Copyright: eurekalert.org – “Artificial Intelligence-Based Chatbot Created for Bioimage Analysis”

Scientists from Universidad Carlos III de Madrid (UC3M), together with a research team from Ericsson and the KTH Royal Institute of Technology in Sweden, have developed an artificial intelligence-based software programme that can search for information and make recommendations for biomedical image analysis. This innovation streamlines the work of individuals using large bioimage databases, including life sciences researchers, workflow developers, and biotech and pharmaceutical companies.

The new assistant, called the BioImage.IO Chatbot and introduced in the journal Nature Methods, was developed as a response to the issue of information overload faced by some researchers. “We realised that many scientists have to process large volumes of technical documentation, which can become a tedious and overwhelming task,” explains Caterina Fuster Barceló, a researcher in the Department of Bioengineering at UC3M and one of the study’s authors. “Our goal was to facilitate access to data information while providing a simple interface that allows scientists to focus their time on bioimage analysis rather than programming,” she adds.

The chatbot can be a very useful tool, enabling researchers to perform complex image analysis tasks in a simple and intuitive manner. For example, if a researcher needs to process microscopy images using segmentation models, the chatbot can help select and execute the appropriate model.

The assistant is based on extensive language models and employs a technique called Retrieval-Augmented Generation (RAG), which enables real-time access to databases. “The main advantage is that we do not train the model with specific information; instead, we extract it from up-to-date sources, minimising errors known as ‘hallucinations’, which are common inaccuracies in other AI models like ChatGPT,” adds Arrate Muñoz Barrutia, professor in the Department of Bioengineering at UC3M and another author of the study.[…]

Read more: www.eurekalert.org

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Strategic AI Alliances and Funding Trends – SwissCognitive AI Investment Radar

Today, we explore a range of significant AI investments and strategic partnerships shaping the global landscape. Our journey takes us from multi-billion dollar alliances to innovative funding initiatives, highlighting how AI continues to drive economic growth and technological advancement across various sectors.

This week, we witness the formation of a substantial $18.9 billion partnership between Hitachi and Microsoft, aimed at integrating advanced AI technologies into Hitachi’s solutions. Microsoft also makes headlines with a $3.2 billion investment in Sweden, leveraging the region’s commitment to green energy to enhance AI and cloud facilities.

Visa’s launch of a $100 million fund underscores the transformative potential of generative AI in commerce and payments, while AI’s prowess in financial analysis helps investors make better long-term decisions. Guidance from ESMA on AI in investment services highlights the importance of compliance and data quality in this evolving landscape.

Google’s $2 billion commitment to Malaysia promises significant economic and job growth, driven by advancements in AI and cloud infrastructure. Meanwhile, JPMorgan’s expansion of its AI tool aims to refine investment decisions and outcomes.

From parcel delivery innovations by Evri to the growth of AI-driven asset management strategies, this edition covers the diverse applications and impacts of AI investments.

Join us as we delve into these dynamic developments and the future they herald for AI and its role in the global economy.

Previous SwissCognitive AI Investments Radar: The AI Funding Fever’s Key Insights And Major Players.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Strategic AI Alliances and Funding Trends – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Join us for an exhilarating ride through the latest news in the field of AI!

We’re exploring innovative AI solutions for dyslexia, AI’s impact on holiday retail, and MIT’s new technique for explaining complex AI systems.

Dive into a study using AI and satellites to uncover hidden industrial activities at sea and join the debate on the ‘Turing Trap’ in AI development.

Discover generative AI’s rise in design, Sweden’s AI-focused National Technology Strategy, new AI guidelines for judges, and how venture capitalists evaluate generative AI companies.

We are wishing you a succesful and happy New Year!

Best regards,

The Team of SwissCognitive

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SwissCognitive Guest Blogger: Kilian Woods, Sustainability & Technology Educator, Data & Analytics Strategist, Systems Innovator. “Can Artificial Intelligence increase innovation within the Swiss economy?”

Artificial Intelligence (AI) can revolutionize the Swiss economy. One of the many ways it can do this is through transforming innovation management practices within Switzerland. For example, AI would make the innovation process more efficient and effective. Nevertheless, businesses should not mistake technical innovations with effective implementation of innovation management practices within their companies. For example, many financial companies have now realised the importance of big data for predicting risks and adhering to regulative practices, such as ESG reporting, non-financial reporting, etc. The analysis and gathering of the data necessary to streamline these processes would benefit from the application of artificial intelligence to improve the company’s operation. This would help companies save millions by adhering to regulations, such as the new Swiss non-financial reporting obligations (Baumgartner, 2023).

