This year, Beyond Limits was honored by being presented with yet another esteemed award from Frost & Sullivan. For six decades, Frost & Sullivan has been world-renowned for its role in helping investors, corporate leaders, and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models, and companies to action, resulting in a continuous flow of growth opportunities to drive future success. Award criteria and selection processes involve industry analysts comparing market participants and measuring performance through in-depth interviews, analyses, and extensive secondary research to identify the industry’s best practices.
2019 Frost & Sullivan Technology Innovation Award – North America
Frost & Sullivan presents their Best PracticesTechnology Innovation award to companies that develop products with standout innovative features and functionality which are gaining rapid acceptance in the market. This award recognizes the quality of an organization’s solution and the customer value enhancements it enables. Beyond Limits’ award was representative of a Frost & Sullivan commendation for “sharing accumulated expertise across a variety of industries with its innovative Cognitive AI which combines the expertise of human knowledge with a symbolic AI engine to support human decision-makers.” (Frost & Sullivan)
Frost & Sullivan applauded Beyond Limits’ technology for pushing past conventional artificial intelligence limitations concerning defined data sets and controlled environments – with the ability to draw from small, incomplete, or unstructured data sources and providing auditable solutions for a variety of highly complex use cases of interdependent systems and variables. Frost & Sullivan also made sure to call out the fact that Beyond Limits Cognitive AI advisors have the unparalleled ability to help organizations replicate the capabilities of top-performing individuals to guide decision-making across the board.
One major, differentiating feature of Beyond Limits’ Cognitive AI – pointed out by Frost & Sullivan – includes the explainable nature of the solutions. Award analysts admired the fact that this artificial intelligence could actually operate inside of a transparent, glass box – as opposed to the more commonly found black box AI solutions that do not provide any insight into their results or recommendations. In this way, Beyond Limits artificial intelligence advisors are more likely to solidify essential trust from the human decision-maker they are designed to support.
“The company’s advisors, or cognitive agents, provide tangible results and recommendations that are always explainable. These explainable results, or transparent audit trails, are derived from cognitive patterns of life technologies that automatically capture all of the internal inferences that were used to generate the result. These audit trails are used internally to provide heuristics and hints to other reasoners, and these trails can be reformulated by other symbolic technologies into a variety of formats, such as narratives.” (Frost & Sullivan)
Another distinguishing feature that contributed to Frost & Sullivan’s assessment, included the technology’s flexibility to be generally applied to a variety of production circumstances with optimization and results-oriented solutions that can be tailored for the complex needs of particular customers. In situations where marginally shifting process margins can yield millions of dollars in savings, Beyond Limits was saluted for developing strategic applications for some of the highest value assets across the globe.
The Energy sector is no exception and Beyond Limits’ Refinery Operations Advisor (ROA) is a great example of the impact their artificial intelligence is having on critical industries with such high-value assets. ROA has provided the industry with unparalleled visibility, efficiency, reliability and consistency across the entirety of operations, helping companies realize tight-margin targets unlike ever before. The AI solution has been designed to empower refinery teams to plan, operate, and improve processes, accelerating time to decision and increasing consistencies between shifts through a cognitive trace summary, live view scorecards, centralized planning, and retrospective analysis.
“Beyond Limits’ flagship product, the Refinery Operations Advisor, drives operational efficiency through streamlined decision making. It identifies operational issues in real time and recommends responses. For example, in refineries, numerous sensors generate massive amounts of data, much of which goes unused. Beyond Limits combines this data with human expertise and digitizes operational models to allow operators to plan, scale, and increase profitability,” said Clare Walker, Industry Principal, Frost & Sullivan. “Beyond Limits also enables companies to retain industry knowledge that would otherwise be lost when veteran engineers retire. It embeds their domain expertise into systems to make it available to junior engineers.”
2021 Frost & Sullivan Best Practices Award – Global Company of the Year
Every year, Frost & Sullivan presents their Best PracticesCompany of the Yearaward to organizations that demonstrate excellence in growth strategy and implementation in their respective fields. Each award recognizes a high degree of innovation with products and technologies and the resulting leadership in customer value and market penetration. The awards spotlight companies in a variety of regional and global markets for demonstrating outstanding achievement and superior performance in areas such as leadership, technological innovation, customer service, and strategic product development.
Frost & Sullivan again took the opportunity to commend Beyond Limits for the capabilities of their unique Cognitive AI technology, this time shedding a more pronounced spotlight on their solutions’ impact and growth across the Financial sector. Notable focus was placed on the international growth that has taken place since their initial award with particular attention paid toward Beyond Limits’ expansion into Asia Pacific, with regional headquarters having opened in Singapore.
“While Beyond Limits has primarily worked within industrial settings, it recently entered the financial services sector. In 2020, it extended its presence to Asia-Pacific through three main segments: general administration, compliance and accounting, and transactional and new business issues. By minimizing manual labor, backlogs, false positives, and negative determinations, it enables its customers to drive revenue, comply with regulations, manage accounting operations, and process a continuous stream of financial transactions.” (Frost & Sullivan)
Beyond Limits’ Cognitive AI for Finance incorporates state-of-the-art technology designed to help industry professionals more effectively assess and realize opportunities with improved risk mitigation. This advanced artificial intelligence software employs human-like reasoning to provide financial professionals support through improved quality monitoring and reduced uncertainty in credit portfolios, increased visibility into credit and loan identification, as well as enhanced risk calculation and underwriting.
From One Award to the Next: Beyond Rapid Growth
The rapid rate of growth and progress Beyond Limits has experienced from the first accolade to the second speaks to the apropos disposition of Frost & Sullivan’s awards.
“Beyond Limits is currently building out a full value chain, expanding its product portfolio with additional software-as-a-service products,” noted Jeffrey Castilla, Best Practices Research Team Leader, Frost & Sullivan. “Overall, its technology focus and solid expansion strategies are expected to help it continue growing rapidly in the global Cognitive AI market.”
Beyond Limits Cognitive AI for Formulation and Cognitive Formulation Advisor (CFA) are just a couple more noteworthy solutions that have been perking up expert ears across enterprises. Designed to give companies a competitive advantage by facilitating more efficient formulation development processes and ultimately lowering costs, these solutions leverage both historical data and domain expert knowledge to accelerate time-to-market and deliver hundreds of optimal, viable lead candidates in minutes. CFA identifies best-performing, most economic blend combinations that are optimized with respect to material cost while providing the innate capacity to deep-dive into formulation recommendations with the same evidence-rich audit trail capabilities that were mentioned earlier, increasing confidence in predictions and lowering the risk of failure.
Talk about growth, last summer Beyond Limits also closed their Series C funding round at a stellar US $133 million. The significant capital is already being put to good use, expanding business both in the United States and internationally; this includes the aforementioned launch of Beyond Limits Asia, with regional headquarters in Singapore and operations in Hong Kong, Taipei, and Tokyo. Additional expansion funds were also allotted for extensions into Europe, the Middle East, and Africa. Resources are also apportioned for further development of Beyond Limits’ Cognitive AI applications and SaaS product portfolio, while also continuing to bolster their Beyond Labs R&D program.
At this juncture, it’s safe to assume the organization’s name says it all – Beyond Limits knows no bounds. These prestigious awards and accolades by Frost & Sullivan signify a more-than-promising enterprise-grade AI software company on a powerful path without limits.
Artificial Intelligence (AI) solutions for the downstream and process manufacturing industry are proving themselves as frontrunners when it comes to streamlining processes and optimizing entire operations during this time of digital transformation. Organizations across the globe are grasping just how vital intentional digitalization strategies are to gaining competitive advantages and overall growth prospects; lubricant formulation companies and other blend manufacturers are no exception. Cognitive AI in manufacturing for lubricant and product formulation is already making its mark.
“Manufacturing companies are coming to realize just how important this tool can be to create a more efficient supply chain. Recent advancements in AI and data analytics have allowed manufacturers to eliminate potential bottlenecks and improve quality management in their manufacturing cycle. Industrial lubrication manufacturers can specifically benefit from this technology in many ways: procurement pattern breakdown, production scheduling, calculation of material requirements, increased efficiency of regularly scheduled maintenance, and increasing forecast accuracy are just a couple of areas that this technology can make a positive impact.” (Chemseed)
Intricate Industries that are Time-intensive & Require Significant Resources
Formulation and process manufacturing are multidimensional sectors with complex organizational requirements and sophisticated processes involving numerous stakeholders across facilities. Simply stated, business isn’t easy and it isn’t getting any easier – particularly as specialized domain experts become harder and harder to find. That essential knowledge is invaluable to processes and operations, yet not easily accessible. The industry is further complicated by the introduction of more and more constantly fluctuating protocols.
