The Rise of AI: Shaping Industries Through Intelligent Innovation

THE RISE OF AI:

FOREWORD

TOM KELLY EXECUTIVE DIRECTOR & CEO AUTOMATION ALLEY

Is this foreword written by a human, and does it even matter? This is the question everyone must answer now that open-source artificial intelligence (AI) has catapulted onto the world’s main stage.

We are entering a new era of AI technology in our everyday lives, from the media we consume to the products we use. But there are also challenges — fears of job displacement, academic plagiarism, deep fakes, cyber threats and a new degree of misinformation.

As industry stakeholders, we need to be vigilant of bad actors in AI, and participate hand-in-hand with the government to craft policy that will limit negative incursions while providing companies with the flexibility to invent and capitalize on these new methods.

We must also take on the responsibility as leadership to train our workforce in these new skills so they may deploy them in a safe and constructive way. It is a human condition to fear what we don’t understand. Let us be inspired by former advances in technology that changed the world. Photography did not kill the art of painting. The vinyl record did not kill the art of live music. AI will not kill the art of communication and production. Instead, it will provide a way to augment production and creativity to new heights.

CHRIS HEIDEN

ASSOCIATE PROFESSOR, INFORMATION TECHNOLOGY AND DECISION SCIENCES

COLLEGE

However, before that happens, we must do the work to make it so. It starts with defining how we believe AI should be regulated collaboratively. The Biden Administration already laid some initial groundwork in this regard. Moving forward, how can this policy be improved and what are the blind spots?

Another topic is defining what AI training and literacy looks like in the workforce. Is it as simple as knowing how to use a Large Language Model, or is it now a necessity to understand exactly how the technology functions?

Lastly, how can we deploy AI to address areas of need? Can AI be the answer to propping up lagging test scores for students? Can AI lead to the next breakthrough in disease treatment or prevention? Can AI be the answer to helping small businesses navigate new market realities? Can AI transform the manufacturing process from the shop floor to the top floor?

Let’s answer these questions together. Automation Alley is proud to bring forward leading voices in academia, government and industry to tackle the most pressing issues of our time. We look forward to serving you as a trusted source for information and insight.

WALSH

TOM KELLY EXECUTIVE DIRECTOR & CEO AUTOMATION ALLEY

RUSSEL ZARRAS

LEAD BUSINESS DEVELOPER AND TECHBRIDGE PROGRAM DIRECTOR FRAUNHOFER, USA INC

MO ABUALI DIRECTOR WIPFLI

ARJUN DHAKE PRESIDENT & CEO DHAKE INDUSTRIES

BOB AXTMAN DIRECTOR, MARKETING & SALES EXHIBIT EXCHANGE

PAVAN MUZUMDAR

COO, AUTOMATION ALLEY CEO, PROJECT DIAMOnD

CHRIS HEIDEN

ASSOCIATE PROFESSOR - ITDS WALSH COLLEGE

EMERSON KROLWESKI ECONOMIC DEVELOPMENT SPECIALIST MACOMB COUNTY GOVERNMENT

STEPHANIE WRIGHT

CHIEF OPERATING OFFICER US CENTER FOR ADVANCED MANUFACTURING

BRIAN BREUHAN

GLOBAL MANUFACTURING OPTIMIZATION STRATAGIST GENERAL MOTORS

CODY SCHAUB ECONOMIC DEVELOPMENT COORDINATOR THE OFFICE OF CONGRESSWOMAN HALEY STEVENS

DON SWANSON VICE PRESIDENT, SALES AMERICAS ROSS CONTROLS

GARY KRUS VICE PRESIDENT OF BUSINESS DEVELOPMENT AND OPERATION

SEBASTIAN WICKLEIN

DIRECTOR, BUSINESS DEVELOPMENT COORDINATION FRAUNHOFER, USA INC

COURTNEY STEELE DIRECTOR OF MARKETING & COMMUNICATIONS DEEPHOW

CHARLES

GAURAV AGRAWAL FOUNDER & CEO

LINUS L. DROGS III PRESIDENT/CEO AU ENTERPRISES, INC

KLAUS BADEN TECHNOLOGY SOLUTION CONSULTANT FRAUNHOFER, USA INC

ASHRAF SALEEM ASSISTANT PROFESSOR IN MECHATRONICS ENGINEERING MICHIGAN TECHNOLOGICAL UNIVERSITY

TOM SWARTZ

CHIEF TECHNOLOGY OFFICER DETROIT MANUFACTURING SYSTEMS

NOT PICTURED: EDWARD CLEMENTE

SR. ADVISOR FOR TRENDS AND DEVELOPMENT, MEDC

By Sebastian Wicklein, Director, Business Development Coordination

Unleashing the Power of Artificial Intelligence Across Industries: A Transformative Frontier EXPERT INSIGHTS:

The progression towards "Industry 4.0," the fourth industrial revolution (4IR), is heavily dependent on artificial intelligence (AI), IoT, and real-time data accessibility. This transformative shift elevates the significance of digital technology from previous pilot applications to an industrial-grade level, which enables a holistic approach to manufacturing and theoretically furnishing business managers with deeper insights into every facet of their operations, in real-time, unlocking increased efficiency and flexibility with significant quality enhancements and the optimization of complex processes with data-driven decision making.

