?
A lot of things set us apart. Firstly, we focus on solving big problems for large scale industries that matter. That touch millions of people. We’re not interested in serving up pizza ads on your phone or beating the Jeopardy champion. Maybe it’s our origins in the space program, but we tend to specialize in solving complex problems in high risk environments where the mission can’t fail.
Secondly, unlike the conventional machine learning, neural networks, and deep learning techniques that are gaining traction today, we take a different approach. We use all those techniques, but we add cognitive reasoning. Much of the cognitive intelligence capability and technologies created at Beyond Limits has deep roots in what we call bio-inspired algorithms. They are designed to imitate the functions of a human brain. It allows to do things like deductive, inductive and abductive reasoning. Really the ability to do human like reasoning.
Thirdly, our systems understand situational awareness, or context. You can show a machine learning system a thousand pictures of a chair, and it can be trained to be pretty good at identifying a chair. But it will never know what a chair is for, or why a human wants to sit down, or what is a good chair compared to a bad chair. Our systems utilize human knowledge to learn context and meaning, so they can make good recommendations to people for faster, better, decision-making, and reduce risk.
One more thing. Conventional AI systems are what we refer to as black box implementations, where systems are trained based on data, but cannot explain how they got the answer. That bothers me. At Beyond Limits, we’re taking a different approach where we can explain how we got the answer. Our systems provide an audit trail that explains the rationale and evidence for the answer. I think establishing trust is important. In industries where the stakes are high, like medicine, energy, finance, people need accurate information. We need to have the doctors, engineers, and other professionals understand how they got the answer and provide them with that audit trail.
Abdallat has his eyes on growth and plans to take Beyond Limits to other areas around the world. Innovation is the company’s calling card and it has put it on the forefront of AI technology. Abdallat sees new offices and an expansion of business on the horizon along with pushing the boundaries of technology.
AJ, what’s next for Beyond Limits?
Well, there are two parts to that. One is growth. We’re going to expand on our global footprint. We’re going to open more offices in North America, Europe, Asia, and the Gulf. We’re establishing collaborative and research collaborations with many research universities around the world, and we have many talented scientists from NASA JPL. So, expanding the global footprint is going to be a key for us.
The other one is innovation. That’s really our DNA and the trend you’re going to see in 2018 and beyond is pushing intelligence to the edge. You’re going to see Beyond Limits introducing the first cognitive/symbolic AI chip. We’re excited about that. This creates tremendous opportunities for delivering cognitive solutions to locations where previously it was impossible. We’re going to complement the existing machine learning and deep learning capabilities that some companies are introducing in 2018 with our cognitive AI chip. We’re looking forward to that.
Instinct versus expertise: Which is more important?
I would say it depends on what phase you’re in. Expertise is always important, but there’s something to be said for instinct. I pay attention to what I describe as a gut feeling. If I’m about 80% it’s good. When it goes down to 50%, I’m going to re-evaluate my strategy. It doesn’t mean that I’m so good at predicting things, but I think after 20 years you start developing a great BS detector. I think for an entrepreneur this is important.
There was one time where I trusted my instincts over experience and it worked out well. We had started discussions with a major energy company, and the price of oil at the time was at $28. I remember talking to a VC about the company, and he didn’t understand why we would go after oil and gas business when the price of oil was so low. But I had a good feeling about it and believed in it. I felt if we can turn the situation around, great things can happen. I’m not going to tell you I predicted they would also invest in the company, because I really had no clue. But I trusted my feeling that they would be a good customer. Indeed, this customer became an outstanding strategic partner for us.
What is the most challenging part of your work?

From a human capital point view — attracting talent, retaining talent and building an effective team that can row in the same direction.
From a business point of view — navigating the business landscape very carefully by going after the right opportunity at the right time and taking calculated risks.
What was your most rewarding professional experience?
Taking our unique approach to AI by unifying both the Numeric — machine learning & deep learning — with the Symbolic side to create cognitive reasoning capability. Our systems are both educated and trained which allows us to handle complex industrial problems where human-like intuition for actionable intelligence is required. I am proud to say with this approach, we were able to demonstrate cognitive intelligence and win many opportunities including a global top 10 company.
While many people fear AI and other forms of technology, Abdallat stresses people shouldn’t be frightened. AI will create new jobs and opportunities for people around the world. As technology evolves, it gives people the chance to evolve with it.
Globally, how do you see the future of AI developing?
One of the misconceptions about AI, really, the clichés about conventional AI, is that some people feel machines are going to be taking our jobs. In reality, we are automating what I consider to be repetitive or dangerous tasks. Gartner did a report in 2017 where they forecasted that by 2020 artificial intelligence will automate about 1.8 million jobs. But it also forecasted that by 2020 AI is going to create 2.3 million jobs. So, the net gain is going to be about 500,000 jobs.
We at Beyond Limit believe that we are going to create a healthy collaboration between man and machine with trust, to help magnify human talent and amplify human talent. Which is going to end up creating more opportunities and more job for humans.
What’s a day in your life like?
It’s pretty simple. I get up, take a shower, feed the cats, have a cup of tea, drive to work, and think. Then I work on progress at Beyond Limits improving today from the day before. If we can continuously advance the cause and improve progress on a daily basis, we will succeed. We are at an exciting stage at Beyond Limits right now. We’re scaling up, growing, getting into production. We’re raising our C-round financing, and it’s all-consuming.
What makes you smile?
My kids. I am very proud of them.
What scares you?
Well, there are things that you can control and there are some things beyond your control. I truly believe right now there is an opportunity for us at Beyond Limits to take a leadership role in terms of next generation AI. What really scares me is what every CEO worries about at a certain phase: execution. We have a window of opportunity and need to execute to hit the mark. If you really think about it, there’s the example of Yahoo and Google. Google executed, Yahoo didn’t. I mean Yahoo had the technology, they actually had the market and brand name, but they didn’t execute.
What has been your greatest achievement?
I know I am being redundant here. Again, my kids, I’m proud of my kids. I am also, proud of Beyond Limits and my Beyond Limits family. At Beyond Limits we are carrying the flag for a new and different approach to AI.
What is your secret talent?
To build a great company, you need an outstanding team around you. Getting people to believe in your vision, values and goals is very essential. Maintaining that for the long haul is a proven recipe for success!
Which historical figure do you most admire?
I am a big fan of technology pioneers and inventors. I like many, but two I feel deserve more credit. Nikola Tesla and Edwin Armstrong. Thomas Edison gets credit for popularizing electricity, but it was Tesla’s invention that made it possible for everyone to access electricity. Second is Edwin Armstrong. He was the guy who invented FM radio. Armstrong was a brilliant engineer. Very analytical and intuitive. He didn’t shy away from complex and impossible problems. His ideas made significant technology advancements for radio and television. He also didn’t compromise.
Like many serial entrepreneurs, Abdallat focuses much of his attention on building and maintaining thriving businesses. Abdallat does have a great interest in history and loves to read about the American Civil War and technology when he has free time.
Do you have any hobbies?
No hobbies other than creating and building companies. But I am a big history buff. I would say history is of great interest to me. I am not biased to any specific period or region. By the way, I love the Civil War, I’m into that specific period. But I love history in general. I am very interested in the history of technology and AI. I find it fascinating that machine learning was invented in 1959. So, it’s been around for a while, but now it’s relevant to business because of the development of huge data sets and inexpensive computing power. Looking back and studying why and what happened is fascinating to me.
What are you never without?
Tea. I am more of a tea drinker than coffee. I like black tea. I like tea with mint. Also, with lemon and honey.
What advice would you give to anyone starting a new business?
I like this question. I would say it goes back to do what you believe in. Don’t let others influence you. Go with your gut feeling and instinct. It’s ok to go out of your comfort zone, but stay true to your vision and your values. I’m very passionate about technology in general. I’m very passionate about AI. I think AI is the most constructive force in computer science. I love being an entrepreneur. I feel I’m blessed to work in an environment where we can have significant and major impact on humanity.
Beyond Limits is a unique AI company, with proud Caltech/JPL heritage in our leadership team, and advanced technology developed for the NASA space program. Founded in 2014, Beyond Limits is transforming proven technologies from Caltech and NASA’s Jet Propulsion Lab into advanced AI solutions, hardened to industrial strength, and put to work for forward-looking companies on earth. Beyond Limits leverages this unparalleled innovation portfolio, along with the company’s breakthrough cognitive technology, to go beyond conventional AI, blending deep learning and machine learning tools together with symbolic AIs that emulate human reasoning. For more information, please visit
By Drew Farmer

