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Artificial Intelligence: A Green Chemistry Technology

Sustainability has been a buzzword for at least the last decade across all industries and even within people’s homes but it seems now, more than ever, it is actually becoming a leader in disruptive change for corporations all over the globe. We see this push in Chemistry with the increased focus on “Green Chemistry” which the Environmental Protection Agency (EPA) defines as “the design of chemical products and processes that reduce or eliminate the generation of hazardous substances.”

Green Chemistry represents a broad reach in the Chemical Industry to become more sustainable in many different facets of the word. The EPA states that corporations that create technology which, “reduces or eliminates the hazardous chemicals used to clean up environmental contaminants” are creating green chemistry technology. Per this definition, Artificial Intelligence, when applied to Chemical Manufacturing Process to reduce environmental contaminants or when used to develop materials in a less hazardous way, and many more applications, can and would be considered green chemistry technology.

ne 12 Principiu of o GREEN CHEMISTRY • Green chemistry is an approach to chemistry that aims to maximize efficiency and minimize hazardous effects on human health and the environment. While no reaction can be perfectly •green', the overall negative impact Of chemistry research and the chemical industry can be reduced by implementing the 12 Principles Of Green Chemistry wherever possible. l. WASTE PREVENTION Prioritize the prevention Of waste, rather than cleaning up and treating waste after it has been created. Plan ahead to minimize waste at every Step. 2. ATOM ECONOMY Reduce waste at the molecular level by maximizing the number Of atoms from all reagents that are incorporated into the final product. Use atom econonny to evaluate reaction efficiency. 3. LESS HAZARDOUS CHEMICAL SYNTHESIS Design chemical reactions and synthetic routes to be as safe as possible. Consider the hazards of all substances handled during the reaction, including waste. 4. DESIGNING SAFER CHEMICALS Minimize toxicity directly by molecular design. Predict and evaluate aspects such as physical properties, toxicity, and environmental fate throughout the design process. 5. SAFER SOLVENTS AUXILIARIES Choose the safest solvent available for any given step. Minimize the total amount Of solvents and auxiliary substances used, as these make up a large percentage Of the total waste created. 6. DESIGN FOR ENERGY EFFICIENCY Choose the least energy-intensive chemical route. Avoid heating and cooling, as well as pressurized and vacuum conditions (i.e. ambient temperature & pressure are optimal). 7. USE OF RENEWABLE FEEDSTOCKS Use chemicals which are made from renewable (i.e. plant-based) sources, rather than other, equivalent chemicals originating from petrochemical sources. 8. REDUCE DERIVATIVES Minimize the use Of temporary derivatives such as protecting groups. Avoid derivatives to reduce reaction steps, resources required, and waste created. 9. CATALYSIS Use catalytic instead Of stoichiometric reagents in reactions. Choose catalysts to help increase selectivity, minimize waste, and reduce reaction times and energy demands. 10. DESIGN FOR DEGRADATION Design chemicals that degrade and can be discarded easily. Ensure that both chemicals and their degradation products are not toxic, bioaccumulative, or environmentally persistent. Il. REAL-TIME POLLUTION PREVENTION Monitor chemical reactions in real-time as they occur to prevent the formation and release Of any potentially hazardous and polluting substances. 12. SAFER CHEMISTRY FOR ACCIDENT PREVENTION Choose and develop chemical procedures that are safer and Inherently minimize the risk Of accidents. Know the possible risks and assess them beforehand. Ci O COMPOUND INTEREST 2015; WWW.COMPOUNDCHEM.COM Shared under a CC Attribution-NonCommercial-NoDerivatives licence. BY NC ND
source: www.compoundchem.com

Dr. Paul Anastas, known as the “Father of Green Chemistry” and the Director of Yale’s Center for Green Chemistry & Green Engineering, has shed even further light on sustainable chemistry sharing his ideas that not only is Green Chemistry about aligning environmental and health goals but also the consideration that “Green” must extend to economic goals through new inventive and innovative change.