There are hurdles that must be overcome to use artificial intelligence to accelerate innovation. An example would be companies that hire software engineers and expect them to build artificial intelligence that will revolutionise the industry without having effective innovation management processes. This leads to software engineers working in silos and producing poor-quality work that does not benefit the end user or having to wait endlessly for approval from executives.

The application of AI in businesses should be approached as an experimental and incremental process. Companies should have effective and efficient innovation management practices in place that will then make it easier to implement AI and then increase AI-driven innovation. The Swiss economy is well placed to lead AI-driven innovation. For example, the Global Innovation Index has ranked Switzerland as one of the most innovative countries (WIPO, 2022). Zurich is a research hub for artificial intelligence and can help industries innovate. An example would be how AI can be used for condition monitoring of wind turbines or solar farms. Such as how Aerones is implementing AI to check for material damage in wind turbines (Allinson, 2023).

Another indicator that Switzerland would be able to drive innovation within Switzerland using AI is Microsoft’s 2023 Annual Work Trend Index. According to this index, 65% of Switzerland’s workforce wants to do as much work as possible to AI to lessen their workload (Microsoft, 2023).  AI can also be used by the Swiss government to make sure companies are adhering to regulations and that their practices are not destroying the planet. An example would be to use AI for checking ESG reporting’s by companies.

Therefore, AI can revolutionize the Swiss economy when it is used alongside effective and efficient innovation management practices. Switzerland must promote education in artificial intelligence/ data science. Switzerland must continue to promote innovation and invest in research in AI to lead innovation worldwide. There must be enough investment that AI start-ups will stay in Switzerland and help to drive innovation in the economy. This will help to solidify Switzerland’s place as a leader in AI expertise and innovation.

ALLINSON. MARK, 2023, Aerones demonstrates ‘first’ robot for wind turbine maintenance and repair (roboticsandautomationnews.com)

Baumgartner, Hannes, 2023, New Swiss Non-Financial Reporting Obligations apply as of Business Year 2023 | Pestalozzi Attorneys at Law (pestalozzilaw.com)

Microsoft, 2023, Microsoft Study Reveals: Swiss Employees Eager to Delegate Tasks to Artificial Intelligence – Microsoft Switzerland News Center

WIPO, 2022, Global Innovation Index 2022: Switzerland, the U.S., and Sweden lead the Global Innovation Ranking; China Approaches Top 10; India and Türkiye Ramping Up Fast; Impact-Driven Innovation Needed in Turbulent Times (wipo.int)

Kilian Woods:
Kilian is passionate about driving systemic change towards solving the climate crisis. He has facilitated workshops on climate change with decision makers at COP26, over 45 different schools and universities such as IIT Delhi. He communicates the importance of artificial intelligence to transition society to renewable energy sources and for countries to achieve their climate goals. He is also in the ACE working group in YOUNGO, which is the official UN youth organization – behalf of which, he has been chosen to go as representative to the United Nations conference SB56. Last but not least, he analyses risk exposures of renewable energy projects at AXIS. Kilian, it is really nice to have you with us today – especially on such a short notice. Our world can turn upside down sometimes where flexibility become fundamental.

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Dalith Steiger and Andy Fitze, Co-Founders of SwissCognitive, World-Leading AI Network – “Swiss Power in Action: Leading and Propelling Innovation Beyond Borders”

From pioneering contributions in AI, precision manufacturing, and clean energy, to its unique education and startup-friendly policies, Switzerland serves as an influential global player, propelling the wheel of innovation in remarkable ways. Step into the world of Swiss ingenuity and see how it shapes global progress.