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By Eve Gumpel, California Business Journal
AJ Abdallat has been living on the edge for years – the edge of scientific discovery, that is.
He describes himself as a serial entrepreneur, co-founder and CEO of several high-tech startup companies that have worked exclusively with the California Institute of Technology (Cal Tech) and the Jet Propulsion Laboratory (JPL) on technology designed to, for example, detect water vapor on Mars and translate thermal heat into images for use on fighter jets.
Now, as CEO of Beyond Limits, he’s embarked on what he believes is the “most disruptive technology since electricity and the internet”: Artificial Intelligence.
As Abdallat explains it, artificial intelligence (AI) involves a machine performing functions a casual observer would view as intelligent. The term dates back several decades – it was coined by John McCarthy of Massachusetts Institute of Technology in 1956.

However, there are two sides to AI. The numeric side and the symbolic side. The numeric side is mainly machine learning – training the machine based on data or experience. It can provide an answer, but it cannot explain how it arrived at that answer. The machine can classify something as a cat or a dog. “But it doesn’t understand what a cat is or what a dog is,” Abdallat explained.
By contrast, the symbolic side of AI relies on educating the system. Says Abdallat, “A symbolic system will tell you, ‘This is a cat because it has a tail. It has whiskers. It has a color. It has four legs, and it’s this high.’ It can give you more information based on the training and the knowledge it was provided. Think of it as sending a kid to college, and they’re reading textbooks and learning about a new field.”
According to Abdallat, “NASA will not implement any system where you cannot explain how you got the answer and provide the audit trail. We’ve built our capabilities and technologies from the ground up to have the explainability angle and provide the audit trail. That has proved very effective in the sectors we operate in.”
Abdallat describes what “explainability” could mean to the drug development industry, which spends billions on drug discovery. “Out of 10 trials, one is successful,” he says. “And if you have the wrong assumption or the wrong data set, you have to start over from scratch.
“With AI, we have these intermediate points. We can understand what went right and what went wrong, and adjust accordingly. And we don’t have to go and redo it from point A.” As he says, “This can be a game changer in the drug discovery market.”
Beyond Limits is working with strategic partners to bring that potentiality to life. “Definitely, we can cut the time [to get a drug to market] and reduce the cost of drugs,” he says.
Unlike most AI companies today, which identify with the numeric side, Beyond Limits uses both symbolic and numeric processes to solve problems. “So we’re closer to human-like reasoning than conventional AI. And that way, we’re also able to do autonomous, actual intelligence.”
Symbolic intelligence is critical if you’re operating in space, says Abdallat, and  translates well to earth industries working in rarified environments. For Beyond Limits, that means the energy industry in particular. Companies extracting oil and gas in the Arctic Circle, the Gulf of Mexico, the North Sea or the Sub-Sahara are facing the same kind of conditions faced in space, he says. “We’re leveraging our experience and expertise for the last two decades handling these dynamic and harsh environments where a human being cannot be present, and applying those to solve complex industrial problems in the energy sector.” And, says Abdallat, “We’re getting some good results.”
The company also serves the financial services industry, health-care and transportation. But unlike companies working on autonomous vehicles in the transportation field, Beyond Limits is focused on what Abdallat calls “situational awareness” or assisted driving. “We have a project in Japan that is focusing on the driving elderly population and how they can build that safety net trying to identify a distracted driver or someone who has a health issue, and fighting that before it happens.”
Says Abdallat, “AI is your friend; it’s not your enemy. It’s going to amplify and magnify human talent. It’s going to allow you to focus on higher-level objectives and leave repetitive tasks for machines to accomplish.  It’s going to allow us to tackle more complex projects that we didn’t think were imaginable before. It’s going to make life a lot easier.
AI is already ubiquitous, Abdallat says. “It’s in your toaster, your microwave oven; it’s in your car. Soon enough it’s going to be in our bodies, in our pets.”
In fact, he says, “AI now is used to write and compose scripts and compose music, to do art. I can’t think of an area you can’t apply AI to.”
Abdallat acknowledges that AI will automate some jobs that involve repetitive tasks. “But we don’t think the question is going to be man vs. machine. We think there will be a new era of man collaborating with machine.”
He envisions a creative and collaborative environment that will allow mankind to handle a second set of problems we were not able to handle before. “Right now we’re getting into a new area where you need adaptive processes – customized processes – where factory systems and processes need to be flexible, customized. Corporations need to adapt to not just produce large, standardized products but customized products for consumers.
“For that,” says Abdallat, “we need man plus machine.” He points to BMW factories in Germany that employ very sophisticated robots. “The robots are aware of every movement that their human colleague is doing. They’re working in harmony, like a ballet.
“This is the future factory,” he says. “We need that to end up creating more opportunities and more jobs for mankind.
“Think of it this way,” he says. “Imagine if I want to go to an operating room. I want that surgeon to have access to 20 AI agents.” AI can advise the surgeon, providing information about your medical history and your reaction to drugs, for example. “If you give me the option – you want one surgeon, or one plus 20 intelligent agents? I would take the one plus 20.”
What’s next for Beyond Limits? “We are launching embedded intelligence in hardware,” or “AI on a chip,” says Abdallat. “We’ll be the first company to do symbolic AI on a chip. We’re making significant progress in that area, currently working with two strategic partners on embedded intelligence.”
Abdallat is also focused on global expansion. With customers in Europe and Asia, as well as North America, Beyond Limits is beginning to tackle problems overseas. “Expanding our footprint is a priority for us,” Abdallat says.
On the horizon in the next five years is having AI systems talk to each other. “We think that connected intelligence – having cognitive agents talk to each other – is going to be the wave of the future. Abdallat calls it the “holy grail in AI,” the point where machine intelligence will be equal or superior to human intelligence. “We see that on the horizon in the next five years, and we believe that Beyond Limits is going to be one of the pioneers, leading in that arena.”