Dr. Yuan Yao, a rising star in the industry and a Yale Professor, has worked alongside Dr. Anastas to conduct investigative research that illustrates, “…how emerging technologies and industrial development will affect the environment,” and she also uses, “interdisciplinary approaches in industrial ecology, sustainable engineering, and machine learning to develop systems analysis tools to support engineering and policy decisions towards sustainability.”

It is Dr. Yao’s belief that, “artificial intelligence shows great potential in reducing the energy consumption and environmental footprints for the chemical industry.” And the team at Solve The Unsolvable and Elutions have to agree with her, as our lengthy experience in chemicals and beyond prove this statement to be true. 

Artificial Intelligence, and Elutions’ Maestro Platform specifically, can certainly be considered innovative and a large contributor to sustainable chemical manufacturing. Our team works tirelessly to solve previously unsolvable business quandaries in the Chemical Industry and beyond to create more sustainable business processes and practices. Through the bespoke application of Maestro AI across the entirety of the chemical manufacturing process, from procurement all the way through to the sale of the product, enterprise efficiency is taken to a new level.

Not only does Maestro cover many of the 12 Principles of Green Chemistry such as waste prevention & energy efficiency but it goes far beyond to incorporate Dr. Anastas’ ideals of taking into account economic goals, through significant operating margin increases and constant innovation within our Neural Network. Maestro maximizes benefits with continuously increasing speed, scale, and impact.

To learn more about Maestro, please contact us and to read more about our work in chemicals, and beyond, please subscribe below.

Up Next for NCW: Digitization and Chemical Manufacturing

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AI & COVID-19: Safely Back to School

As we enter September, in one of the strangest years to date, COVID-19 has infiltrated nearly every facet of our everyday lives. When people leave their homes, it is normal to grab keys, cellphone, wallet and now a mask. For most, the changes have simply become the new routine. But one of the biggest disruptors caused by COVID-19 is in the return to campus for many college students. This results in colossal questions being asked, and one extremely important question:

How can we ensure the protection of our student-body, faculty and staff braving the return to college campuses across the globe?

Recently, the World Health Organization (WHO) released that “…current evidence suggests that COVID-19 spreads between people through direct, indirect (through contaminated objects or surfaces), or close contact with infected people via mouth and nose secretions. These are released from the mouth or nose when an infected person coughs, sneezes, speaks or sings, for example.” We recognize that the spread of COVID-19 is an Airborne illness and yet, if colleges are to re-open this fall as many already have, how can we protect the integrity of the open, constructive in-person dialogue that many classrooms thrive upon? The answer exists, partially, in making the environment on campus the healthiest for all, limiting the spread of COVID-19.

According to the WHO, many reported outbreaks of COVID-19 share their occurrence in “closed settings, such as restaurants, nightclubs, places of worship or places of work where people may be shouting, talking, or singing.” Additionally, the lack of social-distancing in practice and lack of mask wearing increased the rate of the spread and we’ve seen in many States bars and restaurants closing again to contain outbreaks. One of the most important things to pay attention to regarding these outbreaks and the future spread is this statement from the WHO,

“In these outbreaks, aerosol transmission, particularly in these indoor locations where there are crowded and inadequately ventilated spaces where infected persons spend long periods of time with others, cannot be ruled out.”

The World Health Organization

Beyond wearing a mask, social distancing and lowering the capacity of contained spaces, the physical environment of the classroom and broader spaces can have a profound effect on the virus’ ability to spread. We acknowledge that this is one of the keys leading to the decreased spread of COVID-19 on the College Campuses, increasing the safety for all. In fact, we are currently working with esteemed universities to prepare their shared spaces for the return of activity on campus.

For years, our team has followed the gold star of industry standard guidelines from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and other international equivalents in our work on campuses and beyond. ASHRAE published a report in April of 2020 to rebut some false statements regarding HVAC and also put out a full statement with guidelines regarding the optimal HVAC conditions to decrease the spread of COVID-19.

In their reports, ASHRAE specifically stated the following, “Transmission of SARS-CoV-2 through the air is sufficiently likely that airborne exposure to the virus should be controlled. Changes to building operations, including the operation of heating, ventilating, and air-conditioning systems, can reduce airborne exposures.” This is not only pivotal to the re-opening of college campuses but in all industries where offices are re-opening for example, busy sales floors at large corporations or call centers where many people are talking all day long in close quarters.