The Swiss AI Landscape

AI and other emerging technologies have positioned Switzerland at the forefront of the global digital economy, generating new opportunities and fostering resilience. The technological advancements have brought about profound changes in the country across various industries, enhancing operational efficiency, supporting sustainable development, and accelerating economic growth. In the banking sector for instance, AI-powered solutions are streamlining transactions, optimizing risk assessment models, and enhancing fraud detection mechanisms, reinforcing its global financial status. In healthcare, providers are integrating AI to improve diagnostics and personalized treatments. Swiss manufacturers are also leveraging AI for automation and quality control. For instance, ABB’s use of AI in robotics and industrial automation signifies a new era of manufacturing precision and efficiency. AI is also revolutionizing Switzerland’s world-class education system, with adaptive learning platforms customizing educational experiences to individual student needs. And as a final example, with AI, Swiss public transportation systems, such as the Swiss Federal Railways (SBB), are optimizing route planning, enhancing safety measures, and improving overall passenger experiences.

“AI and other emerging technologies have positioned Switzerland at the forefront of the global digital economy, generating new opportunities and fostering resilience.”

Switzerland sculptures its trajectory through advanced education, innovation, and strategic policy. Renowned universities like ETH Zurich and EPFL act as hotbeds of AI research and development, attracting global talent, and fostering ground-breaking AI applications. Coupled with a thriving startup ecosystem, Switzerland brings forth AI innovations across sectors, from healthcare to finance. Switzerland’s balanced regulatory approach prioritises technological advancement and ethical considerations, making it a trusted global AI partner. Hosting European branches of tech giants and AI research Hubs, such as Microsoft, Oracle, Google, Disney Research, and IBM underscores its prominence as a leading AI nation. Consequently, Switzerland’s multifaceted contributions are crucial in shaping the global AI discourse, underlining its indispensable role in the future of AI.

Taking all the above into consideration, no wonder that since 2011, Switzerland is number one innovative country according to the Global Innovation Index (GII), a ranking published annually by the World Intellectual Property Organization (WIPO). The question is, with the current pace of development on a global level, how can the country ensure that it stays in that position?

Credit: SICTIC, https://www.sictic.ch/map/

The Global AI Landscape

On a global scale, technological advancements, including AI, are reshaping economies and societies. Businesses worldwide leverage AI to enhance decision-making, streamline operations, and improve customer experiences. In the Western world, the United States leads the pack, with Silicon Valley companies like Google and Tesla integrating AI into everything from search algorithms to autonomous vehicles. Meanwhile, in Europe, Sweden’s healthcare sector innovatively uses AI for predictive medicine, substantially enhancing patient care. In the United Kingdom, AI-powered financial technologies are revolutionizing the banking sector. Companies like Revolut provide digital banking services, changing traditional banking paradigms and enhancing user experience. In France, the public sector employs AI to enhance public services, such as predictive policing to prevent crime. Furthermore, also in the Global South, AI is making significant inroads. For example, in Rwanda AI-driven drones deliver medical supplies to remote areas, overcoming infrastructural challenges. In Asia, China’s AI advancements are pervasive, from facial recognition in public security to AI-driven tutoring in education. Japan, known for its robotic technology, uses AI in eldercare robots, addressing its demographic challenge. In the Middle East, the UAE’s AI strategy 2031 underscores its commitment to integrating AI in sectors like transport, with autonomous vehicles and AI-driven traffic management systems becoming increasingly common. Advancement with cognitive technologies is happening in almost all corners of the world.

However, as with any profound shift, these advancements bring challenges. Concerns around privacy, cybersecurity, and the ethical use of AI remain, requiring a concerted effort to find viable solutions. Policymakers, businesses, and academia must collaborate to establish standards and regulations that ensure the responsible and beneficial use of AI. Furthermore, we need to find ways how we can narrow the gap when it comes to inequalities on global level. The Global South could take great advantage of emerging technologies, yet they are the least ready to harness these. The inability of developing countries to embrace the technological revolution, owing to inadequate policy intervention or lack of targeted investment in building skills and capacities, could lead to persistent adverse consequences.

Swiss Mastery: Shaping the Global AI Trajectory

With the available resources and infrastructure, Switzerland has the potential not only for being top leader in innovation and AI, but also key propeller of global development.

Harnessing its rich resources and sophisticated infrastructure, Switzerland is uniquely positioned to catalyze global development beyond its outstanding AI achievements. Its advanced education system, complemented by world-class research institutions, fosters a nurturing environment for digital talent. This intellectual capital fuels innovative AI startups and attracts global tech giants, further strengthening Switzerland’s role as a key influencer in the international AI arena. The Swiss government’s balanced regulatory framework fosters trust, encouraging collaborative ventures in AI. This positions Switzerland not only as an AI powerhouse, but also as a convener for ethical standards and norms in the digital sphere.