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Beyond Limits, a leading developer of advanced artificial intelligence (AI) solutions, today announced its participation at the Wonder Women Tech National Conference 2018. The event will take place from October 5–6 at the Long Beach Convention Center. The conference will be hosted by Wonder Women Tech (WWT) to promote underrepresented entrepreneurs and innovators in science, technology, engineering, arts, and math (STEAM). The two-day conference will feature keynote speeches and discussion panels on some of today’s hottest tech topics and address the unique challenges that minorities of all ages, ethnicities, and genders face in the tech industry.
Beyond Limits’ outreach goals are closely aligned with those of WWT, a nonprofit organization that supports, promotes, and encourages underrepresented people in technology industries via national and international conferences and programming. As a company that recruits and benefits from talented people in the STEAM community, Beyond Limits is proud to collaborate with WWT to raise awareness for workplace diversity and inclusion. The company’s commitment to these issues is based on the collective welfare of its employees and awareness that diversity and inclusion are competitive advantages.
According to the World Economic Forum, 60 percent of college graduates are women, but account for only 35 percent of the total number of undergraduate degrees in science, technology, engineering, and math (STEM). 40 percent earn degrees in mathematics, while a mere 18 percent earn degrees in engineering or computer science. A 2010 research report conducted by the American Association of University Women (AAUW) concludes that this disparity is the result of young women’s social environment, specifically at home and at college, and well-documented issues with gender bias and sexual harassment.
“Diversity impacts everyone,” said Mario Portugal, Beyond Limits head of recruiting. “We welcome the opportunity to support talented people of all genders, cultures and backgrounds with STEAM skills, and we’ll continue to do so in the future.”
In addition to Wonder Women in Tech, Beyond Limits’ commitment to diversity has included recruiting and networking events hosted by WomenHack LA and Reed Smith’s Diversity Summit. These events are designed to create awareness about the importance of diversity and connect top talent in product management, software development, and UI/UX design to tech companies that value inclusion and diversity.
Launched in 2014, Beyond Limits produces cognitive AI systems with human-like reasoning available to transform the performance of industrial and enterprise operations and systems. The company leverages advanced technologies developed at Caltech’s famed Jet Propulsion Laboratory (JPL) for the NASA space program, as well as breakthrough technology innovations originated by Beyond Limits scientists and engineers.
Beyond Limits is the only AI company in the world with advanced technology proven in extreme environments from space missions to subsurface oil and gas exploration. Today, Beyond Limits goes beyond conventional AI, applying cognitive AI software inventions to solve complex industrial and enterprise problems, bringing cognitive advantages to energy, fintech, healthcare, and logistics.
Beyond Limits was selected as a Silver Stevie® Award Winner in the 2018 American Business Awards®, a winner for the Tech Trailblazers Award in the category of artificial intelligence, and has been covered in the Wall Street Journal, Financial Times, Forbes, Fortune, Entrepreneur, ZDNet and the International Business Times.

01 October 2018
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Trent Jacobs, JPT Digital Editor: BP and Startup Beyond Limits Try To Prove That Cognitive AI Is Ready for Oil and Gas

BP has invested more than $100 million into nine different startup companies in the past 2 years—but only one of them wants to turn your brain into a piece of its software.
The international major is working with the ambitiously named firm Beyond Limits on a set of artificial intelligence (AI) programs that will absorb the learnings of geologists and petroleum engineers, and then imitate their decision-making processes as they work on subsurface challenges together.
Before this partnership, Beyond Limits had never been involved in solving the complexities of oil and gas, which is something that might be counted against it by venture capital or prospective upstream clients. But after BP saw what the young company was working with in its office about 10 miles north of downtown Los Angeles, it decided to become both its largest client and its largest investor by injecting it with $20 million a year ago.
The attraction for BP came down to getting its hands on a strain of AI known as cognitive computing, or what Paul Stone refers to as “the pinnacle of the artificial intelligence pyramid.”
“We haven’t seen that elsewhere, so we wanted to be the first to engage and see where it might go in the oil and gas industry,” said Stone, who serves as a technology director in BP’s digital innovation group.
And where it lacks a prior track record in oil and gas, Beyond Limits compensates with a technology team that helped design most of the same intelligent software it is licensing from the Jet Propulsion Laboratory (JPL), an institution run by the influential California Institute of Technology in cooperation with NASA. Similar to oil and gas explorers, at JPL immense uncertainty is the perpetual driver for emerging technologies.
AJ Abdallat has served as chief executive officer of Beyond Limits since its founding in 2014 and previously spearheaded several other startups that have been spun out of work first done at JPL. He believes that this firm’s pedigree gives it a big head start in pushing the envelope on cognitive computing because “the issues and challenges we are tackling today, JPL has been tackling for decades.”
And though there remains a lively debate about where cognitive computing really stands today in terms of its human-like reasoning capabilities, Abdallat said, “People tend to judge AI based on the last 2 or 3 decades—you really need to look at what has happened in the past 5 to 6 years.”
Some of the most notable recent advancements include the AI sector proving that intelligent programs can best human reasoning when it comes to complex games such as Go and Texas Hold’em Poker. Cognitive programs also demonstrate growing promise as a diagnostic tool in the healthcare industry—a vertical that Beyond Limits is positioning itself to break into along with oil and gas.
For Stone, who was instrumental in bringing the Glendale, California-based Beyond Limits into BP’s orbit, integrating petrotechnical experts with cognitive computing products can be summed up as an effort to leverage the oil company’s brain trust at scales never possible before.
“Ideally, we’d like the human to be able to work at the speed of a computer with the data, but they can’t do that,” he said. “So the next best thing is to get the computer to work with the knowledge that the human has.”
The future Abdallat hopes for is one where Beyond Limits’ cognitive computing programs become trusted enough to run through every digital vein of an oil company, canvassing reams of information to run all kinds of optimization simulations.
The experiment that will test this vision is well under way.

The AI Reservoir and Production Assistant

A cognitive system can be loosely described as the combination of multiple advanced computing methods that include basic analytics and deep learning tools, with a few others sitting at various points between each end of the spectrum. Practitioners argue that the sum of these parts is a sort of AI cocktail that can reason through problems much the way a human would.
“It has that knowledge layer for how to grapple with all the different inputs that can come in, and how to anticipate how they might evolve, and what to do in light of that,” explained Zack Nolan, the senior vice president of technology programs for Beyond Limits.
Since July, the first of these ensemble programs developed by Beyond Limits are up and running within a select group of BP’s upstream engineering teams in Houston. Their collective mandate is to raise the ceiling on what the oil industry can get out of AI, which has so far been most prominently centered around predicting equipment failures and automating artificial lift units.
In one of a handful of early-stage projects, BP says it is looking at how it can use these AI systems to mitigate the impact of sand production, among the last things an operator ever wants coming out of its wells. One of the biggest benefits to BP is that as its most tenured remediation experts train this system, their expertise will live on digitally within the oil company long after they retire.
Outside of its work with BP, Beyond Limits is developing another system that will learn from geologists and reservoir engineers as they look for signs of the prize in offshore seismic data. Such technology could be used by oil companies to propose plausible well locations, or the well designs that it thinks will recover the most product.

An AI-based reservoir management advisor will take on the tasks of modeling risk and reward for multimillion-dollar well locations. Source: Beyond Limits.