So far so good. At the same time, this paradigm shift presents business managers with apparent investment

risks, trepidations, and impediments to implementing this technology successfully, awarding them with sleepless nights and exposing them to executive management having to defend their decisions and actions. What about the competition?

Indian companies are developing the roadmap to adopt digital transformation with 54% of them implementing analytics and AI for business functions, according to a survey by the professional services firm PwC India.

Deloitte stated in its survey report about the adoption of AI in manufacturing that 93% of companies believe AI will be a crucial technology for innovation.

IBM 2022 Global AI Adoption Index report stated that 1 in 4 companies are adopting artificial intelligence solutions due to labor and skill shortage.

According to McKinsey, by 2030, the manufacturing industry with the penetration of AI will contribute almost 19% to the economic growth of China.

What needs to happen?

Our belief is that, to master these challenges and to seize the full opportunity of this paradigm shift, leading manufacturing businesses and such businesses who strive to become leaders have to engage in Innovation-Partnerships with trusted R&D organizations to jointly develop the AI-technology ecosystem in accordance with reliable business models and advance it to industrial grade.

It is essential for the industry partners to invest in the upscaling of dedicated computing capacities and resources and for the R&D partner organizations to establish committed and robust AI-technology advancement programs with the overall objective to develop innovative but regulated and dependable technology.

To pave the way for a robust and regulated industry standard, it will be key to introduce technologies that were independently validated and verified. Scalability and transferability are another important aspect of a trusted and robust technology. The R&D organizations can engage in experimentation under the guidance of the industry towards a business case to ensure a swift adoption of the technology and transfer from lab to operations.

While there is unwarranted fear of the workforce to be replaced as a result of this paradigm shift, it offers an opportunity for employees and employers to engage in upskilling activities of the existing workforce and entering the knowledge workforce as compared to manual labor. The innovation partnership between industry and R&D organization offers a convenient avenue to effectively educate and upskill the workers for the designated implementation of the technology.

A whole new range of jobs will be created because of AI-technology. For example, large language models will need AI model and prompt engineers,

AI is not a piece of software or hardware that can be acquired, installed, and operated in a one-step decision. It is a way of utilizing data generated in the process of operating a business.

interface and interaction designers, AI content creators, data curators and trainers, and ethics and governance specialists.

When it comes to the introduction of AI capabilities in manufacturing, the complexity of the AI-technology itself and the complexity of the integration into the existing operations environment in compatibility with current equipment is a considerable challenge that bears many risks.

To de-risk this process and enable customization for each operation, the technology transfer from TD-Lab through an R&D organization to the shop floor is a sustainable option.

AI is not a piece of software or hardware that can be acquired, installed, and operated in a one-step decision. It is a way of utilizing data generated in the process of operating a business. Of course, new generations of fundamental manufacturing equipment will come equipped with AI capabilities which are customizable to a certain degree. But to truly unlock the capabilities of AI and move ahead of the competition, it is necessary to have a designated AI-task force team working hand-in-hand with the technology developers to ensure that the full AI-potential is deployed.

How to get started?

Most important is the assessment of data-readiness of your operation, which is the ability and handle large amounts of data and to identify sources of data (e.g., sensors, systems, and databases) and ensure that the data is accessible, standardized, and suitable for generating AI-models.

Additionally, it is essential to form cross-functional task-force teams to combine the expertise portfolio of manufacturing operations, data science, AI development, and IT infrastructure. Such task-force teams can most efficiently interface between the business and the partnering R&D organization.

Clearly define what type of AI-technology you are pursuing and why and to what purpose. Examples include but are not limited to machine learning, digital twinning, computer vision and image analysis, natural language processing, predictive maintenance, robotics, autonomous systems, and operations.

Pick designated pilot projects that are scalable and transferable across the operation and operation sites which are tailored to enable data collection and preparation and model development and training.

Hand-in-hand deployment with your R&D-partner allows for de-risking this process and keeping cost low by avoiding costly mistakes and timeconsuming detours.

Involve employees in this process from the start and engage in training and change management processes before the technology is deployed. Your R&D-partner can help with this process considerably.

THE RISE OF AI:

Shaping Industries Through Intelligent Innovation

Artificial Intelligence is the most disruptive innovation of our lifetime. The rapid transition from science fiction to reality of technology levies many challenges to the status quo. Everything, from business to communication to education, will be impacted to some degree.

Join us as we discuss the burgeoning field of Artificial Intelligence (AI) and its implications for industry stakeholders, business owners, and employees alike.

Defining AI Regulation

There is no doubt about the fact that Artificial Intelligence is powerful. Despite its popular fame rising only within the last year, people have found an abundance of ways to incorporate open-source AI into their everyday lives. From writing workflows and graphic design to self-driving cars and facial recognition technology — AI is all around us.

This integration makes AI a force for progress, but also a force for bad actors. As we continue to advance in technology and integrate AI into more aspects of our lives, it is important to consider the implications and potential consequences. One of the biggest challenges to mass deployment is ensuring ethical and responsible implementation. Concerns about data privacy, bias in algorithms, and potential job displacement are all very valid as generative AI was trained on human input.