Conventional formula discovery and development processes for lubricants are complicated, costly, and time-intensive. The industry is primarily manual, demanding a lot of resources and research with habitual requirements, as well as several stages of discovery, optimization, and testing. Substantial specialized domain expertise and extensive trial and error procedures are also fundamental necessities that cannot be done without. All of this results in consistently high-cost, high-risk practices.
Eternally shifting regulations, oil and other base stock prices in a relentless flux-state, along with demanding technology needs have made maintaining profitability problematic for the lubricant industry. Maintaining a crucial competitive edge means the industry must evolve by reexamining traditional long discovery to testing methods in ways that heighten processes, fast-track time-to-market, and elevate operations.
The Threat of Losing Essential Expertise
Another huge concern involves the loss of long-standing expert formulators’ knowledge and experience as they retire from the workforce. In such a specialized field, with a limited reserve of domain experts, retirement and workforce attrition are made even more problematic. With sectors as exacting as formulation and process manufacturing, realizing program objectives commands both an advanced, cohesive method to blend identification and unlimited access to uniformly established sets of expert knowledge bases at any given moment.
Organizations are widely accepting that an imminent need exists for an industrial, state-of-the-art software solution that brings several steps of product development into a singular, cohesive platform while also integrating their extremely valuable institutional knowledge to leverage their most valuable assets – that being historical data records.
“The aim is not to replace formulators, but to automate only those aspects of their activity where it is reasonably practicable to do so. The basic approach taken is to view formulation as a hierarchical planning activity, with deep knowledge represented using a causal model. Formulation decisions are made by rule-bases which mix heuristic knowledge and causal reasoning, together with the facility for formulators to enter their own decisions.” (Elsevier Ltd.)
Lubricant Formulation Process Use Cases
Progress in technology will inevitably result in equipment designed to advance production, leading to accelerated operating speeds that yield higher temperatures and pressures. This increase will place greater demands on the lubricants. These demands, in conjunction with significantly decreased predictive maintenance requirements, increased levels of environmental cognizance, firmer safety protocols and fortified emphasis on energy efficiency, will continue to challenge the industry and propel the need for advanced AI in manufacturing for lubricant formulation to help it keep up.
Compelled by calls for a boost in efficiency and enhanced dependability, manufacturers figure lubricants will be put to use under harsher circumstances. While energy savings and productivity are vital to cost-effectiveness, constraining emissions and executing waste generation/removal are imperative to environmental considerations. There are many different use cases for industries that rely on lubricants, with the formulation process being vital to each respective industry; the following examples are just a few notables.
At the moment, some typical initiatives for the Industrial segment involve equipment downscaling simultaneous to upscaling productivity, realizing greater levels of output per unit of energy consumption. Recognizing said initiative consequently leads to higher strains on lubricants, whether from lesser oil quantities, higher temperatures, accelerated speeds, or larger loads. It’s also worth mentioning that oil is being expected to last longer stretches before needing to be changed. Such elements decrease equipment downtime and boost production while decreasing waste oil and accompanying disposal costs.
Several use cases exist in the industrial sector that require the most effective possible lubrication formulation techniques possible including the production of refrigerant, gas, air, natural, and hydrocarbon compressor oils to name a few examples. In this scenario, lubricants require congruity with the compressed gas, precise viscosity for compressor type, and elevated flash-fire point and auto-ignition temperature. Effective oxidation and carbon formation resistance, water separation, anti-wear/corrosion safeguards, and low temperature plus detergency for portable equipment are also necessities.
Another important use case demanding superior lubrication formulation includes the production of oils for the likes of steam, gas, and hydraulic (hydro) gas turbines. As an instrument converting the power of a gas or liquid moving over collections of rotors and stationary blades into rotary power, it is essential this process goes off without a hitch as it is one of the main methods for producing energy to our world – among numerous other essential functions such as powering aircraft engines, locomotives, and ships. In these scenarios, turbines require the formulation of oils that lubricate, seal, eliminate heat and contamination, as well as avert rust and corrosion.
Currently, as engine and transmission technologies continue to evolve, some typical initiatives for the Automotive segment involve improving fuel economy and reducing emissions. The majority of countries have initiated aggressive policies around fuel economy and emission targets new automobiles must adhere to right out of the factory. In this scenario, engine lubricants require formulations that will decrease friction and block metal-to-metal contact, eliminate heat and wear particles, as well as neutralize combustion products and lessen corrosion. Clean engine component preservation and effectual cylinder sealing for exhaust gas blowby minimization are also required on top of elevated fuel economy and reduced emission expectations.
Following up on the idea of reduced emissions, sustainability around lubricant formulation for the Renewables segment poses an important discussion. The mounting mindfulness for environmental concerns contributing to the climate crisis is making businesses more conscientious of the efficiency of the products they both utilize and produce in terms of waste and other economic impacts. The utilization of renewable biofuels in vehicles – and various other consumer products such as lubricants made from biodegradable and renewable base stocks – is rising as legislation and mandates around climate considerations become the norm for the majority of countries, industries, and businesses across the globe.
“I understand the importance of a worldwide movement toward a lower-carbon future. Our company plans to continue supporting client needs while tackling the cleaner energy challenge together, playing a part in the complex global challenge of transitioning to a future of less carbon while still meeting the energy needs facing our world today. Artificial intelligence will play a key role in helping companies and industries achieve net zero ambitions. Accomplishing a low carbon future will require more efficient operations that help increase productivity and reduce waste. AI for energy can work toward this with technology designed to optimize efficiency, yielding more economical utilization of resources to accelerate renewable, decarbonization and carbon-negative initiatives across the globe.” (AJ Abdallat for Forbes)
An Explainable Cognitive Formulation Advisor Leads the Pack
When all of these requirements for industries start to stack up, the only real contender to take on the challenges boils down to Beyond Limits’ Cognitive Formulation Advisor (CFA). This technology seamlessly blends all of the imperative needs of industries as critical as Formulation and Process Manufacturing, marrying all the features that comprise the perfect solution. Cutting-edge advancements in artificial intelligence, machine learning, data science, computer science, and cloud technology are employed to tackle such dynamic challenges. Cognitive AI in manufacturing for lubricant and product formulation makes use of a data-driven technique via web-based software that delivers a fast and effective solution in a fraction of the time conventional practices would typically take.
The solution encodes an organization’s historical blend data, constraints, and expert formulator knowledge. The system embeds that expertise into the workflow then leverages the information to cleverly recommend hundreds of viable lead candidates through an explainable solution with human-like reasoning. This fast, reliable and unified process standardizes formulation procedures, making them easily executable across an organization. The result is streamlined lead discovery and optimized testing processes, amounting to hundreds of optimal, readily identifiable top-performing – and more economic – blend combinations (with respect to material cost) within minutes.
Beyond Limits’ Cognitive Formulation Advisor is uniquely pioneering AI in manufacturing. It has the ability to work even in scenarios where data is missing or misleading; it is also fully explainable and transparent, buttressing trust and building confidence in predictions. The solution facilitates access to every bit of essential information, providing the ability to deep dive into every formulation blend recommendation with evidence-rich audit trails. The advisor is exactly that, acting as a trusted aide (rather than a replacement) to human decision-makers, providing support by augmenting decision-making for the better.
“It’s truly amazing, what this technology is able to do. By combining numeric and symbolic artificial intelligence, we have developed a tool that has unprecedented flexibility in terms of data type and application area within the general lubrication framework. Overall, more reliable predictions – both in terms of properties and test performance – reduce the risk of failure in later stages and significantly cut down on program time and cost.” (AJ Abdallat, Beyond Limits CEO)
Through increased holistic visibility into operations on a more global scale, process optimization and continuously advising on risk mitigation opportunities, CFA dismantles traditionally time-consuming, high-cost and high-risk processes, ultimately leading to reduced blend recommendation time-table from weeks down to minutes with high predictive accuracy of viscometric properties. When the goal is to achieve faster time-to-market with cheaper material, program and process costs, along with mitigating regulatory, compliance and other failure risks, CFA delivers.