“Switzerland’s influence extends beyond its borders, leveraging AI not merely for national advancement, but global progress.”

By promoting knowledge exchange and shared best practices, Switzerland propels global development, fostering an inclusive, sustainable, and ethically responsible digital future. Consequently, Switzerland’s influence extends beyond its borders, leveraging AI not merely for national advancement, but global progress. As we navigate this tech-driven era, Switzerland stands as a beacon, demonstrating the transformative potential of AI and other emerging technologies for businesses, economies, and societies worldwide.

In order to maintain Switzerland’s growth trajectory, a rising number of leaders and domain experts must unite around shared objectives, and a growing multitude of organizations must embark on the transformative journey with cognitive technologies. Priorities will need to continue to shift towards upskilling and reskilling programs, fostering public-private partnerships for AI research and development, and maintaining a supportive regulatory environment. Equally important is fostering an innovation culture that encourages experimentation and risk-taking. By cultivating these conditions, Switzerland can continue securing its role within and beyond its borders.

“The integration of AI and emerging technologies into businesses and industries is an inevitable development. It presents a promising outlook for global economic growth and sustainable development, with Switzerland leading the charge.”

About the Authors:

Dalith Steiger is a serial entrepreneur and a global AI Strategist and Thought-Leader. She belongs to the top pioneering women in cognitive technologies and one of the top digital shapers and leading voices in the global AI ecosystem. Dalith was featured in Onalytica’s Who’s Who in AI report as a global key opinion leader. She was born in Israel, grew up in Switzerland, and studied mathematics and business informatics at the University of Zurich. With Andy Fitze she co-founded the award-winning AI start-up SwissCognitive, and the CognitiveValley Foundation. Dalith is a global AI-strategy advisor and speaker, sharing her extensive knowledge and experience in the field of AI around the world. She sits in several boards and juries, is leading the Swiss IT Leadership Forum, advises various companies in their AI journey, mentors young women and girls in tech, and teaches AI & Machine Learning in a CAS module at the Applied University of Luzern. Besides her drive for cognitive technologies, she is also a loving mother of two young women, a passionate mountain biker and a big fan of high-heel shoes.

Andy Fitze is a serial entrepreneur, digital cognitive strategist, AI influencer, and top global AI and digital transformation advisor for start-ups and enterprise boards. Andy was featured in Onalytica’s Who’s Who in AI report October 2021 and is one of the top digital shapers. With Dalith Steiger he co-founded the award-winning start-up SwissCognitive, and the CognitiveValley Foundation. He is president of the Swiss IT Leadership Forum, member of the Board of Directors of SwissICT. Andy sits in several Boards of Directors of various companies. Andy is a lecturer and Member of the Strategic Advisory Board at Bern University of Applied Sciences and is a lecturer at the ETH for CAS Architecture Digitization. Previously Andy worked as Group CIO of RUAG, and at PostFinance he was responsible for IT governance. He holds a degree in electrical engineering (HTL), an Executive MBA from the University of St. Gallen, and received the Swiss CIO Award for Best IT Manager in Switzerland in 2015.  To share his 30 years of extensive knowledge and experience, Andy is often seen on global stages. He is also a passionate skipper on the oceans – providing him with a great balance for head and soul.

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Shaping the Societal Acceptance & Development of Artificial Intelligence
It doesn’t matter how great new technological (or any) inventions are, if society doesn’t accept them, all the resources used for research and development goes down the drain. To ensure that new technologies can go through their entire lifecycle and pave the path for new inventions, many aspects come into play. Leaders and experts are one of these aspects and they play a pivotal role in the societal acceptance of these technologies.

Looking at AI From the Right Perspective
AI is an interesting newcomer (if newcomer at all). We use it, as part of end products or services, yet we feel sceptical about it – based on some unrealistic reputation built by the media and Hollywood. As a result, many people, when think about AI, tend to picture robots and avatars, such as Arnold Schwarzenegger in the Terminator, Ava in Ex Machina, David in Prometheus, or they have headlines popping in, such as “AI Will Overtake Humans in 5 Years”, “Humanity Should Fear Advances in Artificial Intelligence”, “Is the World Moving Towards Real Terminators?”, “AI Gained Consciousness”.