The world has been slow to develop policies around AI so far. The biggest challenge lies in pinning down the exact technology being regulated; AI and machine learning have been in development for years, but it has only recently become advanced and mainstream enough to have consequences for society at large.

The European Union became one of the first authorities in the world to implement a comprehensive legal framework for the development and use of AI, what it calls the 'AI Act', in 2023. It introduces dedicated rules companies must follow when training models, as well as criteria to classify projects based on the level of risk they pose.

As for the United States, government officials are slowly but surely taking action. In 2023, a group of Senators from both sides of the political aisle came together to propose a bill for the creation of a commission focused on the regulation of artificial intelligence. Prior to that, the Biden Administration released The Blueprint for an AI Bill of Rights, a document that outlines principles for responsible AI development and use.

“Should we regulate in a national or global perspective? With 50 states, things could get messy,” said Chris Heiden, Walsh College Associate Professor of Business Information Technology. “What are the ethical implications? Will there be regulation lag? We need to educate legislators.”

The need for established ethical boundaries and regulation is becoming more apparent as bad actors capitalize on this new technology. Recently, a person utilizing multiple AI deep fakes of board members at a fake company board meeting convinced a finance worker to wire $25 million to a fraudulent account. Early reports of AI mimicking voices of loved ones also caught the attention of the Missouri Department Of Health and Senior Services, which issued a bulletin warning seniors of AI scams on the rise.

“We need people to understand what they are looking at, understand what is real, what is not and what is AI generated,” said Courtney Steele, Director of Marketing and Communications at DeepHow, an AI-startup. “We need to identify and internalize those hallmarks and train the next generation to identify those as well since they are growing up in an AI world.”

The rise of AI for insidious purposes caught the attention of The Bulletin of the Atomic Scientists, keepers of the fabled “Doomsday Clock” that has famously ticked down to world annihilation (midnight) since 1947. In January 2024, the Doomsday Clock was moved up to 90 seconds to midnight in response to unregulated AI and nuclear threats.

In industry, most companies are self-regulating their workforce with this technology.

“Some companies are using a private environment for their ChatGPT applications so that IP can stay within an organization and not escape,” said Brian Breuhan, Global Manufacturing Optimization Strategist, General Motors. “At the same time, you need a code of ethics put in place so your employees will not use an open AI to save or distribute that data or information.”

Business, academia, and government will need to collaborate to provide insight in the creation of fast-acting regulation in the face of this rapid advancement.

“Everybody needs to be a data scientist if they want to deal with AI.”

- Sebastian Wicklein, Fraunhofer USA

Training Workforce with AI

AI is arguably the biggest disruption to happen to the labor force since industrialization. At no other point have people's roles at work faced such a prospect of change. Like how assembly lines removed the need for large-scale manual labor, AI promises to do the same with many of the data-oriented tasks performed by white-collar workers more accustomed to Excel spreadsheets than a drill press station. This will require a mindset shift.

“Everybody needs to be a data scientist if they want to deal with AI,” said Sebastian Wicklein, Fraunhofer USA Director of Business Development and R&D Coordination. “It does not mean you need to be a hardcore scientist sitting in front of a computer crunching data, but it does mean you need to look at data from a different angle. Everything now is data based, and we make data-based decisions.”

That does not necessarily mean people will lose their jobs. Employers see AI as more of an opportunity than anything else. According to a worldwide 2023 survey of 1,000 organizations by the Capgemini Research Institute, 69% of international executives believe AI will lead to the creation of more jobs. Being a non-sentient technology, AI will always require human management to deliver value.

“The question now is how do you bring this together so everyone in your organization can be a data scientist? You cannot have people that just push buttons. Everyone must have the mindset ‘I can handle data, I know what it is for, I know where it comes from, and I know what to do with it,’ ” Wicklein added.

In manufacturing specifically, companies are already using AI to identify faulty products, reduce waste, and streamline processes. With this,

workflows are becoming more efficient, and workers have more time to focus on higher-level tasks.

Early studies are reflecting this: According to the 2023 OECD Employment Outlook, 63% of manufacturing and finance workforce survey respondents said the deployment of AI improved enjoyment of their job, with 80% of manufacturing respondents saying AI tools improved their job performance. Additionally, 49% of employers responded that advancements in AI made them hire new workers. A separate study by the Pew Research Center found 32% of IT (Information Technology) workers say AI will help more than hurt them, compared with 11% who say it will hurt more than it helps.

“Many people do not understand AI and have a fear of losing their jobs,” Heiden said. “We have seen this with other adoptions of technology in the past like automation and robotics. We need to educate managers and line workers to make them see we are not replacing them, but taking those mundane mechanical tasks away so they may pursue something with more creativity.”

As more companies incorporate AI into their production processes, there will be an increased need for workers who can understand and work with the technology. This means a shift in the job market, where there will be a higher demand for individuals with skills such as data analysis, programming, and machine learning over traditional labor positions.

Recent market advancements in AI/ML computing power and sophistication should prompt all manufacturers to take a renewed look into how this technology can optimize their workflows.

“We need subsidies and incentives for workforce training. We need to incentivize people to be willing to upskill and give them an option that is readily available,” Wicklein said. “Perhaps colleges should offer an entry level course that is free. Maybe this looks like a level system where you can attain four levels [of certification] when you go through it.”