The expectation is for this reservoir management advisor to be as reliable as a top expert, only be much faster and able to review much more data. However, this system and the others Beyond Limits is building are meant to complement the experts, not replace them. Nolan said one example of how this might work is using AI to catalogue the ideas of an exploration team, especially those that were never acted on.
Before a new offshore well is drilled, more than a dozen professionals may see the same set of seismic data a little differently. Ultimately, they will hone in on a single view and start making hole. But if all the discounted angles are kept for retrospective study, they may prove valuable in helping reduce reservoir uncertainty for the next wells.
“That’s the kind of thing you can afford in an AI system,” Nolan said. “Because it can at least store these things indefinitely, with pretty much perfect recall—something that humans are not great at.”
Another common thread with all of these applications is the concept of rapid scenario generation. To solve problems quickly, the idea goes that the AI will toss up qualified ideas to the engineers who will call the shots from there, putting them “in an underwriting position” as compared with “an initial creation position,” according to Nolan.

Haven’t We Seen This Before?

Because cognitive computing is not monolithic, some aspects of it are further ahead than others. Such is the case for computer vision.
“You will be able to infer the faults, and you will be able to infer horizons in the reservoir using this technology,” noted Chirag Rathi, the director of consulting at Frost & Sullivan, which has researched Beyond Limits along with similar AI vendors for the oil and gas industry.
But when it comes to matching human skill in other areas, he said new cognitive products must overcome an “underwhelming” history of delivering “rudimentary answers to really complex situations.”
This may indicate that there is still a lengthy road to maturity for cognitive systems. “It’s not a comment on the lack of hardware, but more on the software and all the aspects of decision making that need to be programmed,” Rathi said, adding that machine-based extrapolation will also need a higher order of data quality and availability than what the industry has traditionally demonstrated.
The concept of capturing the information a company acquires over the years and then activating it with a recommendation engine is not a new one. What Beyond Limits is working on can be traced back to the computers introduced in the late 1970s as expert systems. They were designed to retain an organization’s knowledge and follow a rules-based approach to generate answers.
Though advanced for its time, expert systems would lose much of their luster during the “AI winter” of the 1980s as they proved to be poor extrapolators—they knew only what they were trained to know. But the technology never really went away. People continued working to improve the logic behind it, and Moore’s Law kept enabling computing advancements at increasingly lower costs.
Among the places that expert systems lived on, and became templates for today’s generation of more capable software, was JPL.

It Came From Outer Space

From his desk, Abdallat can see out his window to the pair of hills that JPL sits behind—his deep ties mean he knows exactly where to point.
Down the hallway, walls are adorned with travel posters of recently discovered planets that lie far beyond our solar system. And in its lobby area, Beyond Limits boasts a 4-foot-tall scale model of the Mars rover Curiosity that stands on a pile of faux rocks with a panorama of the vast and inspiring Mars-scape behind it.
If not for the work that some of its team did on the real Mars rovers, this small company of about 80 employees and interns might never have caught the eye of BP in the first place.
Stone recalled how one of the principals at Beyond Limits authored a unique AI program responsible for the ­mission-critical task of managing one of the rover’s battery. When that program detected that the solar panels were suffering from dust storms, it did something it was never designed to do: access data from pressure and temperature sensors to build the Red Planet’s first weather model.
“That really impressed us,” Stone said, explaining this meant the rover could prep for dust storms by simply knowing which way to turn its solar panels. “I think it is a big step forward—no data scientists created that model.”
This uniquely adaptable software is just one of dozens from JPL that now form the backbone of Beyond Limits’ technology stack. Several bear the fingerprints of its chief technology officer Mark James, who was previously an advanced software scientist at JPL where he spent 25 years.
Among the programs he authored and have since followed him to the startup is a natural-language processing system called Hunter. Company documents say it is capable of “autonomous summarization” and “translating narrative descriptions of algorithms and processes.”
Originally developed for military purposes, this software is now central to Beyond Limits’ ability to spell out to end users the origins of answers in what it calls an audit trail. Through a machine-translation process, this program also allows those users to interrogate the reasoning behind each conclusion.
Another software named Sherlock IQ arose directly from work on the rover program and uses machine cognition to “autonomously shift through corridors of data to discover plausible facts and scenarios.” Tools like this are how a program designed for watching a rover’s battery can autonomously access sensor data and become a digital meteorologist. Similar systems are being adapted to form Beyond Limit’s AI reservoir management advisor, which aims to take risk analysis processes that usually require months down to just a few hours.

An AI-based reservoir management advisor will take on the tasks of modeling risk and reward for multimillion-dollar well locations.

Engineering Trust

Whether you can successfully convert software built for billion-dollar deep space projects into software for billion-dollar deepwater projects may come down to the simple concept of trust.
“If I can explain to you how I got the answer, if I can provide you with an audit trail, you’re going to be willing to test it and try things,” Abdallat said.
As central as the audit trail is to fostering human confidence, it is underpinned by two other major components: knowledge bases and inference engines known in the AI-world as intelligent agents.
In the case of the knowledge base, one of the biggest questions is whose internal thought processes and actions are to be encoded into a machine-digestible form.
“We are replicating their best,” said Shahram Farhadi, a data scientist and the head of oil and gas technologies at Beyond Limits. Prior to joining the firm last October, Farhadi had been a petrophysicist and reservoir engineer with Occidental Petroleum. He pointed out that the ideal candidates initially include those who draft company procedures and best practices, and, in many cases, are also the same people who are on call to troubleshoot an operator’s biggest problems.
Those procedural documents go into these data bases, along with the same industry heuristics and first principle physics found inside textbooks and technical reports. Stone from BP equated the initial result to “a person who just started university.”
By the time a knowledge base model is actually put into the hands of engineers, it will have metaphorically graduated and should resemble what a green professional would be expected to know in their first year of work. At that point, “It will learn through being used on the job, interacting with different professionals, and it will start to build experience and store knowledge further,” Stone explained.
Some users will train the system simply by asking it questions. Others, typically senior engineers, will be the most impactful teachers and “can add new rules, variables, and new concepts,” Farhadi said.
These expert users are also the ones who will be popping the hood most often to see the program’s decision tree and read over the audit trail to understand the route taken to an answer.
How long it might take to build a knowledge base and other supporting systems to drive oilfield decisions depends on a number of factors, including the digital readiness of the operator and the scope of the problem being tackled. But in general, fleshing out an AI technology that can reliably select offshore well locations should be expected to take more time to deploy compared with one that will predict and advise an operator on asphaltene buildups in a well.
Meanwhile, a number of AI-agents will scour these knowledge bases, talk to other ones, and interact with all the same professionals so they can learn the art of problem solving in the oil and gas business.
And where more brittle AI systems might fall down in the face of missing data, these agents will reason past those gaps, an ability that takes time and training to sharpen.
“If you build an agent today with data and knowledge, you’re not going to put it in charge of making decisions tomorrow,” noted Farhadi. “Initially, it will be used as a design tool, then it will become a recommendation tool, and then once you build trust, it will be used as a control system.”

An example of how an audit trail might appear to end users. Source: Beyond Limits


Keeping Humans in the Equation

Whenever AI is discussed in the context of high-level tasks such as reservoir management, the conversation inevitability turns to the future prospects of the human workforce. While a worthy talking point, many analysts do not see the mass replacement of human engineering talent on the immediate horizon.
In a report published last year, international consultancy Accenture predicted that cognitive computing “will have a profound impact in oil and gas” but said the change for professionals is likely to come in the form of “super-charged teamwork.”
That point of view is largely shared by BP and Beyond Limits, which translate the emerging shift toward these powerful computing tools as one that will “augment” engineering groups.
“This is not about automating jobs and getting the human out of the equation,” Abdallat said. The true aim of AI is to “amplify and magnify the human talent.”