The invisibility of this technology also doesn’t make it easy for many of us to understand and accept it. Which is quite interesting as with most inventions we are actually not necessarily interested how they work, but rather what purpose they serve and how they contribute to our lives. Nevertheless, the case is different with AI. Which is not an strange issue after all, as we, human beings are naturally curious, but it certainly sets tougher standards and expectations for industries which need to be met. Organizations not only need to ensure that they comply with Technological Social Responsibility, but they also need to find ways to communicate to the public how the technology works and how they benefit from it.  The sources of these information have to be trusted, justified and credible. Industry experts and leaders play a pivotal role in the process of spotlighting the need for AI-based solutions, and with hands-on transparent insights create understanding and drive development.

“Industry experts and leaders play a pivotal role in the process of spotlighting the need for AI-based solutions, and with hands-on transparent insights create understanding drive development.”

AI Experts in the Spotlight
To ensure that our products and services increasingly benefit from the power of AI across industries, credible experts with hands-on practical insights need to be put more into the public eye. Reason being is that their expertise, skills, experience, and limitless curiosity don’t only serve as mediums to advance AI, but also as a mediums to demystify this technology, create understanding, and  build public trust.

Global AI Ambassadors on a Joint Mission
With the purpose of demystify AI, creating understanding, and  building public trust, and with the strong SwissCognitive principle of “Share for Success”, a global network of AI experts have been created, consisting of 50 AI Ambassadors. These incredibly inspiring minds have been carefully selected by the core and extended Team of SwissCognitive, World-Leading AI Network strictly on the basis of their personal accomplishments – regardless companies, organizations or products. They meet various tough criteria concerning for instance expertise, experience and, very importantly, trust. The AI Ambassadors believe in the smart combination of human and artificial intelligence that can drive businesses and societies forward. They are propelled by passion and committed to share their knowledge & experience with an interdisciplinary approach. They are driven to involve communities worldwide in the AI journey, stimulate information flow across borders and disciplines, and while building trust, play a crucial role in demystifying AI.

The current network of AI Ambassadors is spread out on six continents and consists of experts with an average of twenty years of experience in AI. The power of the network of AI Ambassadors lies in collaboration – reducing misconceptions, spotlighting applications, potentials and challenges and driving the development forward together to the point where the limitation of this technology is only our imagination.

Find out who these experts are! Connect with them on social media and get practical, realistic and un-hyped insights with them into the world of AI.

“The power of the network of AI Ambassadors lies in collaboration – reducing misconceptions, spotlighting applications, potentials and challenges and driving the development forward together to the point where the limitation of this technology is only our imagination.”

Global AI Ambassadors 2022
Aleksandra Przegalinska, Angelica Sirotin, Ann Aerts, Antonio Russo, Aruna Pattam, Ashley Casovan, Carolina Pinart, Christian Guttmann, Claire Matuka, Clara Langevin, Craig Ganssle, Daniel Angerhausen, David Meza, Enrico Molinari, Erik Nygren, Eva Schönleitner, Ganesh Padmanabhan, Heinz V. Hoenen, Irakli Beridze, Jacques Ludik, Jayant Narayan, John Kamara, Marisa Tschopp, Muhannad Alomari, Nancy Nemes, Ngozi Bell, Pascal Bornet, Ria Persad, Ricardo Chavarriaga, Sophie Achermann, Steffen Konrath, Tania Peitzker, Thomas Helfrich, Tom Allen, Umberto Michelucci, Utkarsh Amitabh, Utpal Chakraborty, Alexandra Ebert, Andeed Ma, Chad Osorio, Frida Polli, Giselle Mota, Jeff Winter, Johan Steyn, Kim Dressendoerfer, Leila Toplic, Natasja Ludik, Bret Greenstein, Andreas Welsch, Giuliano Liguori

Countries: Australia, Austria, Brazil, Canada, Germany, India, Italy, Kenya, Netherlands, Philippines, Poland, Singapore, South Africa, Spain, Sweden, Switzerland, United Kingdom, United States

Continents: Africa, North America, South America, Europe, Asia, Australia,

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SwissCognitive Guest Blogger: Health Tech World

When it comes to the impact of AI and big data on global healthcare, one of the most sceptical is John Taysom, NED & Co-Founder, Privitar, a data privacy specialist software engineer.

During a recent rigorous roundtable debate orchestrated by Health Tech World, Taysom laid down the gauntlet to the aficionados of AI and Big Data in one fell swoop, comparing the promise of artificial intelligence with the much-discredited Holy Roman Empire.