WANT TO LEARN MORE ABOUT BEYOND LIMITS COGNITIVE FORMULATION ADVISOR? CHECK IT OUT HERE.
After being promoted to University Recruiter, the prospect of traveling to network with students was very exciting; then COVID struck, halting all of the new and fulfilling duties this position promised. Companies, schools, and the entire world were all forced to adjust everything that had come before the pandemic – the fun meet-and-greets, community gatherings, and other (formerly) commonplace events were put on hold and traded in for more of a “virtual reality.”
Virtual career fairs and university events became common, necessary placeholders over the past year. What do they actually entail? They are digital events that are similar to webinars. They occur at a specific time in a selected virtual space so that companies, recruiters, job seekers, and students can gather via chat, teleconference, webcast, and other similar platforms to discuss and exchange information about jobs or other potential career moves under consideration.
Growing Pains of a Remote-controlled World
Pre-COVID expectations around university recruitment events involved setting up interactive booths, engaging with hundreds of students both one-on-one and in groups, physically passing out merch, and shaking hands in polite greetings. Of course, the reality of what 2020 doled out for the entire world was quite the opposite. Going from such an inherently immersive in-person experience and meeting students en masse, in small groups, or via one-on-ones – to no face-to-face interaction whatsoever – was an interesting and sudden transition, to say the least.
Every school adopted its own method for organizing virtual university recruitment events, whether held over Zoom or other third-party vendors, such as Brazen. In the beginning, adapting to the new methods of connecting was a learning curve on both sides. Doing everything from home came with its quirks, from the internet timing out at inopportune moments to unexpected puppy-noise interruptions and unanticipated package delivery doorbells – at first, this “new normal” wasn’t exactly entirely normal, per se. However, the freedom to jump up and stretch out at random or fill up a water bottle from a private source were indeed very nice caveats.
Getting Into the Swing of Things
Once a moderately consistent cadence was established, the process did start to smooth out and other tangible benefits of this new approach began presenting themselves. Virtual career fairs generally lasted about 4-5 hours out of a business day and, during that time, we would try to get to one-on-one sessions with as many students as possible. A lot of times, those sessions were a really great way to engage with students on a more personal level, providing a direct line of help for those that needed a little extra guidance or had more complex questions.
As more and more of these events continued taking place over time, it was a great learning opportunity to see what worked best – and what didn’t – from one experience to the next. Watching the process evolve to fit the needs of students during this trying time in their academic careers was pretty inspiring. Everyone was doing the best they could to make an effort and ensure these students would experience at least a little normalcy amid their unique educational experiences.
Other benefits for companies, recruiters, and hiring managers also presented themselves including a reduction in time-spent and out-of-pocket expense associated with attending a traditional, in-person event. Employers were also able to more easily connect with prospective employees from anywhere in the globe, broadening the scope of international potential from a more diverse pool of candidates that may come from a wider variety of disciplines.
A Few Tips from a Recruiter to a Candidate
If you are feeling a little in the dark about an upcoming meeting, here are some tips to make the interaction go off without a hitch:
Do your due diligence. Researching the company beforehand will save you a few extra minutes in the “any questions for us?” portion of the conversation. With that extra time, you may have more of an opportunity to cover over more of your own back story and ask those questions about the company you wanted answers for but couldn’t find easily. Dig into the company’s website, research the CEO, review latest press releases, read articles by their subject matter experts, scroll through social feeds, etc. The more research you do, the better impression you’ll likely make on recruiters. Having a pre-prepared list of questions at the ready is also a very good idea, it will yield a seamless flow in the conversation.
Speak up in group settings. Don’t be afraid to ask questions – especially when in a group! You’ll be the one that stands out in the pack. Plus, other folks participating in the call might have similar questions they may be holding back. Getting the conversation going could help everyone feel more compelled to participate. When it comes to one-on-one conversations, keep it short and sweet; you have to be cautious of time. It’s not uncommon for conversations with students to simply cut out mid-conversation because time was up. In that scenario, there was no graceful exit with a sub-par closing. Important topics to cover include degree and focus, new grad/intern qualifications, and other relevant experiences – whether academic or professional.
Check your tech!You want to come off as well prepared as possible for the meeting. Checking that everything you need to work is in fact working will provide a polished vibe and effortless back-and-forth. Dealing with tech issues on time constraints will lead to a strained and disjointed conversation. Items to check include your camera, microphone, and internet connection. It’s can also be helpful to use headphones for improved sound quality, connection, etc. Also, take the time to figure out a spot with good lighting and a background that isn’t distracting. The focus should be on you not a pile of dirty laundry on an unmade bed. One other item of note, if you can turn your camera on – whether in a group or individual setting – it really does go a long way. In the group setting, it helps recruiters feel more comfortable, from an engagement perspective. The same goes for one-on-one meetings; a camera is much easier (and more personable) than giving off the impression of juggling multiple conversations at once.
Enthusiasm! It cannot be emphasized enough that this characteristic goes a really long way in almost any scenario. Put that passion and drive on display in the conversation; it will make a lasting impression. Recruiters working from home can often start to become a little jaded tired from talking to numerous students saying the same thing all day. Set yourself apart! Come in engaged and bring some flair to the conversation. Have an upbeat ‘about me’ pitch ready and maybe do some practice runs in front of your camera to get a better idea of how you come off. If it helps, a good practice is to record yourself and review the footage to determine areas of improvement. One other point for new grads to consider, don’t feel hopeless if companies that you engage with aren’t hiring. Continue the conversation, keep it lighthearted, and talk about your interests, experiences, future goals, etc. You never know when the company may start hiring again. That good impression you gave may resonate down the line, jogging the recruiter’s memory of you and placing you at the front of the line for a relevant job referral.
Plan for/expect to follow up. Allot enough time to request contact info so you can reach out the next day with a thank you and/or follow-up. Not everyone will think to bookend the conversation this way; it will remind the recruiter of you and leave a lasting impression.
The Future of Virtual University Events
Virtual career fairs and university events certainly served their purpose over this past year and can most definitely be replicated as necessary. If we were to keep one factor that should be carried over from these events, it would be the intimate one-on-one time allotted with each student.
However, while virtual events were decent alternatives that succeeded at what we needed them for over the past year, I think anyone “in the know” would voice a preference for the real deal. The process of hosting group sessions did not prove as fruitful a venture. These often felt a bit forced; when faced with the intimidating eyes and ears of their peers, students were not as willing to engage or ask questions as they normally would in person. When the floor finally opened for students to make their voices heard, quite the opposite would occur and the room was often left adrift in that awkward silence you see in movies or shows, crickets chirping away in the background.
Luckily, as we move into this spring of 2021, a return to some form of what we used to know looks like it may be on the horizon as the pandemic seemingly starts to loosen its grip on the globe. That first on-campus, in-person event is an exciting prospect – and you can bet Beyond Limits will be one of the first booths set up that day. Come see us – we can’t wait to meet you!
WANT TO CHECK OUT OUR CURRENT OPENINGS AT BEYOND LIMITS? CHECK OUT THE CAREER PAGE HERE.
30 MARCH 2021
LOS ANGELES, CA
The midstream sector of the oil and gas industry is faced with complex obstacles relating to expensive, resource-intensive pipeline inspections from unforgiving environments and outmoded maintenance frameworks. Issues also arise due to complications from inaccurate forecasting of high-value commodities as market balances are in constant flux and as supply chain infrastructures struggle to modernize.
Simply put, when it all adds up, these challenges can lead to significant incidents that cost tens of millions of dollars in lost revenue.
Beyond Limits Cognitive AI technology for midstream operations provides state-of-the-art solutions that bring pioneering artificial intelligence to the edge, supporting key stakeholders in keeping the most ideal oversights possible of their most valuable assets while in transit. Explore an innovative approach to midstream with a solution that:
ACCELERATES DECISION-MAKING & INVESTIGATIVE PROCESS TIMETABLES
EFFECTIVELY INSPECTS REMOTE PIPELINES AT THE EDGE, IMPROVING MAINTENANCE & DECREASING RISK
UTILIZES SMARTER SYSTEMS TO STREAMLINE SUPPLY CHAIN INFRASTRUCTURE, REDUCING ERRORS
ENHANCES TRADER SUPPORT FOR BETTER IMPORT/EXPORT MARKET BALANCE PREDICTIONS
Check out this video to see how advanced, transparent AI solutions are supporting stakeholders at every level of operations to Master Midstream.