Read full article here:
Trent Jacobs, JPT Digital Editor: BP and Startup Beyond Limits Try To Prove That Cognitive AI Is Ready for Oil and Gas
01 October 2018
Read the full Forbes article here.


For scale, consider the Statue of Liberty, standing 305 feet tall. At 466 feet, the average wind turbine in the U.S. dwarfs Lady Liberty by more than half. And when GE’s next-generation monster wind turbine, the Haliade-X, hits the market in 2021, it will nearly double that size to 877 feet, just shy of the Eiffel Tower. A single Haliade-X rotor blade will stretch 315 feet, longer than a football field.

As a general rule of thumb, when it comes to energy and energy exploration, bigger is better: the larger the machinery, the deeper the dig, the greater the production yield. But this massive scale can pose major challenges. By necessity, assets like oil rigs, wind farms and mines are often located in remote and harsh environments, posing safety risks to human workers during construction, inspections and repairs. Equipment laden with sensors can collect petabytes of data, but without reliable high-speed wireless infrastructure, transmitting it can be slow and unwieldy, straining the system’s bandwidth.
That’s all changing—and fast, thanks to rapidly evolving and emerging 5G, AI and IoT technology. Collectively, they’re transforming the energy sector in fundamental ways, by enabling energy and mineral harvesting optimization, predictive and automated maintenance, high-volume and low-latency data delivery, and smarter power grid management for better allocation of energy resources to countries, cities, manufacturers and consumers.
“Together these technologies are improving efficiency, driving down costs and allowing companies in those spaces to make better use of the available assets,” says Paul Miller, senior analyst at Forrester Research. “The biggest impact is around asset visibility and asset management. What is my equipment doing? Is that wind turbine turning and how much is it producing?”
But the benefits of today’s tech advances go beyond ensuring that machinery will perform at peak capacity. Miller sees IoT and AI’s impact transforming the very concept and function of a city’s power grid, which has remained structurally unchanged since the 1930s.
“The energy grid for a city is a mix of different sources: nuclear, gas, wind and solar,” he says. “You’re going to want to use renewable energy as much as possible, but you have to make a guess for how much energy is going to be needed by the city. Essentially you want as much data as possible to make those guesses as data-driven as possible.”
“The ultimate impact will be for every city and every country optimizing their use of power so that you need to produce less,” Miller says.
Viewed up close, current tech advances in the energy and mining sectors look like a patchwork quilt of isolated improvements, but the big picture shows something more sweeping. Here are four key arenas where AI and IoT are changing the game in the energy and energy exploration sectors.

Yield Optimization

By 2020, the industrial IoT is expected to comprise more than a trillion sensors, each collecting and sharing data in real time. This mountain of data, when processed and analyzed by advanced machine learning software, will let energy companies monitor and regulate production to cut costs and maximize output—down to the minute.
A McKinsey study projects that AI innovations could save oil and gas companies as much as $50 billion in production costs annually. Among the companies innovating in the synchronization of AI, IoT and oil and gas hardware is Calgary-based Ambyint, whose intelligent “adaptive controller” platform samples data from thousands of vertical and horizontal oils wells every five milliseconds to recommend optimization strategies. San Francisco–based Tachyus also integrates real-time equipment data with seismic activityto regulate maximum oil flow through pipelines.
Predictive AI is even helping improve how oil and gas companies locate the most resource-rich drilling grounds. Chevron is using AI to identify new well locations in California; by drilling in better locations, production has risen by 30%, the oil giant claims. Recently, BP invested $20 million in Beyond Limits, an AI startup commercializing cutting-edge tools from NASA to adapt deep-space exploration technology to deep-sea oil and gas exploration in the search for promising drilling grounds.
For the wind and solar power industries, AI is enabling greater energy yield through advanced weather forecasting and analysis. How do you maximize wind power when the wind dies down, or solar power during overcast days? By incorporating intelligent “tuning” mechanisms into the hardware that automatically adjust control settings for varied weather conditions.
GE Renewable Energy is taking a different tack to optimize wind power by creating digital wind farms. These “digital twins” are virtual models of actual wind farms that gather data from the physical turbines during operations and analyze potential settings to determine optimal efficiency. GE reports that its digital-twin technology will boost energy production by 20% annually, generating $100 million more profit over the lifespan of a typical 100-megawatt wind farm.

Predictive Maintenance And Cognitive Vision

In northeast Iowa, on a blustery day in March recently, a wind turbine’s blades churned steadily. But 400 miles away, data analytics software detected an anomaly: Unexpectedly, the turbine’s gearbox was on the verge of failure. The wind farm’s operators quickly dispatched a crew for a $5,000 repair job, averting a catastrophic breakdown costing several days of downtime and $250,000 in lost revenue.
But predictive maintenance, enabled by AI and IoT, isn’t just about preventing unforeseen equipment failure. By predicting wear and tear, it allows timely maintenance that can extend the life cycle of complex and costly machinery. More importantly, it can ensure the safety of human crews who scale massive equipment while exposed to the elements or who must attempt a dangerous rescue mission after a mine collapse.
Much of predictive maintenance technology today is enabled by sophisticated IoT sensors inside machinery to monitor temperature, moisture, output flow and seismic vibrations. Externally, AI-enhanced drones and robots are proving equally valuable in revolutionizing inspections and repairs.
Among the powerful new tools for monitoring outdoor machinery such as oil rigging and wind turbines is Aerialtronic’s “digital vision” platform, a camera-computer hybrid that can be mounted to drones or mobile robots. Its optical and thermal cameras, along with an onboard 1.5-teraflop GPU, let it detect even the tiniest of fissures that could lead to equipment failure. Another digital vision system from SkySpec lets an autonomous flying drone inspect an offshore wind turbine in less than 15 minutes. If it finds damage, its analytics can project repair costs and calculate whether they’re worthwhile, or if it’s more cost-effective to replace the equipment.

Environmental And Safety Upgrades

A recent survey asked executives from 100 of the largest mineral extraction companies in the world to name their top priority for deploying IoT in mining operations and 47% of them gave the same answer: monitoring their mines’ environmental impact. The reason? Meeting strict government regulations on environmental impact is costly, but an even greater responsibility is ensuring the health and safety of miners.
Companies like Inmarsat are working with mining companies to leverage IoT and machine learning to bolster worker safety and environmental compliance using smart sensors. Wireless sensor networks provide early detection of excessive vibrations that could lead to structural collapses, as well as the presence of dangerous flammable and combustible gases such as methane and carbon dioxide. Data collected by these sensors, as well as workers’ wearable sensors and sensor-laden flying drones used to conduct site surveillance, helps mining firms generate predictive models to minimize future dangers. All told, experts predict that smart sensors could save the mining industry $34 billion in costs by reducing health and safety incidents.
The nuclear power industry is also tapping machine learning to improve reactor safety, which could strengthen it as an alternative power source in the U.S.; currently, nuclear power plants provide 20% of all electricity generated. (Nuclear power safety is no small concern. Since the 1986 Chernobyl disaster, 56 of 99 major nuclear power accidents have occurred in the U.S.)
Engineers at Purdue University have created a deep learning neural network that can detect minute cracks within nuclear reactors by rapidly analyzing video images, which until now has been a lengthy, tedious and imprecise job for human inspectors. Rendering the inspection task even more difficult? Large sections of nuclear reactors are underwater and difficult to monitor.
Trained on a dataset of 300,000 images of crack and non-crack examples, Purdue’s AI has scored a 98.3% success rate in identifying tiny fissures in reactor walls—a significantly higher rate than that of human inspectors.