‘You may recall that it was neither holy, nor Roman nor an empire. Artificial intelligence is neither artificial nor intelligent!’

That set the tone for Taysom’s excoriating shredding of many an AI promise.

Unstable systems

‘Artificial intelligence is just an application of probability and it’s just statistics in a tuxedo. The minute people forget that, and imagine it has some kind of animus that is above and beyond that is a fatal point.’

To buttress his caustic assertion, Taysom referred to a quote from a professor at Cambridge University’s Department of Applied Mathematics and Theoretical Physics lambasting many AI systems as ‘unstable’. ‘This is a major liability, especially as they are increasingly used in high-risk areas such as disease diagnosis.’

Chaired by Alastair MacColl, ‘AI & Big Data – A Bright New Future in Diagnosis’ brought together global experts in the field to unpick the critical threads of the future application of these technologies.

Though not as sceptical as his fellow roundtable contributor, Gil Bashe, Managing Partner and Chair Global Health at Finn Partners, queried the accuracy of the term ‘artificial intelligence’.

‘If it were called adjunctive intelligence or composite IQ we might have a better chance of embracing it both from a clinician and a consumer standpoint.’

Bashe pointed to the lack of what he called ‘composite wisdom’ in tackling the biggest killers of humanity: non-communicable diseases such as heart disease, diabetes, cancer, respiratory disease and mental health crises.

‘We don’t use the totality of wisdom available to us. We seem to approach each person who is at risk as an n of 1, a one-off. ‘Composite intelligence’ – what we’re terming in this conversation artificial intelligence – actually could give us the tools to engage people during our health planning visits and speak to them about the trajectory of their wellness, or their lack of wellness.’

Bashe made the bold claim that, in the US, the primary care medicine system has collapsed.

‘It’s become a walk-in clinic and it’s because the consumer no longer feels value and walking into see the primary care physician. They don’t understand what they’re getting [in terms of their] return on investment for their 40 minutes: Waiting in the waiting room, and then waiting in a little office to see a doctor for nine minutes if that visit were worth something.’

To illustrate his point, Bashe revealed he contracted Covid-19 last September. ‘I was vaccinated. I was in the Ukraine and I came back to the ‘States and, lo and behold, I had Covid and I had to go to the hospital.’ My pulse oxygen level was about 88. I didn’t feel any risk, but I felt no, I’d better have it checked out.

‘Three doctors in succession gave me three different answers. This is one area where artificial intelligence – which is not ‘artificial’, it’s organic intelligence – can be brought together to some extent. Best practice medicine will be guided in the future by data being joined together.’

According to Bashe, in the patient/doctor nexus, doctors simply have to give an answer. ‘It is a knee-jerk reaction. Artificial intelligence is not instead of doctors. It is actually almost like what we called ‘The Merck Manual’, which is published every four years and contains the compendium of medical wisdom. Then suddenly it had to be published almost every other year – and then it had to be re-published digitally almost every other day. That’s because we’re accumulating [data] faster and faster.’

Bashe went on to argue that many clinicians feel threatened by the prospect of AI, comparing it to a ‘collective IQ’ being dumped in the room with them. ‘Doctors are psychologically trained to give answers, and they even have a word for when they don’t have an answer if you have pain and they can’t explain it. It’s called ‘idiopathic’ pain. It’s a great diagnosis, right?’

John Taysom at “AI & Big Data – A Bright New Diagnosis?” Roundtable

Bashe echoed these sentiments in relation to surgeons’ innate belief that they are omniscient. ‘They don’t need to know anything. What they know, they know. That’s a problem psychologically. We need to deal with the doctors’ desire to make decisions, but actually learn to question their decisions. They need a little bit of Taysom embedded in their mindset!’

Bashe did, however, concede that certain medical organisations were already receptive to AI’s potential, given their reliance on real-time data. AI-enabled systems can, of course, read and synthesise massive amounts of data.

According to Bashe, the American College of Cardiology, the European Society of Cardiology and the American Heart Association have always been ‘obsessed’ with data and patterns.

‘They’re probably more adaptable to artificial intelligence. They’ve always been very obsessed with data and looking at data connections, even making new connections. So artificial intelligence is an accelerator of scientific change.’

