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.


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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.”

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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
01 October 2018
Read the full Forbes article here.
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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.

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Cognitive AI in edge devices will enable the Internet of Things (IoT) to become more than a collection of sensors. AI on a Chip promises a revolution in IoT applications because it delivers actionable intelligence at the edge, where it previously was impossible.

We live in a digital world where everything can potentially be connected to everything and generate data. Data is a core resource and we need to capitalize on it. Instead of simply sensing our environment, we can transform it into something that is safer, more profitable, and insightful. The key is actionable intelligence, which is data and information that can be immediately acted upon without further processing by man or machine.
In its varied forms, from the mysterious brain of the octopus and the swarm intelligence of ants to Go-playing deep learning machines and driverless vehicles, intelligence is the most powerful and precious resource in existence. Despite recent advances in Artificial Intelligence (AI) that enable it to win games and drive cars, there are countless untapped opportunities for intelligence to have a significant impact on making the world a better place.
Cognitive AI is not about chatbots, talking virtual assistants, or playing chess against a machine. It’s about powering the next generation of commercial and industrial IoT edge devices, making it possible to apply them in scenarios that we can only dream about right now.
Businesses are eagerly embracing the Internet of Things and its potential to make new product offerings possible, provide actionable business insights, lower costs, and increase productivity and safety. BI Intelligence predicts that 34 billion devices will be connected to the internet by 2020, up from only 10 billion in 2015, with businesses being the top adopters of IoT solutions. A recent study by International Data Corporation (IDC) projected worldwide spending on the IoT to reach $772.5 billion in 2018, up from $674 billion in 2017, and surpassing $1 trillion in 2020.
Leading the pack in IoT spending this year will be the manufacturing ($189 billion), transportation ($85 billion), and utilities ($73 billion) industries. Healthcare is also a bright spot for the Internet of Things. The global market for wearable connected medical devices is projected to reach nearly $19.5 billion in 2021, up from $5.5 billion in 2016.
Connectivity, Intelligent Analysis Abilities Remain Stumbling Blocks
As the number of commercial and industrial IoT devices proliferate, connecting them and getting them to behave intelligently are among the biggest challenges to IoT realizing its full potential. An industrial facility might have 20-30,000 sensors monitoring status of thousands of machines and processes. But they often reside in silos that do not communicate. Some AI solutions are dependent on a cloud service architecture. Essentially a mainframe approach with centralized computing.
But in many industrial locations, sufficient bandwidth or even connectivity cannot be relied upon. Sensor data needs to be collected, correlated with historical performance data, and analyzed to provide actionable information and make decisions in real-time. There’s no time to reach out to the mother ship for answers. One important strategy for obtaining timely actionable intelligence is embedding intelligence at the source of the sensing. This development enables decisions to be made at the sensor rather than “phoning home” to headquarters or a cloud service for “what to do next”.
Since many IoT applications have operational control, making decisions quickly is essential. Unfortunately, the latency inherent in data processing and decision support far from the edge is too slow for many applications.
In some cases, IoT devices must be controlled within milliseconds by intelligence that resides within the control loop. A good example is military aircraft, where sensor data needs to be acted upon constantly, on the spot. If the thousands of sensors were wired to a central computer onboard the aircraft, the wiring and the computer could weigh more than the entire aircraft. This necessitates the use of edge computing architectures and equipping smart IoT devices with artificial intelligence.
AI on a Chip Unlocks Intelligence to Operate Complex Systems and Makes it Tamper Proof
Today, 25% of organizations with established IoT strategies are also investing in AI. However, what commonly passes as “AI” these days – conventional software approaches designed to handle very large, complex data sets, or chatbots that possess rudimentary contextual awareness – are not sufficient.
Some chip companies are working on incorporating AI software on their chips. One software company, Beyond Limits, is doing the reverse: building advanced cognitive AI that can be embedded in off-the-shelf inexpensive chips. When edge devices are equipped with cognitive intelligence and are able to act without moving all the data to remote data centers for analysis, the number and type of new smart IoT applications is virtually limitless.
Consider These Real-World Industrial Applications
•   Mesh networks, such as swarms of industrial drones or remote facilities filled with smart sensors and actuators, need to be able to communicate and coordinate with each other to accomplish tasks without being connected to a mothership.
•   An offshore oil rig may have thousands of sensors that need internet connectivity to provide data to the land base. Meanwhile, rigs don’t have fast internet connections, so cloud service AI is not feasible.
•   In the energy industry, determining where to site a well is an expensive decision. Expert human decision-making must be augmented and backed up by data from geological, geospatial, seismic, weather, historical production performance, and subsurface sensors that must operate in remote areas and under extreme conditions.
Cognitive AI: Intelligence at the Edge
Cognitive artificial intelligence – truly intelligent symbolic AI software with bio-inspired, human-like reasoning ability – will take IoT technologies to the next level and allow enterprises to make full use of their IoT investments. Using cognitive AI, IoT devices can work together to not only analyze time-sensitive data at the point of origin but also diagnose and solve problems in real-time, even when the devices cannot communicate with their operators.
More futuristic applications of cognitive AI in the IoT sphere include:
•   Currently, large fleets of ships are largely unmonitored and un-instrumented, especially compared to other modes of transportation, such as jets and smart cars. If we imagine supertankers as nothing more than larger-than-average IoT devices, they can be connected, tracked, and coordinated through networks of satellites powered by cognitive AI.
•   Cognitive AI can take smart medical devices to the next level by making it possible for two completely independent systems – one inside the body, the other attached to the body – to work in sync. Imagine an intelligent spinal alignment implant that communicates with an intelligent prosthetic limb to coordinate strategies to give a patient balance, allowing them to walk confidently, increasing their stamina and reducing their strain, all while providing actionable data to physical therapists. Currently, IoT medical devices are not much more than dynamic alarm limits.
Sounds like science fiction? These scenarios are not so different from empowering semi-autonomous rovers on Mars that make decisions, from great distances and in the most extreme conditions imaginable – which has already been done by Caltech and NASA’s Jet Propulsion Laboratory. In 2012, AI technology was used to support the landing of the Curiosity Rover on Mars and operate it 150 million miles from Earth. Advanced AI technology has also been employed by NASA to monitor the Voyager 2 deep space probe and search for water on Mars.
The vision of the future for AI includes cognitive systems that can do what machine learning systems can’t: intelligently and fluently interact with human experts and provide articulate explanations and answers, even at the edge of the network. Across the board, you will see, and work with, systems endowed with rare and valuable intelligence.
About the Author:
AJ Abdallat is CEO of Beyond Limits, an artificial intelligence and cognitive computing company that is transforming proven space and defense technology from NASA and the U.S. Department of Defense into innovative solutions to address large and emerging markets.