Autonomous Energy Production

In a report on the future of AI for the renewable energy industry, global risk management consultants DNV GL envision a day when wind and solar farms could spring up without any human involvement. Self-driving trucks could transport wind turbine and solar array components from the factory to the site. Another set of robots would unload and assemble them on a foundation dug in earth and filled with concrete by more robots. Finally, drones and robots would assemble the working facility.
Far-fetched? Not entirely. Autonomous mining is already underway in Boliden, Sweden’s Kankberg gold mine, and plans include its eventual operation without any human workers. In conjunction with the Swedish government, the mine’s operator has teamed up with telecom giant Ericsson, Volvo and Abb on the innovative project.
Self-driving excavators and haulers remove minerals from the 500-meter deep site. A 5G wireless network connects all machinery and sensors to ensure seamless production, transmitting data at 100 gigabits per second, nearly 100 times faster than current Wi-Fi technology.
No human workers mean no humans are at risk from mining accidents or disaster. A 24/7 production cycle optimizes value for mining companies. All told, the benefits of autonomous energy production are clear. Even if its arrival as a reality is far off, one by one the pieces are falling into place thanks to the convergence of 5G, AI and IoT. Together these advances are disrupting the energy sector at every stage, from production to refinement and consumption. In the coming decade, you can expect to see this sweeping digital transformation pay off in lower-cost, lower-risk and higher-yield businesses.

Read the full Forbes article here.

According to the US Department of Energy, pumping systems account for almost 20 percent of the world’s energy consumed by electric motors and approximately 25 to 50 percent of the total electric energy usage in certain industrial facilities.  Among other applications, pumps move crude oil through vast pipeline networks, which in turn play an indispensable role in transporting hydrocarbons to key markets. Suppliers are already developing solutions such as digital twins that can leverage data analytics to optimize the performance of pump stations for crude oil and other pipelines.

Digital Twins and Oil & Gas 4.0 – Key Benefits

As digital replica modeling tools, digital twins support a digital culture of “fail fast, learn quick” by providing the perfect testing ground for innovative new ways of working.  Also, connectivity to the components and equipment provides real-time monitoring and adjustment capabilities.  The original concept came from the desire to take all information available on a piece of equipment or asset and then applying higher level analysis to that information.

“Digital Twin of a Petroleum Refinery (Source: GE Ventures)”

Digital twins can help oil & gas companies:

  • detect early signs of equipment failure or degradation to move from reacting and responding to a failure to being proactive; which enables owner-operators to plan and implement corrective maintenance actions before failure occurs and often at much lower cost
  • model drilling and extractions to determine whether virtual equipment designs are feasible
  • gather real-time data feeds from sensors in an operational asset to know the exact state and condition, no matter where it is located

The real advantage of the digital twin concept, however, materializes when all aspects of the asset (from design to real-time operating and status data) are brought together to optimize the asset over its lifetime.  Companies can test pricing levels, logistics challenges, even potential safety hazards. A digital twin allows users to identify numerous plausible futures for an asset and consider their potential impact.

Best Practices of Oil & Gas 4.0

Recent research indicates that many of the oil and gas organizations implementing the Internet of Things (IoT) are already using or plan to use digital twins in 2018.  In addition, the number of participating organizations using digital twins will triple by 2022.  Several best practices in this area are emerging among the major engineering and oil & gas firms:

  • it’s best to involve the entire value chain
  • establish well-documented practices for constructing and modifying digital twins
  • include data from the multiple sources (as-builts, operational data, costs, maintenance program, engineering detail, physical constraints, behavioral patterns, operating parameters, customer demands, and weather patterns)
  • look beyond the normal software development cycles to consider asset lifecycle issues

Initiatives Already Underway

Due to its asset-intensive nature and reliance on large pieces of highly instrumented equipment, often operating in remote, unsafe, and uncompromising locations, the oil & gas industry has had digital twins on its agenda for several years.

Shell, together with Swiss engineering modeling and simulation technology company, Akselos and engineering research and development experts at LICengineering, a Danish consultancy firm specializing in the marine and offshore energy sectors, have recently signed up as participants in a two-year digital twin initiative.  The partnership focuses on advancing the structural integrity management of offshore assets by combining fully detailed cyber-twin simulation models.  Things are well under way with Shell North Sea assets, with the intention to improve management of their offshore assets, improve worker safety, and explore predictive maintenance.  There are two phases to this initial project:

  • First, to develop a condition-based model of its selected assets, enabling the company to analyze structural integrity with more accuracy and detail
  • Second, to combine this model with sensor data, to allow Shell to monitor the health of its asset in real time, which would enable the company’s operators to predict the future condition

The world’s first “digital rig” is targeted to achieve a 20 percent reduction in operational expenditures across the targeted equipment and improve drilling efficiency.  The solution connects to all targeted control systems, including the drilling control network, the power management system and the dynamic positioning system.  Data is collected through individual IoT sensors and control systems, modeled and then centralized on the vessel before transmitting in near real time to GE’s Industrial Performance & Reliability Center for predictive analytics.  The system has already started to capture multiple anomalies and produce alerts of potential failures up to two months before they would occur.  The data models come from a digital twin of various physical assets, along with advanced analytics to detect behavioral deviation. Thanks to vessel-wide intelligence, personnel both on the vessel or onshore can gain a holistic view of an entire vessel’s health state and the real-time performance of each piece of equipment onboard.

In 2017, BP invested in the Beyond Limits start up to build upon existing NASA- and DOD-based experience in robotics. The intent was to operationalize new insights from operations to help them locate and develop reservoirs, enhance production/refining of crude oil, and increase process automation and efficiencies.  Extensive infrastructure was established, including supercomputers, and 2,000 km of fiberoptic cable and large investments were made to increase in data storage to six petabytes.  As a result, IoT sensors are collecting data about temperature, chemicals, vibration and more from oil and gas wells, rigs, and facilities.

The Gazprom subsidiary, GAZPROMNEFT-KHANTOS, has established an Upstream Control Centre that’s pulled together already established solutions.  The objective is to improve upstream process efficiencies from a central operating center.  One of the most important components has been to establish the digital twin, developed for mechanical fluid-lifting built around hybrid models. This is further enhanced with machine learning tools and the ability to self-calibrate based on rapidly changing information, sourced from automated controls.  Information collated by the digital twin, new maintenance solutions, and other Gazpromneft-Khantos systems are accumulated at the Control Centre and can be displayed and visualized by multifunctional teams to take timely and well-informed decisions.  The functionality of the Gazpromneft-Khantos Upstream Control Centre will be significantly expanded in the future.  Currently, the company is completing testing of digital twins for formation pressure maintenance systems, energy supply systems, and treating and utilizing associated petroleum gas.