Early data on hiring sites like Indeed show positions that require a knowledge of generative AI is up 50%

“We should start looking at incorporating basic knowledge of AI in K-12 and later ingraining that within our colleges as well,” said Breuhan. “There can be certain new degree programs and certificates, different types of IT, to help people understand and work better with AI.”

Steele concurred with Breuhan, saying that AI should be taught at the K-12 level.

“Academia should begin AI education early. When I was a fourth grader I had a typing class. When my daughter is in fourth grade, she might be learning how to use AI to do a project on animals,” Steele said.

AI/ML in Industry

Though the meteoric rise of AI is only now affecting society at large, the manufacturing industry has incorporated this technology for years through AI and ML operations. Industry stalwarts like CAD and CNC programs began utilizing ML in the late 1980s all the way to present times to calculate new efficiencies and accuracy. However, recent market advancements in AI/ML computing power and sophistication should prompt all manufacturers to take a renewed look into how this technology can optimize their workflows.

Here are some common uses of AI in industry that will be improved significantly with the rise of AI:

• Quality Control in industry has significantly benefited from AI integration. Machine learning algorithms can analyze vast amounts of data from production processes to identify patterns and anomalies, enabling real-time monitoring and early detection of defects.

• Predictive Maintenance leverages AI algorithms to anticipate equipment failures before they occur, thereby minimizing downtime and optimizing maintenance schedules.

• Supply Chain Management is another candidate for AI integration. Advanced analytics and machine learning algorithms enable organizations to optimize inventory levels, forecast demand more accurately, and mitigate risks in the supply chain.

• Generative Design utilizes AI algorithms to explore countless design permutations based on specified criteria and constraints. By harnessing the power of computational creativity, generative design enables engineers and designers to quickly generate innovative and optimized designs that may not have been conceivable through traditional methods.

However, these applications were already in practice before the boom of AI in 2022. What drives the spark of innovation today?

One application to take note of could be Google DeepMind AI’s Robocat robotic assembler, which learns assemblies without the need for humaninput coding functions. Though the process is in its infancy, the capabilities of a machine to learn how to calculate how to assemble products without explicit directions would be groundbreaking for the automation industry

In lieu of definitive regulation, the responsibility will be placed on organizations to create transparent and accountable processes to ensure AI is used ethically.

AI Challenges

The world is changing because of AI's growth. Fear is a natural response for many, as we have never dealt with rapid innovation of this scale before. Just what does the future look like for organizations that seek to implement AI technology in their operations? More importantly, what risks and challenges should they be aware of?

As with any new technology, there will be a learning curve and potential for misuse. In lieu of definitive regulation, the responsibility will be placed on organizations to create transparent and accountable processes to ensure AI is used ethically.

Undefined Ethics

Consequently, organizations need to have a well-thought-out plan for implementing AI in a way that benefits both the company and its employees. Companies like IBM are already taking proactive steps to ensure the ethos of AI in a work environment and beyond, creating an AI Ethics Board and defined principles and pillars to govern responsible use of this technology.

Employees Skilled in AI

Outside of the C-Suite, investments in reskilling and upskilling programs will be just as crucial to ensuring a seamless transition to a more AI-driven workplace. In early 2024, these upskilling and reskilling programs look like certificates of AI education spearheaded by higher education. For example, Michigan State University is offering an AI Bootcamp for students and employees alike. The Wharton School at the University of Pennsylvania is also offering a similar Generative AI Masterclass complete with an AI certification.

Database Bias

Another challenge to consider is the potential for bias in AI algorithms. These algorithms are only as unbiased as the data they are trained on, and if this data reflects existing societal biases, then the AI will perpetuate them. A study conducted by the U.K.’s University of East Anglia found ChatGPT skewed slightly liberal with its initial unfiltered internet dataset. In a blog post by OpenAI, the AI tech company said any detected biases are “bugs, not features.”

With actively developing technology, it is important for organizations to monitor and evaluate their AI systems for any discriminatory patterns and take steps to correct them.

Data Security

From a security standpoint, businesses must be careful about the data they collect and how it is used. With increasingly personal information being fed into AI systems, there is a risk of sensitive data being exposed. However, broad use AI will only improve with relevant and new data within the system.

The U.N. recently acknowledged this Catch-22 in an interim report by its AI Advisory Body, stating “Regulatory frameworks and techno-legal arrangements that protect privacy and security of personal data, consistent with applicable laws, while actively facilitating the use of such data will be a

critical complement to AI governance arrangements, consistent with local or regional law.”

In absence of definitive regulation on how proprietary data and AI interact, companies need to have strict protocols in place for safeguarding their data. Furthermore, they should thoroughly review the privacy policies of their AI providers to make informed decisions about what is safe to share to an AI database.

Provider Transparency

Primarily, there needs to be a focus on transparency. As AI becomes more prevalent in the workplace, employees and consumers will want to understand how decisions are being made and by whom. Businesses must be open about their use of AI and provide clear explanations for how it is being used, whether that is through data collection, decision-making processes, or other means.

As a call to action on AI transparency, Stanford University created the Foundation Model Transparency Index (FMTI) to score AI companies on a scale of 0-100 on 100 technology variables comprised of upstream, downstream, and model indicators. The top scoring model, Meta’s Llama 2, only satisfied 54 of 100 variables. The mean score of all models tested was 37, prompting the Stanford FMTI advisory board to conclude there is “a fundamental lack of transparency in the AI industry.”