WANT TO LEARN MORE ABOUT THE IMPACT OF BEYOND LIMITS’ TECHNOLOGY ON MIDSTREAM? GET A CLOSER LOOK HERE!
18 March 2021
Los Angeles, CA
Alyssa Cotrina, Beyond Limits
AI solutions are being employed around the world in response to some of the most complex problems facing critical industries today. Enterprise-grade AI has been demonstrating its impact on numerous industries by supporting decision-makers at every juncture of an organization, helping them realize more value from their data and production processes by elevating operations at every level. Businesses, and whole sectors – like the oil and gas industry – are gaining more than just returns on AI implementations; they are seeing credible profits. The oil and gas industry is one of the most substantial sectors already benefiting from AI solutions.
Good examples of such solutions include Beyond Limits’ suite of industrial-grade artificial intelligence products for the oil and gas industry dealing with a wide-ranging scope of challenges across upstream, midstream, and downstream. Advanced systems like Beyond Limits Cognitive AI are designed to provide transparent recommendations, yielding visibility into operations on a global scale. Such holistic capabilities are being leveraged by stakeholders to attain an improved understanding of how best to utilize their valuable data while gaining greater insight into the role domain expertise and knowledge play in the decision-making process. The result is the extraction of more value from the most valuable assets.
“Society of Petroleum Engineers (SPE), a global industry organization, found that nearly 54% of its members are over 55 years old. 44% of Generation Y and 62% of Generation Z say a career in oil and gas is unappealing.” –EY Global
Cognitive AI has the ability to accumulate and encode veteran operator knowledge then circulate resulting expertise, best practices, and industry standards, organization-wide, bridging the knowledge-loss gap currently being experienced by the upstream sector as experienced operators retire. In this way, that valuable knowledge is immortalized, infinitely accessible, and effortlessly transferrable to newer operators.
The hybrid approach of Cognitive AI marries invaluable knowledge with data-driven methods to deal with incomplete or missing data. By modeling hypothetical outlets using machine learning with symbolic artificial intelligence tactics, Cognitive AI equates to the best form of the technology to shrewdly forecast circumstances or results and recommend intentional actions that produce more confident decisions, regardless of data quality or availability.
Insufficient access to clean data and expert knowledge restricts a stakeholder’s confidence and capacity to make critical decisions in any given second. AI approaches with such advanced prognostic competencies and data accessibility result in rates-of-accuracy and risk mitigation unlike ever before, turning doubts associated with operating under inaccurate, or incomplete models, into confidence.
Cognitive AI is also being used across the midstream value chain as a solution to helps track the movement of high-value oil and gas assets as they move from one locale to another while simultaneously defining the most strategic destination possibilities during transport. The solution is designed to facilitate more precise port origin determinations, better assess cargo value as it travels, and more effectively predict destination prospects overall.
Such capabilities can significantly decrease ambiguities when this essential information is lacking, thus reducing potentially overlooked trade opportunities as a result. This level of enterprise-grade artificial intelligence aids industry organizations in their endeavors to more precisely define the worth of their most valuable assets, in real-time and on-demand.
AI for autonomous pipeline inspection is another state-of-the-art midstream solution that serves to bring crucial intelligence to the edge, powering robotics systems to navigate on their own and negotiate unforeseen obstacles. This capability creates unparalleled outlets for identifying and reporting on problems to improve pipeline infrastructure maintenance and increase the accuracy of wear and tear predictions while pinpointing investigative process timetables and accelerating decision-making.
When it comes to the downstream sector, Cognitive AI is being utilized rather extensively to optimize entire operations from supporting engineers and improving coordination between stakeholders to elevating financial performance in delicate market circumstances. In terms of customary downstream processes, profitability is tricky as big challenges the industry faces include slim margins, complicated decision-making, and constant variability. Surpassing, or even simply realizing, profit optimization targets – in and of itself – is often problematic.
Cognitive AI is designed to make all of this easier by providing a more all-encompassing application of best practices, as well as expert human knowledge and experience, to accomplish planning goals. While production teams initially set out to manage systems, processes, and operator performance in order to boost results, real-world operational states in downstream are inclined to stray from original planning expectations, ultimately requiring human mediation.
Luckily, such an advanced solution sets out to support operators by functioning as an expert assistant, bringing entire processes closer to ideal operation. The solution “thinks” like an engineer, providing help through guidance that enables a more efficient method for unraveling problems across facilities. By venturing outside just the scope of conventional, primarily asset-centric tactics, the system aids operators’ efforts to clinch process goals.
“bp’s goal of market-led growth in refining is now easier to achieve because the Beyond Limits advisor allows us to achieve optimized operation closer to 100% of the time and process a significantly higher crude rate.” -bp Innovation and Engineering, Discipline Leader, Intelligent Operations
On the whole, Cognitive AI solutions are employed in the downstream sector to boost operational efficiency, accelerate time-to-decision, and revolutionize analysis approaches to become more conscious of resource utilization and consumption. The system is transparent, offering collaborative audit trails of recommended remedial actions. In this way, every stakeholder can clearly grasp how and why a particular recommendation was given.
By outlining projected performance and embedding mitigation actions, the pioneering solution sets out to help the sector standardize planning processes when creating and applying strategies. This empowers teams to work homogeneously across silos at every level to affect optimal processes and elevate entire operations.
Coming Out Ahead in the Age of Digital Transformation
“Nearly 80% of oil and gas executives said they ‘agree or strongly agree’ that AI will significantly change the way their companies do business in the next five years.” –PwC’s 22nd Annual Global CEO Survey
Even now, the outlook for artificial intelligence solutions’ impact on oil and gas speaks for itself. As the industry moves rapidly onward and headlong into its digitalization strategies, AI that provides reliable, actionable insights, unparalleled risk mitigation, and transformational efficiency will be the cornerstone for coming out ahead and gaining that crucial competitive edge in this new age of digital transformation.
WANT TO LEARN MORE ABOUT BEYOND LIMITS’ PIONEERING SOLUTIONS IN THE OIL & GAS SECTOR? CHECK THEM OUTHERE.
4 March 2021
Los Angeles, CA
Data Den is a thought-leadership alcove within the world of Beyond Limits where we provide an opportunity to dive into the minds of our gifted data scientists to get a better understanding of their domain. Keep reading to catch a glimpse of their essential expertise; without it, artificial intelligence wouldn’t be possible.
Can you give us more insight into the nature of big data sets?
Data comes in all shapes and sizes but the challenges of leveraging that data are common and widespread. On the collection side, even quantitative data has some uncertainty: collection devices are not perfect, baselines drift, external stimuli can affect a reading, etc. On the storage side, standards may be non-existent or change with time, data is not centralized, incomplete, in various formats, etc. I recommend establishing data standards as the first step in all engagements – existing data can then be properly standardized and future data collection will be readily ingestible. Establishing quantitative standards is straightforward and typically requires agreement on naming, units, data coverage, etc. Qualitative standards are more challenging but may include examples to establish a common baseline. For example, a quality rating system may provide photographs or user stories to help a user determine an appropriate value.
In many cases, industrial AI data sets aren’t extremely large, often in the gigabyte to tens of gigabytes size range, which may be large by PC standards but not by so-called “big data” standards. When you’re working with data sets like that it really comes down to good programming practice, it is straightforward but still requires conscious effort to do it right. Examples include making use of efficient algorithms, designing efficient workflows, storing data in sparse matrices and vectorizing operations. In some cases, it’s necessary to test different libraries to find the most efficient implementation of an algorithm. In other cases, you may even need to go back to fundamentals and write your own algorithm.
Writing algorithms from scratch can be tedious and time-consuming but, in many cases, pays large dividends in performance. While data scientists often focus on correctness and completeness, poor performance will lead to a poor user experience and ultimately be a barrier to adoption. On the machine learning side, most algorithms scale well through parallelization – and cloud computing has enabled virtually limitless scalability. With so much raw horsepower available, the sky really is the limit for AI model training and deployment. In most situations, however, the real bottlenecks are budget, time, and the value of the problem to be solved. When developing the prediction pipeline be mindful of the end-goal, leverage data, and develop features at the scale appropriate to generate a meaningful prediction. Generalization is more important than perfection.