The Challenges Ahead

Development of the fourth Industrial Revolution defining technology is not for the casual “toe-dipper,” as the journey to true digitalization is challenging for any enterprise.  Each aggregated digital twin is unique, ultimately enabling powerful data analytics, new machine learning, and potentially valuable information across the OEM network.

Establishing digital twins requires a focused and cross-functional team that spans the organization, incorporating technical expertise across the infrastructure, the enterprise IT and OT applications from the OEM to the fully constructed and operational asset.

For further discussion or to provide feedback on this blogpost, please contact the author, Jyoti Prakash, at

Full article:

This month is a big month for Artificial Intelligence (AI). Facebook has already announced in the past week that it will dramatically increase its investment in AI research and development to ensure that it doesn’t fall behind as a technology innovator. Amazon for its part at its NYC Summit 2018 announced new capabilities for its artificial intelligence machine learning and compute services on the AWS cloud including a way to build new models while Google Cloud Next ’18 conference next week is expected to lift the lid on a number of new AI initiatives.

AI Should Understand AI

All three companies, along with Microsoft, are looking to gain dominance in a market that is still only starting to grow. To do that they need to offer enterprises a way of using and incorporating AI into their business DNA that will not disrupt business processes and business strategies. What do these companies need to offer?
Pascal Kaufmann is a neuroscientist and AI entrepreneur and founder of Switzerland-based Starmind a technology which applies neuroscientific principles to AI development. It identifies experts on any subject within an organization and connects them to those fellow members of the organization who need that expertise most. Kaufmann said that AI, when applied to businesses, has the most impact if the following three conditions are met:

The ROI [Return On Ivestment] of an AI technology can be quantified best when it is benchmarked with human workers doing the same job: For example, even if the machine was 10 times slower, being 20 times more cost effective would already result in a convincing business case.
Related Article: 6 Ways Artificial Intelligence Will Impact the Future Workplace

How AI Generates ROI

“’When you do AI right, it generates value and ROI for the enterprise’ is an excellent premise, however the full potential of AI hasn’t been attained,” AJ Abdallat, CEO of Glendale, Calif.-based Beyond Limits said. “Many conventional AI systems are merely machine learning, or neural networks, or deep learning. They’re good at handling large sets of data but lack situational awareness or the ability to navigate around missing or incomplete data. They get stuck.”
He cites the example of a machine learning system that can be trained to identify photos of chairs but will never know what a human being is or why a person might need three kinds of chairs for different reasons. As AI systems acquire cognitive reasoning abilities — an evolutionary leap beyond conventional AI — they employ a human-like ability to perceive, understand, correlate, learn, teach, reason, and solve problems faster than conventional AI solutions. “Cognitive AI systems are designed to magnify human talent, providing actionable information faster, reducing risk and identifying opportunity. In our view, the real potential of AI is a symbiotic relationship with people, almost like an assistant that enables humans to apply their attention, experience, and passions to solving problems that truly matter,” he said.

AI Is Moving To The Edge

Intelligence is also moving to the edge. With cognitive intelligence and situational awareness embedded in edge devices, these devices will be able to read sensor data and analyze it in the context of historical data, human expertise, and overall system performance goals to solve problems on the spot, in real-time. “This has profound positive implications to deliver the benefits of AI to industries as diverse as healthcare applications in clinical patient care, industrial process control in remote or dangerous locations, or bringing human expertise to every node in a network, no matter how geographically dispersed,” he added. It doesn’t end there though. The next big era in AI and the technology that will bring immense ROI for enterprises is intelligent hardware.
Even where organizations have access to affordable, powerful tools and hardware that make this possible, providing access to the data is only part of the equation ThoughtSpot’s Chief Data Officer, Doug Bordonaro, added.  Employees must be able to assess the value of the data they have and interpret it properly. Not everyone needs to be a data scientist to get value from data, but everyone needs to be data-literate.
“Once employees have access to data, they need to be able to view it, manipulate it, and share results with colleagues. Confining data to a desktop application is limiting and leads to inconsistencies as information gets out of date. Having a common platform for viewing, analyzing, and sharing data is helpful. It provides a single source of truth, ensuring everyone has access to the latest information. It’s also much easier to enforce policies around security and governance when data is centrally stored and managed,” he said. ”
Related Article: 8 Examples of Artificial Intelligence (AI) in the Workplace

How AI Models Help

At the heart of all AI is a model. Mac Steele, director of product at San Francisco-based Domino Data Lab said that organizations fail to achieve the promise of their models (and AI overall) because they assume models should be managed like other assets, data and software, when they are quite different. To be successful in building, deploying and sustaining models at large scale, companies need to develop an organizational capability of model management. Leading companies have built a strategy comprised of five elements. Steel outlines the elements as follows:
Model Technology – The software tooling and infrastructure stack that gives data scientists the agility they need to build and deploy models.
Model Development –  Business processes and systems that allow data scientists to rapidly develop models, experiment, and drive breakthrough research.
Model Production –  The mechanism(s) of operationalizing data science research projects to a live product or output that affects the business.
Model Governance – The ability to constantly monitor the activity, performance, and impact of models and data science initiatives across the organization.
Model Context – At the heart of Model Management, Model Context encompasses all knowledge, insights, and artifacts generated while building or using models.
This is often a company’s most valuable IP. The ability to find, reuse, and build upon it is critical to driving rapid innovation and a model-driven culture.

Implications for the Digital Workplace

In practical terms,  according to Praful Krishna, foudner of San Francisco-based Corseer, this means four things in the workplace. Successful AI platforms that provide a worthwhile ROI should do the following:
Train without annotated data – In any enterprise AI project, annotating the training data is the most cumbersome and least enjoyable step. Any AI platform that can train without this burden on the users can deliver very high ROI.
Offer adaptable platforms – AI platforms that are adaptable and let their users model bespoke solutions are more accurate and deliver higher ROI, than AI products that try to take a cookie cutter approach. A reason for this is that AI solutions train on very pertinent data while AI products are really solving somebody else’s problem.
Be able to ingest any data – Enterprises, even the ones with data lakes in place, have their data stored in various media, formats or access levels. An AI platform that can ingest all this data in a relatively automated way is usually more successful.
Offer transparent AI – A disproportionate number of AI projects get stalled because they are blackboxes that do not explain why some mortgage application was declined, or why certain diagnosis was made. The current regulatory impetus is about explainable AI platforms.

What to Look For in a Platform

However for an enterprise to do this ,according to Tom Wilde, CEO of AI startup Indico they should be considering the implementation of platforms that offer the following.
Incorporate explain-ability into the learning models it produces – This enables the data science team to be able to work effectively with the business/SMEs and demonstrate credibility as well as meet the variety of requirements around compliance, etc. Without it, the business is very unlikely to participate.
Data segregation – Enterprises should not allow their data to be incorporated back into vendors’ data models to improve that vendors’ models and benefit other companies/competitors that might work with that vendor. Any valid enterprise platform should be able to incorporate clear boundaries here.
Manage both structured and unstructured content – Most enterprise platforms today are well-suited for the former — but do not work well with the latter — messy, document-based text and images that make up over 80 percent of the data in most enterprises.
A collaboration framework/tool – This enables data scientists and SMEs to easily work together to evaluate model performance, label data quickly, adjust on the fly, etc. If the business can’t participate in an efficient manner, they won’t participate, and AI will be very limited in its business impact for the enterprise.