In relation to universities and colleges specifically, our team works with facilities teams to automate implementation of the guidelines set by ASHRAE’s Epidemic Task Force. The new guidelines for COVID-19 are vast and the automated implementation by Maestro allows for facilities teams to focus on non-COVID-19 related projects. While Maestro’s core remains to reduce operational and energy spend, our team is passionate about making the working environment as safe as possible for students, faculty and staff to return to this fall.

With Maestro, it is possible to return to safer classrooms and offices, all while achieving significant energy and maintenance cost reduction opportunities through automated, no-cost measures across the campus, eliminating the need for direct human interaction with the University’s assets.

Contact us to learn how Maestro can make your campus a safer place to return to.

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AI & COVID-19: Optimize

To maintain operations at the onset of COVID-19, corporations took swift, unprecedented action to counteract the pandemic’s sweeping impacts on market demand, workforce, supply chain, short-term costs and liquidity. For many corporations, this involved redirecting investment from long-term initiatives to short-term remedies. For others, expediting the execution of long-term strategies was critical to survival.

Corporations that chose to accelerate, rather than delay, their digital transformation initiatives are now leveraging Artificial Intelligence to enhance employee safety and security, reduce short-term costs and improve liquidity. In the mid-term, that same investment will allow them to stabilize revenue, further reduce OPEX, prioritize CAPEX, and lay the groundwork for competitive advantage.

How will Artificial Intelligence empower corporations to build resilience and optimize performance amidst supply chain disruption?

Elutions’ highly-automated and autonomous Artificial Intelligence solution, Maestro, integrates value chain systems and infrastructure to dynamically coordinate activity and optimize net performance, improving OPEX, CAPEX, yield and revenue.

The following Artificial Intelligence use cases are paramount to building resilience and optimizing performance in industry:

  • End-to-End Operational Alignment & Planning: By overlaying and interconnecting enterprise systems, assets and processes with AI and automation, corporations gain granular visibility and foresight into enterprise-level performance and risks, and unparalleled agility in aligning operational parameters, activities and decision-making across processes, enabling otherwise unachievable improvements in operating costs, yield and revenue.

  • Dynamic Process, UCP & Yield Optimization: By enabling dynamic, system-driven performance prediction and operating parameter adjustment at each stage of production, automated Predictive Preventative Maintenance work-order creation at the asset-component level, and automated alternate control sequencing in the event of a sensor, asset or process failure, corporations autonomously optimize process efficiency and product quality while avoiding unplanned downtime, minimizing the Unit Cost of Production, maximizing effective capacity, throughput and yield, and stabilizing revenue in line with design capacity.

  • Predictive CAPEX Planning & Budgetary Prioritization: By autonomously modeling asset health, availability and use-life, business-casing the margin impact of replacement vs continued O&M, and prioritizing CAPEX projects in line with their projected impacts on safety & security, uptime and margin, corporations plan and allocate CAPEX with unparalleled precision, efficiency and flexibility, optimizing IRR.

  • Value Chain Visibility, Scenario Modeling & Strategy Execution: By overlaying and interconnecting value chain systems and processes with AI, corporations gain unprecedented visibility and foresight into value chain performance and risks, certainty in strategic planning via scenario modeling, and oversight in execution, altogether allowing corporations to proactively manage rather than reactively respond to value chain opportunities and disruption.

Beyond mid-term resilience and performance optimization, corporations have placed their bets on Artificial Intelligence to thrive in the long-term. Stay tuned for more market intelligence on how AI is helping corporations gain competitive advantage.

Up Next for NCW: Digitization and Chemical Manufacturing

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Virtual CogX: The Festival of AI & Breakthrough Technology

Elutions is proud to announce their Lead Partnership of The CogX Global Leadership Summit and Festival of AI & Breakthrough Technology, June 8th- June 10th. In a world that is constantly in flux to adapt to impacts of COVID-19, CogX made the difficult decision to virtualize their event. We fully align with this decision as they themselves said,

“There are important, and in some cases urgent, topics to discuss: both the immediate challenges presented by COVID-19, and to play our part in helping restart the economy by connecting, collaborating and supporting each other.”