Building on this thought – Can you talk a little more about how all of this plays into workflows that contribute to the user experience?
For any user-facing applications, the user experience should be front and center throughout the development process. For data scientists, this primarily means accurate results and fast execution. I generally approach these in this order: first, we run an R&D phase where we analyze data, engineer features, and test different prediction approaches to measure algorithm accuracy and build an accurate prediction process. If we are working on a data-sparse or scientific domain-heavy application, then we also include a phase to determine where and how to best embed knowledge in our hybrid solution. After we have a workflow that provides an accurate solution it is time to go back and build an efficient deployable version that meets our performance goals.
We have already discussed the importance of good code design – in practical applications, good performance also means effectively leveraging resources. Training accurate machine learning algorithms takes time and most commercial solutions involve a workflow of multiple evaluations, each of which potentially requires real-time training and updating. One of the benefits of cognitive reasoning is that it does not require training, knowledge is directly evaluated based on feature input. However, even with an efficient code design, making tens or hundreds of thousands of evaluations in real-time often means leveraging as many resources as possible to ensure the delay between pushing run and seeing the result works seamlessly.
If we consider the overall process, we can often identify additional areas for further improvement. In many cases, scenarios can be grouped for fewer evaluations, data transmission volume can be reduced to minimize latency, dimensionality reduction can simplify models, and workflows can be parallelized for even better performance. Every problem will be different but it is important to think holistically, algorithms, workflows, and resources should all be optimally leveraged to provide a good user experience. An example of this approach at work here at Beyond Limits, when it came time to push an R&D project to production the execution time was roughly one day and required improvement. Every part of the code was profiled, every process was broken down, and every bottleneck was identified. Over the course of a few weeks, we were able to reduce the execution time to roughly one minute without any degradation in output quality.
Any advice for data science students, data scientists just getting into the job market, and/or professionals potentially wanting to change their career path to data science?
The market has evolved a lot in the last ten years, in both good and challenging ways. For one, the six-week immersion course Data Scientist is no longer. While those days are behind us, what it really means is that people who want to get into the field need to put in the leg work and prove their mettle. Ultimately, that is a good thing for the industry. If it really were that easy – and anyone could transition overnight – it devalues the work we do, and that’s not a career path anyone should want for themselves. There has also been an explosion of algorithms, libraries, and approaches to solve problems – a data scientist should be familiar with this world but doesn’t need to be an expert in all of it. Like with any science, choosing an area or domain and specializing in it has become the norm among classically trained data scientists. Even more common are people who are classically trained in a different domain but focused on leveraging data science tools in that domain during their education. An example might be a neuroscientist who used image recognition to diagnose anomalies from brain CT scans.
A data scientist should be familiar with the standard algorithms, their strengths, weaknesses, underlying principles, and be able to explain how they work. AI-based solutions, if designed improperly, can have (at best) misleading or offensive results such as mislabeled images in a google search>, to (at worst) dangerous results that seriously affect real people in everyday life. Data scientists wield powerful tools and we have an obligation to use them responsibly.
For people working in areas or companies related to AI, wanting to get familiar with the domain, online immersion courses are a great way to get to know the landscape. Though, for those looking to make a transition, the best place to look is often right where they already are. Many companies are actively building data science teams and look internally for candidates. Being proactive to learn the field through online and weekend courses will give you a leg up, and the support of an employer – while you increase your skill level and gain experience – will be a critical factor to a successful transition.
At its core, data science really is about exploration. Having that entrepreneurial spirit and curious mind to try new things, to be undaunted by failure and to think outside the box, are the foundations the entire field is built on.
Favorite publications, websites, blogs, conferences you attend, or books you read that you find helpful to your work?
Kaggle is a data science-oriented website with a lot of great data sets. Professionals will perform analysis and post it on there; so, it’s really good for exploration and building experience.
There are a ton of data science blogs and websites but one of my favorites is the data science section on Medium which is great for digestible and high-level articles.
Any myths/falsehoods/misunderstandings about data science you want to debunk?
Just to re-iterate, the overnight data science career is just not a real thing. There are a lot of people with their YouTube series on how to be a Data Scientist. Don’t be misled, if it sounds too good to be true, it is. Second, Terminators and AGI are not coming just over the horizon. There are very real limitations to this type of technology and machine learning is not “intelligent” like a person is. Algorithms don’t make arbitrary decisions; they execute explicit unambiguous instructions or learn patterns and then repeat those patterns forever.
WANT TO LEARN MORE ABOUT OUR PIONEERING SOLUTIONS? TAKE A LOOK AT OUR TECH HERE.
Dr. Michael Krause is Senior Manager of AI Solutions at Beyond Limits, a pioneering artificial intelligence engineering company creating advanced software solutions that go beyond conventional AI. Michael specializes in industrial AI with experience from bespoke AI solutions at small businesses to digital transformation at large enterprises. Prior to joining Beyond Limits, Michael was Director of Analytics at Tiandi Energy in Beijing, China, and later at Energective in Houston, Texas. Michael holds a Ph.D. in Energy Resources Engineering from Stanford University.
2 March 2021
Los Angeles, CA
Artificial intelligence in energy and utilities is proving to be a tremendous boon for addressing common efficiency issues through Internet of Things (IoT) devices and smart grid capabilities, resulting in enhanced power management. Beyond operational and cost efficiency, AI solutions for power and energy applications are also generating significant benefits for utility customers, the global economy, and the environment.
According to a Microsoft report in association with PwC, AI in the energy sector is expected to contribute 2.2% to future global GDP by 2030. With these gains in mind, it’s no wonder decision-makers in this sector are seeking AI solutions for the impact they can generate. Here are some ways AI is revolutionizing the industry and paving the way for a higher standard of operation.
According to an IEA technology report, the power sector stands to save around $80 billion per year until 2040 with the help of digital technologies. Enhancing these areas via AI solutions results in greater revenue retention, reduced unplanned outages, extended operational lifetime of assets, and boosted power plant and network efficiency.
Power plant operators are going beyond preventative-only maintenance and expanding to include predictive maintenance and fault predictions through equipment monitoring and better data analysis. For example, the United Kingdom’s National Grid has utilized drones and image recognition to inspect over 7,000 miles of overhead lines that transmit electricity from power plants to homes and businesses. Whereas before engineers had to assess damage and maintenance themselves, drones make light work of this task by utilizing high-resolution still video and infrared cameras to allow for an equal (if not better) assessment of equipment. Additionally, AI paired with sensors can be used to monitor equipment and identify faults that might have gone unnoticed by the human eye.
An AI system can then predict failure months in advance through trend analysis and deep learning tactics. Cognitive AI goes one step further to draw explainable insights for power plant operators to act on, thereby saving time and money that would have otherwise been spent on damage control.
+Load Forecasting & Optimization
AI is especially critical for the energy industry to accurately forecast and predict in real-time how much power needs to be produced. Because electricity is a resource that cannot be easily stored, this makes accurate energy consumption a key component of an efficient power plant. In other words, more accurate power predictions mean lower production costs, reduced energy waste, and retained revenue.
Other important factors to understand include the drivers that affect load requirements such as weather, time of day, calendar days, and seasonality. Due to the unpredictable nature of these influences, power plant operators are implementing artificial neural network (ANN) technologies to leverage historical data to generate accurate predictions for future loads that take these factors and their variability into account.
+Enhanced Grid Reliability
In line with forecasting power supply and demand, AI-powered smart grids enable power plants to integrate a network of devices, sensors, and data that can better monitor every aspect of a power plant’s operation to find areas of improvement and increase resiliency. Having individual components communicate with each other enables grid operators to rapidly respond to changes in energy demand and optimize generation as well as distribution. In other words, facilitating an integrated view of each component allows for better grid management.
During a natural disaster or unforeseen emergency, for example, a smart grid can allow for automatic electricity rerouting during equipment failure and even contain power outages when they occur to mitigate their effects. By assessing the damage, controlling it, and addressing it, these smart grids can reduce downtime and accelerate their startup time more strategically.