24 July 2018
Author: Anna Peters


What does an innovative company do to recruit and retain interns? I had the pleasure of speaking with Mario Portugal, Head of Recruitment at Beyond Limits, a leading developer of advanced artificial intelligence (AI) solutions. They built some strategic elements into their internship program that help them recruit the right talent and retain that talent into the future, all while allowing other employees to benefit from what the intern team has to offer.


Developing interns into leaders

Beyond Limits, says Portugal, wants to build awareness of AI beyond the stereotypes. “Sometimes people think the robots are going to come and they’re going to take over and enslave all of humanity. And we’re going to have to move underground to survive. We’re trying to move away from that and really show people how we’re using our technology to really create effective and efficient ways to work.” That sort of environment pushes their interns to think differently. And that exposure alone to an innovative space can prime interns for being innovative leaders.

In addition, Portugal says they focus on developing interns into leaders by focusing on their emotional intelligence. “They have the technical capabilities. We’ve given them projects where they’ve already shown us that they’re technically capable.”

Perhaps most impressive, however, is that the interns receive executive coaching—on day two. The coach did a workshop and they built a project charter. “They started talking about roles that everyone was going to do, so everyone knew that it was going to be a team effort.” The feedback Portugal heard was that the experience was “incredible.” Interestingly, the most positive feedback came from those on the technical side: the software engineers and data scientists. Those interns who came through an MBA program had already been exposed to that sort of coaching, but “on the engineering side, they were like, we have never experienced this in any classes.”

“The mission of the program is to develop leaders,” says Portugal. “There’s no way we can achieve this without giving them this extra step. Everybody needs to be a leader. It’s not about one person and the results of any one person. It’s about the entire team and what they’re trying to accomplish.”



Professional development is a big part of the program. For example, their lead hiring software engineer manager does “an overview of technical interviewing and how to be successful in your interview,” says Portugal. Another workshop was about how to make a successful presentation.


Interns add real value right now, not just potential talent to be developed

Many organizations spot entry-level talent and funnel that into their pipeline to grow their future leaders. At an innovative company like Beyond Limits, however, their interns add immediate value precisely because they are still at the university level. Portugal says they rely on having “folks that are embedded in universities because they have the latest knowledge. They’re studying the latest practices. They’re not afraid to try things because that’s what they do at school, right?” Bringing in interns who still wear the student hat helps the company “stay at the leading edge of innovation.” By embedding them into the workplace, other employees benefit and learn too.

“As you bring new folks in and they bring in these new ideas, as long as the environment is welcoming to those ideas, you can grow from those.”

The internship experience is entirely project-based. “As we were growing the internship program and developing the strategy, we wanted to make sure that they had a meaningful experience, and that they weren’t just coming here to do the tasks that nobody else wants to do,” says Portugal. Project work made sense, first because of the current project workload that their teams were carrying, and also because without a project focus, “we weren’t necessarily confident that all of them would get the attention that they needed, really get exposed and have that meaningful experience that we wanted them to walk away from.”

Interns get to work on “real problems,” says Portugal. They get very engaged because their work is not just an exercise. “They have project sponsors that work with them and check in with them on a weekly basis to say, are there any roadblocks? Are there challenges that you guys are facing?”


Spotting the right innovative talent among intern candidates

Beyond Limits recruiters focus on candidates’ involvement in clubs, groups or other extra-curricular activities. “One of the things I love about student organizations is that they’re student-run and no one’s getting paid to do it. They don’t earn extra income, they don’t get additional financial aid, so if you see someone who is involved and plugged into the university, especially if they’re taking on leadership positions like that, that’s someone who knows how to work well with others.” Portugal picks out candidates who show soft skills such as collaboration.

Emotional intelligence is also critical. “Technical skills are important. Don’t get me wrong,” says Portugal. “The interview team and the hiring team are definitely going to assess for those technical skills. But at the end of the day, it is really important that in order for someone to be successful here, that they can actually work within the team and they can collaborate.”

They look for candidates with humility, too, and the ability to push aside their ego, despite their impressive technical credentials. They want people who are driven to be valuable team members, because, says Portugal, “the only way we’re successful is when we all can come together, put our differences aside, or even sometimes bring our differences to the table because that’s what allows us to achieve greater goals when we’re able to work together.”

Because the internship program is project-based, Beyond Limits looks specifically for students with diverse skill sets. Portugal acknowledged that their condensed timeline for launching the program made it difficult to spend the time needed on specific diversity recruitment. Moving forward, however, as they build their campus recruitment efforts, he says they plan to partner with student organizations that will allow them to get to know diverse talent.

“At the end of the day, we need to make sure that we’re providing our hiring leaders with a diverse candidate pool because they will never be able to make diverse hires unless we’re making that a priority. And the way that we do that, as we’re sourcing for talent and as we’re going out into the market, is making sure that we’re using different resources. That we’re not just going one place.” Even if they could recruit enough qualified talent from one school, Portugal says that would give you “people who are all taught the same thing, who were all taught to approach problems very similarly. And I guarantee you nine times out of 10, if you put something in front of them, they’re all going to think about it very similarly because that’s what they were taught.”


Collaboration is key to success as an intern

An interesting thing happened when Beyond Limits split their interns into two teams. “They have decided to combine themselves into one team because they were encountering similar issues, so that they can solve some of those problems together. Which I thought was great,” says Portugal. They realized they needed “the power of each other in order to solve these [problems].” Allowing them to restructure in this way unlocked the power of their diversity. “It’s bringing people together from different backgrounds that have different ways of looking at things because of their experiences, because of where they come from, and allowing them to come up with solutions that the team before would not be able to come up with on their own.”



To encourage collaboration and innovation, Beyond Limits has a very open workspace, and the majority of their wall space, says Portugal, is writable. “So when you walk into our office, you’ll see walls that have all kinds of writing on them.”


Reverse mentoring program

Like many internship programs, Beyond Limits folded an element of mentorship into the experience. Portugal says he gets more positive feedback from the mentors than the interns. “Reverse mentorship is happening,” and he says it’s both intentional and accidental. They are international about understanding what each intern and each mentor is looking for and wants to achieve, and the matches that benefit the most indeed tend to be “cases where we’ve really spent the time to pair folks up correctly.”

Mentors are learning both hard and soft skills from their mentees. “They’ve learned some technical things that they hadn’t known before because again, some things that are being taught right now are not even in the workplace yet.” But they’re also developing leadership skills. “One of them came up to me and said, you know, at first I was a little worried about this pairing […] but at the end of the day what I’ve learned is that I’m becoming a really good listener, and it’s something that I don’t know I was ever really good at it.”


Conversion of interns into full-time

Portugal’s goal is to convert 100% of their interns. “I would love to convert every one of them! I’ve been a part of organizations that have had internship programs with no conversion plan and I think it’s so sad.” Aside from providing an amazing and engaging experience to their intern cohort, “at the end of the day you also want to have growth plans for people.” To compete for talent in AI, Portugal strives to communicate to their interns that there is a place for them at Beyond Limits. Their talent acquisition strategy is not just to recruit and attract talent, but to retain it as well.

Beyond Limits plans to continue engaging their interns when they return to school. “We’re actually going to have brand ambassadors through these interns,” says Portugal, “who can talk about the experience that they had. In my experience in talking to students, there is no one who could sell better than their peers.”


Read full article from CIO Applications here.