Final Thoughts

Though it is important to recognize the mountain of potential for AI to change the workplace and manufacturing, it may be more important to keep our focus on ensuring each climbing step is on sure footing. What will separate successful adoption of AI from failure will be a slow and methodical focus on ethical adoption.

Leaders in technology and industry have a unique opportunity to stake the guiding principles regarding AI for this generation and beyond — with that comes great responsibility. Let us be sure to leave a path where all can follow toward a more prosperous future with AI.

Leaders in technology and industry have a unique opportunity to stake the guiding principles regarding AI for this generation and beyond — with that comes great responsibility.

INDUSTRY PULSE

Automation Alley posted weekly polls in January 2024 for our LinkedIn followers of over 5,000 professionals in the technology and manufacturing ecosystem on the topic of AI in the industry. This is how the industry responded at a glance.

Do you see artificial intelligence as the most important Industry 4.0 technology?

Where do you see the most promise in your operations?

What degree of regulation do you think should be imposed on AI?

1 2 3 4 5 6 7

KEY TAKEAWAYS

Workforce Transformation: AI presents both opportunities and challenges for the labor force, with predictions of job creation alongside changes in job roles. Companies are already using AI to improve efficiency and job performance, leading to increased demand for workers skilled in data analysis, programming, and machine learning. Investments in reskilling and upskilling programs are crucial to prepare individuals for the AI-driven workplace.

AI/ML in Industry: AI and machine learning have been integral to industries like manufacturing, enhancing processes such as quality control, predictive maintenance, and supply chain management. Advancements in AI/ML computing power prompt reevaluation of how this technology can optimize workflows, including innovative applications like generative design and robotic assembly

Call for Ethical Adoption: Success in AI adoption hinges on a methodical approach to ethical implementation, with leaders in technology and industry bearing the responsibility to set guiding principles for AI use. Transparency in AI use and decision-making processes is crucial to building trust among employees and consumers, ensuring a path toward a prosperous future with AI.

Define Clear AI Objectives & Start with Pilot Projects: For successful AI adoption, companies should define specific goals and objectives, guiding their AI strategy towards improving production efficiency, enhancing product quality, or reducing maintenance costs. Starting with pilot projects allows for iterative learning, risk mitigation, and gradual adoption of AI within manufacturing operations.

Invest in Data Infrastructure & Cybersecurity: Prioritize the development of a robust data infrastructure to collect, store, and manage the vast amounts of data generated by manufacturing processes. Ensure data quality, integrity, and accessibility for effective AI-driven insights and decision-making. Implement advanced security measures to protect sensitive data and comply with regulations.

Invest in AI R&D: Governments should allocate resources to fund R&D initiatives focused on advancing AI technologies tailored for various sectors, including manufacturing. This investment can include funding academic research, supporting industry collaborations, and providing incentives for businesses to innovate in AI. By fostering a culture of innovation, governments can accelerate the development and adoption of AI solutions that enhance productivity and competitiveness.

Embrace AI as an Augmentation Tool: Educators should view AI as a powerful tool to enhance teaching and learning experiences, automate routine tasks, provide personalized feedback, and facilitate customized learning plans. AI won't replace teachers but will require educators to adapt their teaching methods to engage students effectively. Cultivating partnerships between teachers, students, and AI can advance critical thinking skills in the classroom.

Recommendations for Industry

Artificial intelligence has already revolutionized the automation landscape—and the transformation is far from over. Industry has recently witnessed AI's disruptive impact on customer service, predictive analytics, predictive maintenance, resource allocation, problem solving, and many other aspects of production. As AI’s influence grows, so do the technology’s benefits, challenges, and ethical considerations within industry. To prosper in an AI-drive world, manufacturers must define clear AI objectives, invest in data infrastructure, focus on talent and training and embrace collaboration and partnerships. Doing so will help companies achieve operational excellence and unlock new opportunities for innovation.

Define Clear AI Objectives & Start with Pilot Projects

For successful AI adoption, companies should clearly define the specific goals and objectives they aim to achieve with AI implementation, whether it's improving production efficiency, enhancing product quality, or reducing maintenance costs. Understanding your objectives will guide your AI strategy and ensure alignment with overall business objectives.

Begin AI implementation with pilot projects focused on specific use cases or areas of improvement. Start small to validate AI technologies, assess their impact, and refine strategies before scaling across the organization. Pilot projects allow for iterative learning, risk mitigation, and gradual adoption of AI within manufacturing operations.

Invest in Data Infrastructure & Cybersecurity

Companies looking to adopt AI should prioritize the development of a robust data infrastructure to collect, store, and manage the vast amounts of data generated by manufacturing processes. Ensure data quality, integrity, and accessibility to enable effective AI-driven insights and decision-making, and consider implementing IoT sensors, data lakes, and cloud-based platforms to facilitate data aggregation and analysis.

Tremendous amounts of data are required for predictive analytics to be accurate and valuable to a company. Compiling data sets can include implementing internal statistics and information, crowdsourcing, purchasing prepackaged data, using software to collect data from the web, and reinforcement learning from human feedback (RLHF). This machine learning technique learns from interaction with people to improve performance. AI is only as good as the data it analyzes. Data filled with errors and bias, or incomplete data, will lead to misinterpretation and faulty conclusions.