+Energy Theft Prevention
Energy theft can be especially prevalent in emerging markets, Brazil being a prime example. However, energy theft is not an issue limited to Brazil considering energy companies in other countries like Nigeria are seeing as much as 40% of their distributed energy supply siphoned off as a result of tampered meters or tampered overhead cabling.
AI technology can address this issue by implementing measures such as smart meters to monitor the distribution of energy in designated areas. AI algorithms can also be used to recognize discrepancies in property energy levels and pinpoint areas where suspicious activity is occurring. So, rather than blindly sending engineers to inspect tampered areas—into dangerous locations sometimes—an AI system can identify targeted areas that would benefit most from an in-person inspection.
AI in Renewable Energy
As mentioned before, load forecasting can be unpredictable, therefore necessitating AI technologies to improve forecast accuracy. This is also the case in the renewable energy sector since it’s reliant on uncontrollable elements such as wind, weather, and water flow, making it equally as unpredictable. In addition to the aforementioned use cases for AI in the utilities sector, it’s this unpredictability concerning renewables that AI can address and is why many are already reaping the benefits from AI in renewable energy production.
In fact, machine learning algorithms have been able to increase the value of wind energy for Google’s wind farms by 20%. In the case of wind and solar energy, AI can assess the reliability of solar and wind power generation by analyzing meteorological data, historical weather data, and real-time weather forecasts to predict capacity levels. This gives power plants insight into when they need to gather, store, and distribute power during peak periods. The ability to sufficiently and reliably predict energy supply and reconcile this with variable demands increases the flexibility and sustainability of these energy sources, which in turn increases their value.
On a similar note, hydroelectricity production is seeing extraordinary benefits from AI implementation to help alleviate some of the uncertainty when it comes to changing air temperatures and precipitation levels. Norwegian energy company Agder Energi, for example, conducted a project where they transitioned from using outdated mathematical models for optimizing energy production to using deep reinforcement learning, where the machines can take this uncertainty into account and learn by trial-and-error to optimize production. Faced with growing pressure to reduce their carbon emissions as a result of climate change, AI has become more of a necessity than an optional luxury for many renewable energy suppliers.
Enhancing the Utility Customer Relationship
From infrastructure optimization to supply and demand balance, AI has drastically impacted how the utilities industry operates and this includes their consumer relationships. Customers’ interests and expectations are dynamic and as such, providers must work to keep up with these changes. Gone are the days where consumers will sit back and remain content with traditional power models according to a US nationwide Deloitte study of residential and commercial utility customers. Nowadays, customers are seeking to reduce the cost of their utilities in addition to increasing transparency between them and their utility providers while also expressing growing concerns for the environment.
This generates a trove of opportunities for providers to optimize the customer experience in line with changing interests or create a two-way communication system for increased transparency between them and their customers. All of which can be made possible through an AI-implemented system. For instance, with a disaggregated energy usage strategy, integrated with an AI system, providers can draw insight from how customers interact with their smart home devices and monitor their energy consumption patterns to personalize products, services, and communications. Alternatively, providers can improve customer understanding of renewable energy resources through AI-enhanced renewable energy demonstration projects.
Exponential growth in emerging technologies and ever-changing consumer expectations for their utility providers are revolutionizing the way utility providers are managing their operations. As artificial intelligence continues to grow more advanced beyond conventional data science and deep learning algorithms, so too will the implementation and use cases for these emerging AI technologies.
The energy sector stands to gain much from AI implementation, bringing in opportunities for enhanced asset maintenance, forecast predictions, and heightened energy efficiency. As of 2020, the US Department of Energy has invested $37 million in AI research and design, contributing to the global momentum that many energy suppliers are faced with to include not just AI, but integrated digital technologies in their operations to create a more efficient power plant. With the ability to boost profitability, productivity, and safety, AI has become the proverbial linchpin enabling power plants to continue operating sustainably and adapt to disruptions in the industry.
WANT TO LEARN MORE ABOUT THE IMPACT OF BEYOND LIMITS SOLUTIONS ON UTILITIES? CHECK OUT MOREHERE.
26 FEBRUARY 2021
LOS ANGELES, CA
AI has become crucial to high-value industries, with many organizations creating entirely new departments focused on driving innovation forward and interweaving emerging technology into numerous aspects of their operations and business strategy. The AI jobs market has grown and expanded to accommodate this digital transformation, making artificial intelligence one of the most popular career fields in the world.
Beyond Limits is at the forefront of this global job market shift, hiring top-tier data scientists, AI engineers, and machine learning scientists to help better position large-scale industries for the future.
Thinking about taking the leap into the AI job market? Check out this video to learn why you should be pursuing one of the many AI-related career opportunities across numerous industries.
LEARN MORE ABOUT BEYOND LIMITS’ CAREER OPENINGS HERE
18 February 2021
Los Angeles, CA
Alyssa Cotrina, Beyond Limits
Artificial intelligence isn’t just some vague, figurative ideation contributing to movie plots anymore. It is a very real, tangible technological necessity for the majority of today’s most critical industries. To remain competitive, companies, industries, governments, and entire countries are realizing the need for enterprise-grade AI that can support them in their efforts to deliver to their bottom-lines and keep up with the ever-accelerating road of technological evolution.
Due to a lack of cultural buy-in from internal personnel, the solution’s goal is often misunderstood and thus goes unused, resulting in the inability of the technology to prove its ultimate value.
+ Challenge Identification
Unrealistic expectations exist around technology having the ability to solve a problem without that problem actually being understood before attempting to implement the technology.
+ Technology Integration
The technology is hindered from proving its ultimate value when entities seeking to implement AI don’t do their due diligence in pursuing vendors that won’t disappear after supplying the technology. Issues arise when a company is forced to move forward without access to an AI software vendor that will continue to work with the company on its digital transformation journey.
McMullen’s astute observations serve as an outline that helps us focus on some of the core issues surrounding AI implementation such as building trust in the technology, identifying a solution advanced enough to work with limited, misleading or missing data, and empowering teams to perpetuate a digital-ready, innovation-minded company culture.
The Keys to Enterprise AI Adoption
Fortunately, a majority of the challenges reported by McMullen are made trivial when the right AI solution presents itself. When tackling the issue of adoption, the leading factor comes down to picking an enterprise-grade AI company that possesses the vital keys and features necessary to facilitate implementation including:
Explainable AI is essential for detailing recommendations in a clear manner with transparent information, evidence, uncertainty, confidence and risk, which can be understood by people and interpreted by machines. All of this boils down to the most important factor in successful artificial intelligence adoption and implementation: trust. The value of an AI systems’ functionality may be measured in terms of usability that results in increased efficiency, decreased waste, or accelerated time-to-decision. The ultimate value lies in delivering everyday decision-makers with actionable intelligence so they make faster, more accurate decisions that pique their confidence.
Keeping humans in the loop via explainable AI is a vital key to developing a symbiotic relationship, grounded in trust, with the technology and fortifying confidence in its results. Explainable, glass box artificial intelligence solutions can be found in systems that provide human-understandable, traceable, and adaptable audit trails for their recommendations. All of this clarity and transparency leads to more inherent, cultural buy-in and utilization from relevant personnel.
+ Cognitive AI – The most advanced form of the technology currently on the market
Clean data is always ideal, yet often allusive. Cognitive AI systems can both consume and organize unstructured data, analytics, and other essential information. Moreover, this advanced technological innovation can work with limited, misleading, or missing data by combining the best of conventional numeric AI approaches and advanced symbolic AI techniques to deliver reasoning and intelligence that emulates human intuition.
Cognitive AI can codify expert knowledge, expertise, experience, and best practices – then disseminate that data across every level of an entire organization. This capability is instrumental to unlock the value in seemingly un-quantifiable data. Human-inspired knowledge bases empower the system to compare recommended courses of action against best practices developed by people, enabling the system to also become smarter over time as it continues learning from new data, inputs, and influences. This facilitation of increased and streamlined access to expert information that’s readily recognized by industry professionals sustains trust in the system.