Relevant, now more than ever, is CogX’s 2020 Event Theme- How Do we Get the Next 10 years Right? Outlined below are the ways in which the conference aims to address this massive question:

– Move the conversation forward with concrete actions

– Reframe the climate emergency as the biggest economic opportunity in the last 200 years

–Increase understanding of the current Covid-19 pandemic and champion innovative solutions

The global pandemic has allowed many of us, corporations and individuals, to pause and re-evaluate what the next ten years will look like. It is clear now that the businesses that successfully and swiftly adopt to automated, autonomous applications of AI and rethink their business models will be the ones realizing a competitive advantage.

We will be hosting a virtual expo booth across the three days, June 8th through June 10th, where attendees can reach out to our team to learn more. We will also be hosting virtual lunch and learns, happy hours, and coffee chats. Our Managing Director of EMEA, Jamie Devlin, will also be a featured speaker on Industry 4.0 and sustainable supply chain, Monday, June 8th, 5pm BST (12pm EST).

For a limited time, we are offering our readers, interested in attending our speaking events, the chance to receive a Gold Pass, free of charge. Please click here and provide your full name, title at your company, email address and phone number to receive a Gold Pass.

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College and University Campuses can Combat COVID-19

It’s graduation season around the world and for what seems like the first time, graduation will not commence per usual. The celebratory hustle and bustle of college campuses at this time of year is now replaced with empty classrooms, haunted hallways and desperate hopes that students will return in the Fall. The truth is, nobody knows what will happen in the Fall semester, but students aren’t the only ones feeling the immediate impacts of campus closures. Colleges and Universities are now scrambling to address not only the concerns of parents, students, and staff but also the significant impacts on endowments, revenues and their budgets going forward.

An Inside Higher Ed survey of 172 campus leaders shows that the number one concern remains bringing the students back to campus safely and continuing their high standards for education off campus. However, the financial focus is right up there, “more presidents citing a desire for financial health and operational planning support (60 percent) than for anything else.” Artificial Intelligence can address operational concerns while freeing up financial resources for other concerns such as faculty training and instructional technology.

Source: Inside Higher Ed

While revenues are experiencing immediate impact, the need to protect buildings and assets, ensure environmental compliance is met, and reduce building baseloads becomes more critical as staff and services are less present. After all, students need a campus to return to. Artificial Intelligence, and Maestro specifically, is primed to not only address these impacts in the immediate term but provide long-term stability and digital transformation.

At an operational level, buildings have been closed but the need to maintain them to prevent asset degradation and compliance requirements still need to be met. With fewer people onsite to identify and manage issues and IT stretched with online learning, now more than ever AI and autonomous or remote adoption of solutions need to be applied. At this point, it is likely that there are already significant maintenance backlogs, which AI can address and mitigate future negative impacts of.

The short-term, mid-term and long-term impacts can all be addressed through Maestro artificial intelligence in the following ways:

Short Term: Reduce costs, ensure compliance and protect assets through improved approach to managing assets with limited onsite staff. Optimize baseloads. Identify and prioritize critical failures. Iron out inconsistencies in data. Move to PPM.

Mid Term: Significant cost savings in resource efficiency, maintenance, increase asset life use, reduced capital expenditure.

Long Term: Enhanced scenario modelling for space optimization and asset acquisition ROI assessments.

Benefits like this can be achieved through Maestro’s artificial intelligence and neural network computing capabilities to target significant energy and maintenance cost reduction opportunities through automated, no-cost measures across the campus, eliminating the need for direct human interaction with the on campus assets. A run-rate to deliver a benefit of >$20m annually could easily be achieved in as little as 60 days through a rapid deployment of the technology and the prioritization of the highest energy cost consuming facilities.

In an effort to focus on creating a stable environment for students to come back to, Higher Education Institutions must turn to AI as a remedy for immediate negative financial and operational impacts caused by COVID-19. To learn more about how AI can turn your business operations around, stay tuned for our COVID-19 & AI series or contact our team today to learn more.