Money matters

Simon Legge, Managing Director at Tyson & Blake, brought 25 years’ Wall Street banking experience to the roundtable. He raised the tantalising prospect of compelling returns on investment from the implementation of what he called ‘advanced analytics’.

Legge pointed to a McKinsey estimate that for the average global top 20 pharmaceutical company, the application of advanced analytics will deliver a value add of US$300m per annum.

‘Now that’s significant commercial value and that’s what it comes down to. Just as in drug development on average we’re talking about development costs in excess of a billion dollars per drug. If we are talking about approaches, if we’re talking about innovation, we’re talking about software, we’re talking about AI.

‘That adds value, that helps reduce cost and risk in the process. Well then it has significant commercial value. And the thing about capitalism that’s often misunderstood is it’s only if it has commercial value, if it’s value is recognised will it attract capital to support it.’

To bolster his argument, Legge highlighted the pioneering work performed at the Karolinska Institute in Stockholm developing algorithms that can identify the potential for adverse drug reactions in patients.

Said Legge: ‘If you look globally, millions of people are dying every year from adverse drug reactions – its the top 10 cause of death for one of them. That’s completely avoidable. Again, it’s down to the question of how we get this software, these algorithms that have been developed and that have been proven, in a form that can be embedded and implemented into existing legacy systems?’

Terminator trepidation

The ‘rise of the robots’ conjures up dystopian images of hyper-intelligent machines out-thinking humans then maliciously turning on their creators; Think Terminator meets I, Robot. 

Sci-fi author Isaac Asimov’s Three Laws of Robotics mandates that robots must obey humans and do no harm. But fears persist that artificially-intelligent devices pose a direct threat to humanity. And in healthcare, that threat can be framed as an existential one.

John Murray, Academic Dean, Sunderland University gave the debate added impetus with an exploration of how we reassure people that AI and machine learning can solve a lot of the world’s healthcare problems while being aware of the myriad ethical issues such applications raise.

‘With doctors. They talk about patients and problems and diseases. They’re going through thought processes and they’re explaining their thought processes with each other. They’re explaining how they come to a decision or how they come to a conclusion or how and why they think a particular disease might have might have happened or what the solution might be. With AI and machine learning, we put all our data into the box and we get an answer out and it’s simply what’s going on inside one of those [human] decisions.’

Murray claimed that teaching physicians about artificial intelligence and its benefits is a more compelling proposition than having them regard it as yet another system to learn. ‘With their feedback we can improve our systems and platforms. I think that’s very critical.’

The theoretical physicist Stephen Hawking was worried about the ‘singularity’, a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilisation.

Murray counters this anxiety with down-to-earth healthcare needs. ‘You need AI and people and doctors – and they need to work in symbiosis together to help us understand the data. AI can manage and understand and interpret data at much faster rates than any human could ever do and it helps us inform our decisions rather than potentially make those decisions for us. It’s not going to replace as I think anytime soon.’

The Sunderland academic adds a worrying caveat, though: Litigation.

‘There’s also the other side of the ethics as well. And that’s litigation, which I think is a real challenge of this problem. In recent years I’ve worked with some big UK medical organisations looking at AI solutions to problems. And the question I always get is from their legal team. And that is: what if it makes the wrong decision? What if it gives us advice that is defective? We’re still in the infancy of this technology in a lot of areas and it’s still stuff we have to work through and understand as a society.’

Throwing yet another spanner in the works, Privitar’s Taysom underscored the absence of time factors from machine learning’s processes. ‘Ask machine learning people how they deal with ‘autocorrelation’. Most of them look at you with utter mystery. They don’t know what you’re talking about.

‘But it’s a fact that time as a variable is dealt with broadly in machine learning as if it was any other variable. But how can you possibly expect to determine cause and effect unless you recognise that there is a time component to that expression?’

Taysom did, however, concede a proven benefit to machine learning in healthcare. One of his UK firms had just secured FDA approval for the application of machine learning to the diagnosis of small node cancer of the lung. ‘So that’s an example where an application of machine learning can directly impact economics, both reducing the impact on the patient – and also on society.’

Just goes to show that even sceptics can have a change of heart.