“A digital-ready company culture must exist with an innovation-minded team that’s empowered to implement structural transitions kicked into high gear by the adoption of AI technology. It’s vital this culture places importance on data-driven decision-making instead of defaulting to legacy approaches. This is where your AI investment will either thrive or fall flat.” –AJ Abdallat,Forbes
Any great enterprise-grade AI software company will come with a readiness program to help both identify the main operation problem that needs to be solved as well as support that company along on its digital transformation journey. Selecting such an AI company that sticks around to ensure a seamless transition by working on the ground directly (in tandem) with the software’s users will help ensure correct implementation of the technology. AI engineers should be training everyday decision-makers on how to properly use the solution while simultaneously learning whether the UI/UX is practical for any user’s day-to-day purposes.
Cultivating a digital attitude by diving into innovative technology and including personnel at every level, while building on their abilities and defining an AI outlook at the outset, are important aspects. Involving every stakeholder with a clear angle on success will inspire implementation efforts.
“This is a very exciting time for Beyond Limits to gain such a valuable partner as The Carnrite Group. Through Carnrite’s vast network, we hope to provide valuable guidance and increase awareness of the benefits of AI in critical sectors, including boosting operational insights, improving operating conditions, and ultimately, increase adoption of this next generation technology.” –AJ Abdallat, Beyond Limits & The Carnrite Group Press Release
The Outlook for Enterprise AI Adoption
As of now AI is almost everywhere and contributing to almost everything. From energy to utilities, natural resources, healthcare, and other evolving markets, artificial intelligence solutions are spearheading the trail when it comes to companies outlining their digital transformation strategies. Adoption and implementation may seem like the most daunting challenges but partnering with the right enterprise-grade AI company is key to lighting that path forward and beyond.
WANT TO LEARN MORE ABOUT ENTERPRISE AI THAT’S EASY TO ADOPT? TAKE A LOOK AT OUR TECH HERE.
12 February 2021
Los Angeles, CA
If there’s one thing we know about artificial intelligence it’s this: AI is a disruptive innovation that has the power to make the world a better place. As cliché as it sounds, decision-makers around the world are already realizing the value of AI for resolving global issues. AI has come to mean more than just a competitive advantage for companies, but an important solution providing better healthcare for those in need, improving the state of the global climate change crisis, yielding power in underserved communities, and numerous other examples.
However, uncertainty around AI implementation remains one of the biggest obstacles the world is facing when it comes to embracing these transformative technological solutions. Some regions do exhibit higher levels of AI maturity while others exhibit different priorities when it comes to AI implementation. This is where decision-makers must recognize that AI is not a one-time magic wand but part of a continual improvement process – one that can be evolved by gaining insights from those around the world who are paving pioneering roads for successful AI solutions.
AI Around the Globe
According to the 2020 Government AI Readiness Index from Oxford Insights, the UK, Finland, Germany, and Sweden are leading Europe’s AI market. On the global scale, however, insights from The Carnegie Endowment for International Peace suggest that Europe as a whole still has much to accomplish compared to the United States, China, and Israel. Despite their position compared to their global counterparts, with their extensive resources in education and research as well as the European Commission’s efforts to elevate AI Research and Design (R&D) investment, Europe is set to focus coordinated efforts toward addressing the digital skills gap and building a framework for increasing trust in AI adoption.
+North Africa & Middle East
On a different note, The United Arab Emirates (UAE) and Israel are leading the charge in the Middle East, with the UAE specifically demonstrating massive potential in terms of AI readiness. This is, in large part, thanks to a proactive government that has prioritized AI implementation through the UAE Strategy for AI and the National Program for Artificial Intelligence created in 2017.
Despite progress made in local AI applications like natural language processing and machine learning-based systems, many countries in the Middle East and North Africa region face challenges due to poor datasets and political instability in some areas. Inhibitors to AI adoption range from uncertainty around the economic implications of AI applications to security and ethical concerns related to representation. Countries in this region must focus on implementing proper policy, data, and knowledge infrastructures to better set the foundation for AI development.
Singapore, South Korea, Japan, and China are the four countries that top the scale for AI readiness in this region. According to a Deloitte study on how countries are pursuing AI, China’s government has stated its desire to be the world’s leader in AI innovation by 2030, demonstrating its commitment to competitive digital innovation. They have poured billions worth of US dollars toward AI applications and are leading the world in annual government R&D spending at around $59 billion. AI governance is a top priority for both Singapore and China alike, with both countries going as far as establishing advisory councils to determine how to best implement responsible AI tactics, mitigate concerns, and build consumer trust.
On the other hand, South Korea and Japan are equipped with their own advantages including data availability and representativeness. With a staggering percentage of their population being internet users, not to mention widespread 5G deployment in South Korea, these two countries are poised to lead innovations in areas like autonomous vehicles, smart manufacturing, and gaming.
+ North America
In this region, both the United States and Canada are considered the top innovators – with the United States leading the world in AI readiness. Both regions have strong strategic methods for implementing AI and both have the means to do so considering the advanced technology and data infrastructure they possess. The US government has led many initiatives to implement national standards and priorities for AI development as well as regulations for how to manage the technology. Canada was also one of the first countries to release a national approach for AI when it launched the Pan-Canadian Artificial Intelligence Strategy with CIFAR in 2017.
Overall, the US and Canada foster an incredible amount of dedication towards technological advancement demonstrated through increased R&D spending and international cooperation in AI research. However, even for these unwaveringly dedicated tech giants, some challenges have yet to be faced. Canada will need to address its impeding concerns for making wrong AI-backed decisions, while the US must continue to address the pressing skill-gap and, more importantly, data security concerns in light of the Covid-19 health crisis.
Artificial intelligence is a big part of an evolving technological improvement process and innovators are progressing to find new ways to accelerate its use in the world’s most high-stake industries like energy and healthcare. As such, global decision-makers must understand the impact that an AI system can bring to global value chains in the following industries to derive the most ROI from its implementation.
The global energy value chain stands to gain a lot from employing Cognitive AI, as can be seen with Beyond Limits’ contract order through Xcell to provide the world’s first cognitive power plant in West Africa. As many as 8.7 million people in this region are currently without access to electricity, with spotty service throughout the day at best. Cognitive AI can address this pressing issue by enabling inexperienced power plant operators to make strategic decisions based on recommendations from a cognitive system that possesses and makes accessible essential knowledge.
AI has been a driving force in this industry. In fact, it has played a pivotal role in helping to address the Covid-19 pandemic. A great example of this is shown through Beyond Limits’ partnership with medical professionals to tackle some issues created by the global pandemic by developing a dynamic predictive model that allows for more accurate prognostic analysis, resource allocation, and decision making. AI can also deliver advanced solutions for patient monitoring to allow for real-time analysis of their vitals. Not to mention, it can also support in mitigating the risk of inadequate healthcare in developing countries that lack basic resources, ultimately resulting in a higher standard of care.
AI and IIoT are key components to achieving the coveted smart factory for manufacturers as well as logistics and supply chain managers alike. In fact, over 50% of respondents in an MHI Annual Industry Report declare artificial intelligence and IIoT as potentially disruptive technologies with the ability to give companies a significant competitive advantage. Organizations in the sector can leverage cyber-physical systems and AI to reduce hazardous manual labor tasks, utilize predictive maintenance analytics, and increase supply chain automation as well as optimize facility integrations.
Quality and risk assessment are some of the biggest benefits of implementing AI in financing and banking. Specifically, AI can increase visibility for bankers and credit lenders through AI algorithms to make underwriting decisions with more accuracy as well as enhance monitoring capabilities for quality and risk in credit portfolios. Through machine learning capabilities, financial institutions are better able to create more accurate predictions by pinpointing trends and analyzing data in greater depth.
+Smart City Projects
By leveraging current technologies and policies, cities have the potential to reduce their carbon emissions by 90% by 2050. Specifically, smart city projects such as those bp are undertaking in Houston and Aberdeen have the potential to make serious headway toward mitigating carbon emissions through decarbonized transport, low carbon energy and gas, and smart buildings. As a result, this paves the way for a higher quality of life via optimized costs and a cleaner industry.
Overcoming Barriers to AI Adoption
As is the case when pursuing new and uncharted opportunities, challenges will inevitably follow. According to insights from the aforementioned Deloitte study, we can identify a few major factors that inhibit many countries from fully embracing AI adoption:
+Maturity- how advanced a country’s AI infrastructure is, as well as the ability to achieve and scale impact from AI implementation
+ Urgency- how rapidly countries are adopting and implementing AI strategies
+Overall challenges- technical skills gap, cybersecurity vulnerabilities, distrust, etc.