Additionally, large amounts of sensitive data require exceptional privacy protection. Companies need to employ the most advanced security measures and remain vigilant with local, state, and federal compliance regulations. An essential safety measure is closely guarding those with internal data access.

Focus on Talent and Training

There is a need for programmers, data scientists, engineers, and other highly skilled workers who can create and maintain complex AI programs. Shortages abound in this field and require industry leaders to connect with academia to create training programs and internships that fill specific needs. Companies need to develop internal training programs to reskill and upskill current workforces.

Job loss in the highly automated production industry is a common fear. The idea is that AI-controlled robots will remove people from the lines and leave them without suitable employment. According to a World Economic Forum report, by 2025, 85 million jobs may be displaced by a shift in the division of labor between humans and machines, while 97 million new roles may emerge that are more adapted to the new division of labor between humans, machines, and algorithms.

"Within the AI revolution in manufacturing, the most crucial element remains the human workforce. As we embrace advanced technologies, let us never forget that it is the dedication, creativity, and ingenuity of our people that truly drive progress and innovation," said Stephanie Wright, COO of the US Center for Advanced Manufacturing.

"Within the AI revolution in manufacturing, the most crucial element remains the human workforce. As we embrace advanced technologies, let us never forget that it is the dedication, creativity, and ingenuity of our people that truly drive progress and innovation."

– Stephanie Wright, US Center for Advanced Manufactuing

AI is an invaluable tool for interpreting data and creating algorithms, but it is just a complex piece of technology that lives in the black-andwhite world of the data it provides. It lacks real-world understanding and nuance, so the human-to-machine relationship is crucial to creating effective AI for industry use. Providing ongoing training will empower employees to leverage AI technologies effectively.

According to the World Economic Forum, an average of 66% of employers surveyed expect to get a return on investment in upskilling and reskilling within one year.

Embrace Collaboration and Partnerships

Collaboration with technology providers, industry experts, and academia can help companies leverage external expertise and resources for successful AI implementation. Partner with vendors who specialize in AI solutions for manufacturing and explore collaborative research initiatives to stay at the forefront of technological innovation. Work with education leaders to ensure classroom teaching and training align with current and future needs. Partner with governments to provide training and employment services to displaced workers. By fostering collaboration and partnerships, manufacturers can accelerate AI adoption and drive sustainable competitive advantage.

Recommendations for Academia

"You have to stop thinking that you can teach exactly the way you used to teach when the basic medium has changed."

- Houman Harouni, Harvard Graduate School of Education

Artificial intelligence represents a profound shift to the traditional educational landscape, with a promise to transform teaching methods, research practices and administrative tasks in classrooms at all levels across the globe. From personalized learning experiences to data-driven decision-making, AI is set to disrupt the educational ecosystem perhaps more than any other technology available today. Educators navigating this uncharted territory must leverage AI to harness its potential effectively, while preparing the next wave of graduates with the technical skills needed to enter the workforce.

It is critical that educational institutions at all levels begin to embrace AI as an augmentation tool, cultivate data literacy skills and foster ethical AI practices in the classroom.

Embrace AI as an Augmentation Tool

Automated systems have moved far beyond teachers feeding Scantron sheets into a computer to grade tests and offer a percentage of correct answers. New AI tools can read essay questions, suggest improvements and identify trends in the classroom. Rather than fearing displacement, educators should view AI as a powerful tool to enhance teaching and learning experiences, automate routine tasks, provide personalized feedback, and facilitate customized learning plans.

AI won't replace teachers, but it will require educators to adapt to the technology. Houman Harouni, lecturer on education at the Harvard Graduate School of Education, said in a July 2023 article, "You have to stop thinking that you can teach exactly the way you used to teach when the basic medium has changed." If students can turn to ChatGPT or other AI language models for quick and easy answers, Harouni believes that lessons need to be more robust to engage students. “We have to create assignments that push [students] to the point where they have to question what is the framework that is being used here and what would it mean for me to radically change this framework.”

Going beyond that framework means students become creative in the questions they ask of AI and push beyond simple “yes” or “no” answers or regurgitation of facts. They will take the answers and ask follow-up questions to seek more information.

Teachers, students, and AI can be partners in advancing critical thinking skills.

Cultivate Data Literacy Skills

In today’s digital world, it is imperative students possess data literacy skills and have a basic understanding of AI tools and their capabilities. Educators must ensure students have the knowledge and understanding of data analytics and AI to leverage this technology effectively in the classroom. Educators, too, must prioritize digital literacy and stay abreast of the latest AI offerings. By understanding how to harness data insights, educators can optimize instructional strategies related to AI, identify student needs, and drive continuous improvement and learning.

A recent survey of 500 U.S.-based developers at enterprise companies, found that 92% were already using AI coding tools and 70% responded that AI coding tools offer them an advantage at work. By integrating AI into the learning environment, educators can equip students with the tools needed to navigate the current realities and future transformations of the world.