Roundtable participants were:

Gil Bashe, Managing Partner and Chair Global Health at Finn Partners
Simon Legge, MD at Tyson & Blake
John Murray, Academic Dean, Sunderland University
Aman Bhatti, Senior Vice President, Global BioPharma, AliveCor
John Taysom, NED & Co-Founder, Privitar
Dr Ofer Sharon, CEO OncoHost

The full debate can be found at:

(262) AI & Big Data – A Bright New Diagnosis? Roundtable – YouTube

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Predictive Maintenance is by far the most widely implemented use case in the industry, but also may the most frequently failed use case.

SwissCognitive Guest Blogger: Martin Rugfelt, CEO sentian.ai & Ivan Jursic, Research & Development, Jumo

The reasons are many, but the most important reasons are robust machines that do not fail frequently and consistently enough to create patterns, and in addition most companies have not got the data necessary to make predictive maintenance a success. The result is in most cases that companies resort to anomaly detection, which as an unsupervised method is much harder to succeed with. So it is time for the industry to move beyond predictive maintenance.

Adding AI to industrial control systems opens wide opportunities to reduce cost, scrap and greenhouse emissions while improving quality and yield. So the opportunities are many. Current control system architecture is based on technology from the last century with PLCs, SCADA and DCS systems. They are generally not cloud connected or offered as a Service but provide an excellent data source for AI solutions. For higher value processes and more complicated control challenges many companies have opted for APCs. Again, generally not cloud connected or offered as a service and also from last century.

AI or predictive analytics is offering a new generation of opportunities to optimize the control systems. As the AI uses data from the system they generally cost less to develop and is faster to implement than APCs. Another benefit with AI is that they learn continuously while APCs tend to deteriorate in performance over time as they don’t adapt to changing dynamics beyond their programming. We are also seeing AI products being developed that target parts of the value chain offering even quicker time to market for the industry as well as cloud-based software as a service solutions.

A practical example is JUMO who implemented a solution in the manufacturing process of platinum thin film temperature sensors to improve quality and yield. The goal was to control a process early in the process chain which determines the resistance of the final product. History showed that the process variations between these points led to problems with the yield of sensors at the final measurement. The interactions at all process steps were too complex and some of them not fully understood, so they could not be taken into account to properly compensate for the variations. Despite having seen several data science challenges such as limited data, time lags in measurements, and some process unknowns the project was a success with 10 percentage points improvement in the yield of sensors with the highest accuracy class.

Control systems are the backbone of industrial production and are everywhere both in the process and manufacturing industry. The examples of where AI can boost basic control systems stretch across basically all industrial production. Illustrative examples are; reducing energy and environmental footprint in the chemical industry by optimizing the furnace process, using AI to optimize the cooking process in pulp production where it not only reduces energy but also the use of chemicals, and optimizing the robotic manufacturing in discrete cell-based production.

Looking closer at these we can see that they have all got data science challenges. AI is in particular interesting where there are unknowns, as in the cooking process for the pulp and paper industry, where the AI can find relevant patterns. Cooking is a process that is not possible to understand exactly what is going on inside the process. Operational experts together with the control systems can be supported by advanced AI to optimize the control. Over time many processes can even become autonomous.

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About the Authors:

Martin Rugfelt is an entrepreneur that has spent the last 14 years of AI. Martin is passionate about startups and has been or is involved, as an advisor or board member in several startups working in as diverse AI fields as predictive healthcare, financial AI, image recognition, natural language and text generation. Martin is the founder and CEO of Industrial AI company Sentian.ai. He is also a self described foodie with a passion for gilling over open fire and winter pizzas in the cold of Sweden.

Ivan Jursic has a background in condensed matter physics and optics. He has a PhD in experimental physics. During the postdoc time he was a Researcher at the German National Measurement Institute (PTB). The focus of the research was on the ageing effects in high temperature thermometers for the next generation of power plants. After a stint at the University of Applied Science in Lemgo he went to JUMO. At JUMO he is in the R&D Department. The area of work includes development of thin film platinum resistance temperature sensors and the processes involved in the manufacturing of the sensors. The work of Ivan is driven by the desire to understand how the world functions from a sub-atomic level to every day life and beyond.

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Martin Rugfelt

CEO
Sentian.ai

Ivan Jursic

Research & Development
Jumo

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80% of typical organizations’ data is unstructured, slowing down work, often leading to less-than-optimal decision-making, according to an Accenture study published earlier this year.

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Der Beitrag How A Startup Is Using AI To Help You Find The Market Research You Need erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.