Only 21% of overall global respondents from Deloitte’s study declared they were “seasoned” AI early adopters with the United States exhibiting the highest level of AI maturity. Additionally, 43% declared themselves as “skilled” while 36% stated they were “starters,” not yet having developed proficiency in AI strategies or implementation.
Despite varying levels of maturity, a significant percentage of respondents (63%) state that AI is critical to their company’s success and are seeking ways to advance their maturity level. Additionally, many global respondents are more likely to use AI to create a competitive advantage rather than to catch up with fellow key players, further demonstrating their intent to instigate rapid change on their AI journeys.
It’s a fact that global companies recognize the value of AI adoption. The AI infrastructure market is even expected to see a compound annual growth rate of 21% from 2021 through 2026 according to Mordor Intelligence. However, companies are still met with challenges that inhibit this adoption process.
The technical skills gap is one such challenge that many countries face. In a SnapLogic survey conducted with 300 IT leaders across the US and UK, a lack of skilled talent was cited as the number one barrier to progressing their AI initiatives. Others like Germany are concerned with AI ethics and how it can manipulate information or create falsehoods. The US and China, in particular, are concerned with cybersecurity vulnerabilities and how to combat these risks.
Artificial intelligence is constantly evolving and adapting to humans’ most critical needs in the world’s most heavyweight industries. Because of the weight and complexity in these industries, global decision-makers need an AI technology they can trust if they wish to overcome their hesitancy around adoption. Such trust is more easily built through an AI solution they can actually understand. This is where Cognitive AI comes into play. By implementing explainable systems that can emulate human reasoning to understand and resolve problems, decision-makers are better able to derive meaning from these solutions and make pivotal decisions more strategically.
AI proliferation across the globe has never been more prominent – with countries demonstrating outstanding capabilities in terms of data governance, technical advantages, investment, and building the necessary infrastructure to support AI solutions. As AI continues to grow more advanced and the application of solutions more extensive, the more that global decision-makers must partner with leading AI innovators to overcome challenges and pursue better opportunities.
As one of many pioneers in AI innovation, we’ve come to realize the need for AI and collaborative partnerships to more adequately sustain our current industries. As part of our ongoing effort to develop these partnerships, we’ve expanded our reach across the globe into APAC and MEA regions to deliver AI solutions where they’re needed most. By taking a collaborative approach to AI innovation, and working with other pioneers in the space, global decision-makers will be better prepared to incorporate these advanced solutions into their business operations and ultimately drive impact toward a transformative future.
WANT TO LEARN MORE ABOUT HOW BEYOND LIMITS’ TECHNOLOGY IS EXPANDING ACROSS THE GLOBE?
TAKE A LOOK AT ONE OF OUR EXCITING ANNOUNCEMENTS HERE.
10 February 2021
Los Angeles, CA
Accelerating Digitization in Downstream Oil & Gas with AI
In the past year, COVID-19 has jumpstarted digital transformation and AI adoption in the Oil & Gas industry, most notably in the downstream sector, where AI-led efficiency has proven critical to combat low-price markets.
This webinar, presented by Beyond Limits digital transformation experts, explains how operational efficiency is achieved in refining using AI and how explainable AI technologies are allowing operators to build trust in these cutting-edge systems.
Check out this video to learn:
How a top 5 global oil and gas operator is achieving operational efficiency in its largest refinery by implementing artificial intelligence solutions
How AI is optimizing human decision-making to drive value and accelerate the pace at which operations teams meet profitability goals
How to mitigate core concerns around AI implementation and build trust in the technology
WANT TO LEARN HOW BEYOND LIMITS AI IS MAKING AN IMPACT ON DOWNSTREAM? FOR MORE INSIGHTS CLICK HERE.
26 January 2021
Los Angeles, CA
Data Den is a thought-leadership alcove within the world of Beyond Limits where we provide an opportunity to dive into the minds of our gifted data scientists to get a better understanding of their domain. Keep reading to catch a glimpse of their essential expertise; without it, artificial intelligence wouldn’t be possible.
Let’s talk a little about a project you worked on recently. What were the biggest challenges you faced as a data scientist working on that project?
I’ve been working on a well management project for an oil and gas supermajor for a while now that involves the kind of tech that “technical people” get really excited about. Tech-centric professionals get excited about the technical challenges at the interface of science and machine learning. Though, the real challenges emerge when you try to put all the relevant pieces together to create a coherent system that intuitively executes a complicated workflow.
All data scientists have their own specific, individual tasks they’re working on; it can become really easy to fall prey to the minute details of any one of those tasks. Though, at the end of the day, everyone is creating a lot of moving pieces that need to be able to work together. It’s always fun to dig into the tech, just don’t get bogged down in it because the process isn’t just about data science. The ability to carry out effective evaluation is also about working with databases, the software team, and perhaps internet-based API’s (if the project calls for it). There are a lot of moving pieces that all need to be in sync and talking with each other in order to truly be something.
Steering into the topic of the data itself. How do you handle missing data? What techniques do you recommend? Is there a specific project, as an example, that comes to mind?
There are a lot of different methods for dealing with missing data that range from very simplistic statistical and interpolation approaches to advanced machine learning neural network techniques. With machine learning, you can utilize neural networks or autoencoders to essentially learn behavior from a group and infill those missing data sets as much as possible. You can also make use of the other analog data to which you do have access in order to try and predict what might make up the missing parts. Those are some fairly commonly implemented techniques – but everything depends on each particular application and determining the appropriate method to successfully carry out a particular project.
One thing that’s different about the projects I’ve worked on (while we do use those methods a lot) is that, at Beyond Limits, we tend to handle bulk discrepancies using knowledge-based data. So, in the attempt to supplement missing data, we tease out the models for certain behaviors and feed those to a knowledge base that then interprets them. If I don’t have a direct measurement because I don’t have enough measurement locations in a system, or I don’t know the system-state to make a model, this comes in very handy. For example, an expert with twenty years of experience can tell me – based on the signals they do have – exactly what is happening underneath, which we then embed into a knowledge base that essentially interprets other signals in the context of that knowledge to provide a more meaningful interpretation.
Can you talk a little about codifying domain expert knowledge into an AI system?
There are several different avenues we have taken in the past to go about this. Our most commonly traversed path would probably be the same we took to create one of our geological modeling solutions. Essentially, in most situations, we gather an expert’s (or a set of experts’) expertise, along with common industry knowledge captured from research papers, textbooks, etc. If the gathered information is in a nice clean state, we are able to more easily ingest it. However, that is a fairly rare occurrence. In most cases, we want to understand the problem and how existing knowledge relates to that problem. Once we can grasp that understanding we can codify the information into our proprietary IP format.
For example, with the aforementioned solution, the idea is to have the ability to model a geological environment based on a number of measurements. We would utilize high resolution one-dimensional measurements of the asset in question along with large scale low resolution, three-dimensional seismic measurements that portray the inside of the earth. Then we correlate these measurements to some hard data samples that may exist pointwise throughout some three-dimensional space. However, we may not have sufficient data to understand the kind of system we are dealing with or to determine what the actual environment looks like.
To complete the analysis we can supplement this data using scientific knowledge, like a particular physical process that’s taken place over a long period of time – we can look at systems and know that those processes occurred in a certain order, as well as their relationship to one another. How we interpret such indirect measurements is different based on the type of system we are looking at but the principles of how we incorporate knowledge to supplement the interpretation remain the same. We can build such knowledge and relationships into our knowledge base with interpretations of those indirect measurements that are consistent with the actual physical processes and theories about such a system. So, what we are essentially left with is the ability to build in the true physical relationships of a geological system while using machine learning to automatically understand and build a 3D representation of that system. We’re not just using neural networks to train; we’re actually using the expert knowledge and common principles of science to do so.
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Dr. Michael Krause is Senior Manager of AI Solutions at Beyond Limits, a pioneering artificial intelligence engineering company creating advanced software solutions that go beyond conventional AI. Michael specializes in subsurface machine learning with experience spanning major initiatives at supermajors to next-generation digital transformations at small independents. Prior to joining Beyond Limits, Michael was Director of Analytics at Tiandi Energy in Beijing, China, and later at Energective in Houston, Texas. Michael holds a Ph.D. in Energy Resources Engineering from Stanford University.