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AI & COVID-19: Stabilize & Survive

In the midst of the COVID-19 pandemic, why are corporations accelerating rather than delaying their digital transformation initiatives? The pandemic’s unprecedented impact on demand, workforce, supply chain, short-term costs and liquidity has forced corporations to take immediate, strategic action to maintain operations. In exploring their means of stabilization and survival, corporations found that Artificial Intelligence is uniquely positioned to ensure business continuity in the short-term, with its proven value in improving employee safety & security, short-term costs and liquidity.

While no industry has proven immune to the pandemic, the depth and breadth of its disruption in the manufacturing vertical clearly demonstrates why corporations have adopted transformational technologies over traditional solutions to beat COVID-19. Manufacturers have felt the acute pressure of COVID-19 in their plants and across their value chains.

  • Demand: Sharp declines in demand across non-essential product segments have disrupted each step of the value chain, driving significant earnings adjustments; essential goods producers are struggling to continue operations with heightened risk, and to satiate demand with unprecedented operating and supply chain limitations.
  • Supply Chain: Despite the geographic diversity of supplier networks, sudden overseas supplier shutdowns and domestic fulfillment delays have disrupted if not halted downstream activity, depleting on-hand inventory, prompting a rapid search for market alternatives, and driving material and part shortages, price increases, and an expected spike in upstream transportation costs as restrictions lift.
  • Workforce: Rapid adoption of new distancing protocols, shift structures and offsite resourcing arrangements have ensured employee safety & security, but constrained operating efficiency, quality, throughput and yield; deferred critical asset maintenance and replacement have increased downtime risk and may increase mid-term CAPEX obligations.
  • Short-Term Costs: Essential and non-essential product manufacturers have made significant adjustments to minimize variable costs, maintain business operations, and operate in an environment of significant macroeconomic and trade policy uncertainty, including layoffs, furloughs and temporary plant closures.
  • Liquidity: With strong macroeconomic headwinds, demand and supply side disruption, and constrained operational agility, liquidity is a principal concern of manufacturers that will not be alleviated at the moment restrictions are lifted, but gradually as the supply chain and broader economy rebound.

How is Artificial Intelligence empowering corporations with substantial value chain disruption to act fast and weather the storm?

Elutions’ highly-automated and autonomous Artificial Intelligence solution, Maestro, leverages historical data, deploys rapidly and delivers immediate improvements in safety & security, short-term costs and liquidity. The following Artificial Intelligence use cases are paramount to ensuring business continuity in manufacturing.

  • Remote Operability, Operational Automation & BEP Adherence: By enabling remote asset and process control, visualization and planning, and automated system-driven asset and process optimization, corporations ensure operational continuity, resilience and efficiency, improving employee safety & security, reducing labor requirements and operating costs, and increasing cash-on-hand.
  • In-Line Quality Assurance & Scrap Reduction: By enabling automated quality prediction and dynamic operating parameter adjustment at each stage of production, manufacturers autonomously ensure end-product quality, minimize scrap and rework, and optimize the Unit Cost of Production as conditions change, improving operating margins, yield (revenue), and liquidity through minimized waste.
  • Dynamic Downtime Prevention & Predictive Preventative Maintenance: By enabling dynamic, system-driven alternate control sequencing in the event of a sensor, asset or process failure, corporations autonomously avoid unplanned downtime, associated repair and replacement costs, and foregone revenue. With system-driven Predictive Preventative Maintenance and automated Work Order creation, corporations minimize downtime and production risk, extend asset use life, generate significant savings through right-time, right-size maintenance, and improve safety & security through truck-roll consolidation.
  • Market, Demand & Capacity Driven Procurement & Inventory Management: By continuously predicting raw material costs, sales and capacity alongside the live environment, and enabling automated procurement and IM directives, corporations optimize operating productivity, inventory levels, and the margin value of Inventory on-hand. Similarly, by continuously predicting replacement part costs, process demand alongside forecasted utilization, and automating replacement part purchasing, corporations optimize part inventory levels, reduce short-term costs and improve liquidity.