Foster Ethical AI Practices

Educators face a difficult challenge to allow AI for learning while keeping its use ethical and free from abuse. Online programs offer students the tools to write essays, answer complex math problems, and translate work from other languages and claim it as their own. Using AI to solve problems and complete assignments could leave graduates unprepared for the workforce.

Students might not consider the ethical considerations of using AI. Schools at all levels should institute a code of ethics and discuss the best practices for using the technology. Remind the students that AI is a tool and can be used to expand knowledge and create critical thinking opportunities.

By integrating AI into the learning environment, educators can equip students with the tools needed to navigate the current realities and future transformations of the world.

Accountability is an important aspect of AI. In a December 2023 article about changing assessment strategies to curb improper use, Andre Smith offered tips like open-book tests that require critical thinking and interpretation, in-person exams making it more challenging to use AI to cheat, oral exams requiring interaction between the instructor and student and project-based assignments demanding time and creativity that AI cannot accomplish.

AI itself can help combat technology misuse through software designed to detect plagiarism and identify trends among students. Instructors can learn if students perform outside their usual capabilities or write essays in styles that don't match previous work.

Recommendations for Government

Governments should push industries to adhere to more encompassing approaches to data collection, which can aid in better service to the public and curb potential legal issues.

Government leadership on digital technologies is more critical now than at perhaps any other time in our history. As AI enters more facets of society, government bodies must balance the need to create standards that ensure safety, ethics, privacy, and cybersecurity with this technology’s disruptive potential to drive innovation.

As we enter this uncharted territory, its critical that governments at all levels create regulatory frameworks, promote equity for AI, invest in AI research and development (R&D) and prioritize workforce training to ensure the current and future labor pool is equipped with the skills needed to work alongside this transformative technology.

Create Regulatory Frameworks

The United States federal government has created an AI Bill of Rights, “a set of five principles and associated practices to help guide the design, use, and deployment of automated systems to protect the rights of the American public in the age of artificial intelligence.” With the AI Bill of Rights as a benchmark, government regulations must consider working in various measures to ensure potential legislation or policy implementations achieve their desired result.

For example, governments should push industries to adhere to more encompassing approaches to data collection, which can aid in better service to the public and curb potential legal issues.

Industries should be collecting relevant data and systems also need extensive testing to identify potential risks or shortcomings in the programming, with the results undergoing comprehensive evaluation. Moreover, collecting data representing people among all demographics and avoiding proxy variables such as income, geography, education, and age can curb algorithmic discrimination. The government can also step in with added regulation to help businesses safeguard the public's data privacy.

Promote Equity Within Industry

Governments can ensure AI is available to firms of all sizes by working with industry leaders to determine grant money distribution. The government can create grants for smaller firms that train workers to become proficient in software programming, data storage, analytics, robotics, and other areas required to effectively use the technology. These measures will assist all companies to realize the financial and operational benefits of AI.

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Invest in AI R&D

Governments should invest in R&D initiatives to advance AI technologies specifically tailored for the manufacturing industry and other sectors. This could involve funding academic research, supporting industry collaborations, and providing incentives for businesses to invest in AI innovation. By fostering a culture of innovation and providing financial support for R&D efforts, governments can help accelerate the development and adoption of AI solutions that drive productivity, efficiency, and competitiveness in the manufacturing sector.

Invest in Workforce Training

Governments should prioritize workforce development and education initiatives to ensure that workers have the skills and knowledge needed to thrive in an AI-driven economy. This could involve implementing training programs, reskilling initiatives, and educational partnerships with industry stakeholders to equip workers with the technical skills and expertise required to operate and maintain AI-enabled manufacturing systems. Additionally, governments should promote lifelong learning and encourage continuous upskilling to ensure that the workforce remains adaptable and capable of leveraging AI technologies effectively.

The AI Bill of Rights

Safe and Effective Systems - You should be protected from unsafe or ineffective systems.

Algorithmic Discrimination Protections - You should not face discrimination by algorithms, and systems should be used and designed equitably.

Data Privacy - You should be protected from abusive data practices via built-in protections, and you should have agency over how data about you is used.

Notice and Explanation - You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you.

Human Alternatives, Consideration, and FallbackYou should be able to opt-out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter.

Source: The White House - Blueprint for an AI Bill of Rights

How to Use Generative AI and Large Language Models

Productively and Responsibly

Pavan Muzumdar, Automation Alley COO, Project DIAMOnD CEO

While still rapidly evolving, Generative AI (Gen AI) and Large Language Models (LLMs) have emerged as powerful tools, transforming how we create, analyze, and interact with information. As these technologies become more integrated into our daily tasks, understanding how to use them effectively and ethically is critical to ensure factual and high-quality output. The following guidelines offer a comprehensive set of dos and don'ts for users. These principles are designed to maximize the potential benefits of Gen AI and LLMs while simultaneously addressing the challenges and responsibilities associated with their use, ensuring a balanced and conscientious approach to leveraging these advanced tools in various applications.

DOs

Use Gen AI for information synthesis, comparisons, and summarization.

Gen AI excels at digesting large volumes of information and presenting concise summaries, comparisons, and syntheses. This can significantly enhance productivity, particularly in research, learning, and decision-making contexts.

Use Gen AI for ideation and leverage personas in doing so.