Beyond short-term survival, corporations have placed their bets on Artificial Intelligence to thrive in the mid-to-long-term. Stay tuned for more market intelligence on how AI is helping corporations build resilience and optimize operations.

Up Next for NCW: Digitization and Chemical Manufacturing

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Why In-House Artificial Intelligence Projects Fail

Companies all over the world, from giant corporations to start-ups, are keen to cash in on the vast value of Artificial Intelligence. With an intent to capture as much of that value as possible, many spend millions on in-house AI solution development rather than outsourcing to address their most critical business challenges. In a world where there’s a feasible DIY solution for almost everything, Artificial Intelligence is most often the outlier. The complexity and cost of AI solution development demands experience to reduce financial risk and ensure speed to benefit, especially in highly competitive sectors.

Think of your business as a human body, and your business challenges as illnesses of varying severity. Some challenges, like some illnesses, are treatable with over the counter medications, while others require a visit to the doctor, prescriptions, long-term treatment or intensive care. If you had an illness that required extensive medical attention, you wouldn’t hesitate to seek out the best medical team for treatment. Should you treat your business any differently?

In deciding whether to go in-house or outsource, it is important to consider how a strategic AI implementation will impact your business. If it’s done right, it will reduce costs, increase revenue and enhance competitive advantage. If it’s done wrong, to what extent is your business at risk?

Financial opportunity from AI abounds across sectors (see the figure below), and there is both margin opportunity and market share on the table for the businesses that harness AI to tackle their strategic challenges first. In other words, getting your AI implementation done fast and right matters, and you must weigh your decision to go in-house or outsource accordingly.

Artificial intelligence (Al) has the potential to create value across sectors. Al impact, $ billion 700 500 400 Healthcare systems and services Public and social sectors 300 Advanced electronics/ semiconductors Retail Transport and logistics Travel Consumer packaged goods Automotive and assembly Banking Basic materials Insurance Media and entertainment High tech O Oil and gas 100 20 Telecommunications Pharmaceuticals and medical products 30 Chemicals Agriculture Aerospace and defense 40 50 60 McKinsey&Company Share of Al impact in total impact derived from analytics, % Source: McKinsey Global Institute analysis

With competitive advantage on the line and the clock ticking, corporations place their bets on whether to navigate the AI journey alone or with partners. Instinctively, they are hesitant to collaborate, tantalized by the prospect of minimizing solution costs, while building their own innovative capacity and owning the resulting IP outright.

Logically, they then look to market outcomes and learn why in-house AI solution development efforts fail more often than not, even in the Fortune100 and at tech companies.

Without experience developing and delivering AI solutions, many corporations fail understand the costs, resources, processes, stakeholders, and even the objectives involved from the onset. As a result, in-house projects often lack a clear and viable design and delivery strategy, roadmap and KPIs, dramatically reducing the speed to benefit if not inhibiting benefit delivery altogether. Program costs and timelines become a driving force for failure. With little transparency into which aspects of the solution will drive the most value, there is no clear way to prioritize spending. Costs either spiral out of control, or corners are consecutively cut in design, development, testing and delivery, resulting in piecemeal solutions that impair data quality, promote bias and diminish solution accuracy, functionality, utility and outcomes.

More often than not, successful AI adopters partner with proven providers on a combination of off-the-shelf solution tailoring, ground-up solution design and solution delivery. How do they decide to partner rather than go it alone?

First, they recognize the competitive imperative for AI—the opportunity cost of following rather than leading in their market—along with the direct costs of failure, and their lack of in-house knowledge and experience with AI solution design and delivery. Second, they find a provider with a successful track record in similar or analogous environments. Third, they develop trust with that provider by laying the groundwork for a happy marriage in contracting. Then they see it through. From the leadership level-down, they commit to the partnership and collaborate from end-to-end to ensure project success.

Strategic AI implementations are broad in scope, capturing data and impacting activities across corporate ecosystems. This complexity is readily apparent in industry, where AI not only provides data-driven direction for decision-making at every step of the value chain and in every organizational department, but directly informs control and automation strategies in production, testing, packaging, distribution and even purchasing.

In recognition of the immense value and complexity of AI in industry, and the competitive need for speed in adoption, the World Economic Forum, in collaboration with McKinsey & Company, has published a toolkit of “practical recommendations” for industrials to accelerate their AI journey at scale. Appearing in The Next Economic Growth Engine Scaling Fourth Industrial Revolution Technologies in Production, this toolkit advocates the adoption of proven AI solutions and related technologies through a partnership and acquisition approach rather than in-house development.

Figure 6: Industry toolkit for accelerating adoption of technology Value delivery engine Intelligence • Predictive maintenance • Machine learning-supported, root-cause problem-solving for quality claims Connectivity Augmented reality-guided assembly operations Real-time IOT-based performance management Flexible automation • Robots to automate challenging tasks • Real-time product release 39 high-impact digital applications ready for deployment Scale-up engine Mobilize Mobilize the Organization Strategize Set the vision and the value to capture Innovate Spark innovation by demonstrating the value at stake Scale up Capture full value & Company, Fourth in With the World

With more and more data available for exploit across industries, the opportunities for its monetization through AI are greater and increasingly complex. So too is the risk of getting your implementation wrong.

Can your business afford the DIY approach?

Challenge us to solve your unsolvable business quandaries.

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Enterprise Artificial Intelligence – Academic Theory or Ready for Primetime?

Solvetheunsolvable has already explored various aspects of consumer AI and products purporting to leverage AI technologies, but is AI for Enterprise ready for primetime?

Investors aren’t the only ones betting big on Artificial Intelligence, it turns out Higher Education is also investing heavily into the space. With heavy investment in research and development, enterprise level AI seems to be having a rocky start.  Earlier this month Northeastern University allocated $50 million to an Institute for Experiential Artificial Intelligence. This institute will be dedicated to uniting leading experts to solve the world’s unsolvable problems.

“This new institute, the first of its kind, will focus on enabling artificial intelligence and humans to collaborate interactively around solving problems in health, security, and sustainability. We believe that the true promise of AI lies not in its ability to replace humans, but to optimize what humans do best.”


Northeastern President Joseph E. Aoun

This isn’t Northeastern’s first step into the world of Artificial Intelligence and Automation. They already have an Institute for Experiential Robotics that is bringing together engineers, sociologists and other experts, including economists, to design and build robots with abilities to learn and execute human behaviors.  Northeastern isn’t just building Institutes for experts to conduct research, they are making it a priority to prepare their students for success in the age of artificial intelligence. They have an entire curriculum dedicated to what they call, humanics which is a key part of their strategic plan, Northeastern 2025.

Northeastern 2025 Promo Video

“We are building on substantial strengths across all colleges in the university,” said Carla Brodley, dean of the Khoury College of Computer Sciences. “Experiential AI is highly relevant to our mission.”

Though Northeastern is an example of one university betting heavily on AI, they are not alone in their quest to equip students with proper education for the AI-enabled future. In fact, government agencies are getting involved in funding AI in Education. The UK has pledged to invest £400 million in math, digital and technical education through the government’s AI sector deal to protect Britain’s technology sector amid Brexit and an additional £13 million for postgraduate education on AI. In the US, just a few days ago, the National Science Foundation announced a joint federal program to fund research focused on artificial intelligence at colleges, universities and nonprofit or nonacademic organizations focused on educational or research activities. 

The National Science Foundation is awarding $120 million to fund planning grants and support up to six institutes, but there’s a catch. Each institute must have a principal focus on at least one of six themes:

  • Trustworthy AI
  • Foundations of Machine Learning
  • AI-Driven Innovation in Agriculture and the Food System
  • AI-Augmented Learning
  • AI for Accelerating Molecular Synthesis and Manufacturing
  • AI for Discovery in Physics

As universities and governments bet big on the future of AI and education, it underscores the importance of AI on a global scale in the future, but does it call into question the current existence of AI solutions ready to take business to the next level? Utilizing AI and automation will be imperative for corporations to remain competitive and for the advancement of business, but when will the floodgates be swept open, and by who, remains a mystery.