Brainstorming, in which humans generate many ideas in a group setting, is an effective method to get a collection of ideas on paper before converging on an action plan. AI can simulate brainstorming by providing a similar outcome. By leveraging personas, in which you ask the AI to assume a certain character or outlook, you can enrich the ideation process by simulating a diverse set of opinions.

Encouraging AI to challenge its own conclusions can uncover biases, assumptions, and alternative perspectives, leading to more nuanced and comprehensive understanding.

Use Gen AI to get started on a project.

The first step of any project is often the hardest and the most timeconsuming. Gen AI can provide a kick-start by generating outlines, proposing ideas, or even creating a basic version of the project's deliverables. Once this is done, you can iterate much more rapidly and accomplish your final outcome sooner. Additionally, if you follow some of the other “Dos” and “Don’ts” in this list, you can develop a more comprehensive outcome.

Ensure that you ask Gen AI to offer results in chunks that you as a human can verify and validate.

Breaking down the output into manageable pieces that a human can process allows for more effective verification of accuracy and relevance. This approach fosters a highly effective collaborative synergy between human intelligence and AI capabilities.

Ask Gen AI to analyze its own results and take a contrarian view against itself for a balanced viewpoint.

Encouraging AI to challenge its own conclusions can uncover biases, assumptions, and alternative perspectives, leading to more nuanced and comprehensive understanding. Often these biases are hidden, hence following this approach can bring them to the surface making the output more comprehensive and potentially more ethical.

Understand that prompts, results generated by prompts, and data provided to Gen AI in developing the results should be kept confidential.

LLMs as such are shared software that can be used by a wide range of users. They comprise the code that responds to input and the underlying data that is the result of training input, which itself is vast amounts of data. That said, when you ask an LLM a question via a prompt, the prompt is unique to you, the output of that prompt is unique, and any data that you used in the prompt is unique. This means that it constitutes information unique to you and it’s important to treat it and protect it as such, since over time it will be the source of unique insight, understanding, and know-how

DON'Ts

Ask Gen AI for factual information unless you can independently verify it.

While AI can provide factual information, it's essential to cross-check these facts with reliable sources due to the potential for inaccuracies. Sometimes AI will hallucinate and fabricate information that sounds and appears factual. Unless you can verify it independently, do not trust the output generated by AI.

Fixate on having Gen AI complete a project.

AI should be seen as a tool to assist, not to replace, the human element in projects. Relying solely on AI for completion may result in sub-optimal results. Furthermore, you might spend more time than necessary in completing a task or project.

Abdicate your responsibility as the leader and ultimate arbiter of result quality.

As the user, you are ultimately responsible for the outcomes generated by AI. This means being committed to overseeing the AI's work, ensuring it is factual and of high quality.

Ignore privacy-conscious practices.

An LLM as mentioned above is shared software. Therefore, any information provided to it is not secure. Providers of LLMs and Gen AI tools do not provide definitive guidance on how data provided to it is used. Safeguarding personal and sensitive information is paramount, especially as these systems can store and potentially misuse such data if not handled properly. It is best to assume that it will be available publicly and take steps to prevent unintended and unauthorized access.

The Most Important Do: Be responsible and aware.

Finally, AI and LLMs are software tools. They do not possess humanity or any of the attributes of it. They do not have emotion, sensitivity, or sensibilities. They do not understand offensive language or praise. Everything generated by AI is a probabilistic output that appears magical because of the amount of information used in enabling its generation and the sophistication of the software and processing power behind it. As such, it is incumbent on all of us as users to be aware that the output of AI can be offensive, inaccurate, hurtful, and insensitive; and it is our responsibility to ensure that what we put out into this world isn’t.

Adhering to these guidelines can maximize the benefits of Gen AI and LLMs while mitigating risks and ethical concerns, ensuring a productive and responsible integration of AI into our work and creative processes. Know, however, that these systems are rapidly evolving, and these principles are valid as of this writing in early 2024. Always be cognizant of developments and stay abreast of the implications of changes so that you are in charge as a sophisticated discerning user of these powerful technologies. It’s very possible that in the future any or all of these Dos and Don’ts may require revision and new ones need to be defined.

AI and LLMs are software tools. They do not possess humanity or any of the attributes of it. It is incumbent on all of us to be aware that the output of AI can be offensive, inaccurate, hurtful, and insensitive; and it is our responsibility to ensure that what we put out into this world isn’t.

ABOUT:

MISSION:

Automation Alley is a nonprofit technology business association and Digital Transformation Insight Center focused on driving the growth and success of businesses in Michigan and beyond through innovation and automation. With a global outlook and a regional focus, we foster a vibrant community of innovators, entrepreneurs, and business leaders through opportunities for collaboration and learning. Our programs and services help businesses develop the skills and expertise needed to effectively jumpstart or accelerate digital transformation. By bringing together industry, academia, and government, we aim to create a dynamic ecosystem that drives innovation and growth across Michigan.

At Automation Alley, our mission is to help businesses thrive in the rapidly changing digital economy by equipping them with the knowledge, insights, and tools to develop a software-first mindset that leverages the power of automation, AI, and other cognitive technologies. We believe that by working together, we can build a stronger, more innovative, and more competitive economy for the future.

VISION:

Wealth, prosperity and equality through technology.

To find out more about Membership visit: automationalley.com Publication

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