Will you leap into the future and embrace AI now? How do you see the futuristic vision of enterprise AI transform your business? Challenge Solvetheunsolvable with your business conundrum or leave your thoughts in the comments below and let’s explore what AI can do for you. 

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Data Bias on the Daily: Criminal Sentencing- Not all algorithms are created equal

Imagine This…. You’ve been convicted of a non-violent crime, say petty theft. Your legal team decides the best course of action is to take a plea deal. On the day of your sentencing, the judge rejects your plea deal and doubles your sentence. Why? An algorithm says that you are at high risk for violent crime in the future…

You may be reading this thinking, that can’t possibly be real? But that is an all too real scenario because of the COMPAS algorithm.

COMPAS, an acronym for Correctional Offender Management Profiling for Alternative Sanctions, is a case management and decision support tool used by U.S. courts to assess the likelihood of a defendant becoming a repeat offender.

The problem with COMPAS, as a ProPublica report states, “Only 20 percent of the people predicted to commit violent crimes actually went on to do so.” ProPublica also concluded that the algorithm was twice as likely to falsely flag black defendants as future criminals as it was to falsely flag white defendants. And therein lies the problem, the algorithm has inherently biased training data due to years of human bias in the courtroom.

COMPAS is not only biased racially, but it also has bias against age and gender. An independent study done by researchers at Cornell University and Microsoft found that because most of the training data for COMPAS was based on male offenders the model is not as good at distinguishing between male and female as it could be. They even decided to make a separate COMPAS model aimed specifically at recidivism risk prediction for women.

But why would COMPAS separate the data based solely on gender when COMPAS has also shown to have racial bias? Why are judicial systems still turning to private, for-profit, companies whose algorithms are known to support racial, age and gender bias?

Turning to these types of algorithms have long standing implications on human life and our judicial system. Criminals receiving their sentences in the early ages of algorithmic adoption should not be test samples or guinea pigs for faulty and biased algorithms. As Artificial Intelligence becomes more main stream, understanding the data sets and training methodologies is key to understanding the results – how is data bias affecting your daily life?

For more information on COMPAS and ProPublica’s report, please click here.

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Artificial Intelligence… A Buzz Word?

It’s a super-computer. It’s technology.  It’s a Computer Brain.  It’s a….Buzz Word?

Artificial Intelligence or “AI” is all the rage in consumer goods and services, and analysts say it will change the face of business forever, but what exactly is “Artificial Intelligence”. The market has failed to define what this ambiguous buzz word means, but yet investors are readily throwing billions of dollars at companies that claim to be “AI- Focused”.  According to the Financial Times,

“Companies branded as AI businesses have historically raised larger funding rounds and secured higher valuations than other software businesses. The median funding round for an AI start-up last year was about 15 per cent higher than for a software start-up.”

Financial Times

And yet investors struggle to understand what Artificial Intelligence means.  Is it the technology?  Is it the application?  Is it the result?

Artificial Intelligence has become a catch-all phrase for various types of computer and data science technologies and applications aimed at automating work and aiding in decision making.   Articles on the proliferation of AI in the enterprise market speak to the vast potential use cases, the efficiencies, the streamlined customer experience, but fail to define the technology. Instead, authors caveat their prophecies with statements which undermine the entire industry, “Granted, not all AI systems are alike. Some of them are relatively ‘dumb,’ because they use pre-determined inputs and outputs”.  Enterprise organizations have been leveraging “If/Then” logic programming for decades to optimize their operations, from PLCs on the manufacturing floor, to excel sheets in the board room or sales office.  Is a simple system that has pre-determined inputs and outputs anything more than a logic sequence? 

In truth, Artificial Intelligence is a combination of complex technologies which together have the power to change the way we do business, but how many companies have successfully developed the advanced integrated technology to deliver this seismic shift in enterprise value?  True Artificial Intelligence requires much more than a logic sequence – from system agnostic data connectivity, neural networking, autonomous data-cleansing, to machine learning, automated root cause analysis and continuously learning autonomous decision making and self-scripting technologies – the technology that will transform enterprise operations and ultimately, organizational value, has arrived, but true Artificial Intelligence is far less prevalent than a market summary would lead you to believe…