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In the upcoming age of AI, two very different classes of companies appear well-positioned to leverage AI’s capabilities: startup ventures and multi-billion-dollar giant corporations. Promising AI startups are being launched at an increasing pace in areas like health care, finance, retail, media and cross-industry tech, to name a few. And alongside tech giants like Google or Microsoft, traditional large corporations are employing AI to digitalize their business model and processes. Examples of AI-driven automation and augmentation range from automated customer loan approval and smart infotainment systems at car manufacturer Daimler to predictive maintenance at oil and gas behemoth Shell and AI-assisted medical image reading at industrial manufacturer Siemens. Corporate AI innovation is fairly concentrated with the top-10 patenting firms in the world accounting for more than 15% of AI patents in the period 2011 to 2016.

These two breeds of companies — startups and giants — are also building strong partnerships in the field of AI. A recent study reveals that, while in 2013, AI startups were rarely targeted by corporate venture capital (CVC) investment, only five years later these AI startups received more than $5 billion in CVC funding (approx. 10% of all CVC investments). While much of this money is coming from Asian and U.S. tech giants, like Baidu and Google, big non-digital corporations are increasingly making such investments to access startups’ AI talent. Big data and AI talent (e.g., data scientists, machine learning engineers) are two of the most critical resources for building successful AI applications. By combining the innovative talent of AI startups with the vast amounts of process and user data held by giant corporations, strong synergies can be created.

In this field, midsize companies — many of whom are family-controlled — have difficulties keeping up. Earlier research documented how midsize firms were already struggling in last decade’s winner-takes-all economy. That struggle is likely only going to intensify.

The article was originally published at the Harvard Business Review.

The Boston Global Forum (BGF), in collaboration with the United Nations Centennial Initiative, released a major work entitled Remaking the World – Toward an Age of Global Enlightenment.   More than twenty distinguished leaders, scholars, analysts, and thinkers put forth unprecedented approaches to the challenges before us. These include President of the European Commission Ursula von der Leyen, Governor Michael Dukakis, Father of Internet Vint Cerf, Former Secretary of Defense Ash Carter, Harvard University Professors Joseph Nye and Thomas Patterson, MIT Professors Nazli Choucri and Alex ‘Sandy’ Pentland, and European Parliament Member Eva Kaili.  The BGF introduced core concepts shaping pathbreaking international initiatives, notably, the Social Contract for the AI Age, an AI International Accord, the Global Alliance for Digital Governance, the AI World Society (AIWS) Ecosystem, and AIWS City.

Artificial intelligence-enabled health applications for diagnostic care are becoming widely available to consumers; some can even be accessed via smartphones. Google, for instance, recently announced its entry into this market with an AI-based tool that helps people identify skin, hair, and nail conditions. A major barrier to the adoption of these technologies, however, is that consumers tend to trust medical AI less than human health care providers. They believe that medical AI fails to cater to their unique needs and performs worse than comparable human providers, and they feel that they cannot hold AI accountable for mistakes in the same way they could a human.

This resistance to AI in the medical domain poses a challenge to policymakers who wish to improve health care and to companies selling innovative health services. Our research provides insights that could be used to overcome this resistance.

In a paper recently published in Nature Human Behaviour, we show that consumer adoption of medical AI has as much to do with their negative perceptions of AI care providers as with their unrealistically positive views of human care providers. Consumers are reluctant to rely on AI care providers because they do not believe they understand or objectively understand how AI makes medical decisions; they view its decision-making as a black box. Consumers are also reluctant to utilize medical AI because they erroneously believe they had better understand how humans make medical decisions.

The article was originally published at the Harvard Business Review.

The Boston Global Forum (BGF), in collaboration with the United Nations Centennial Initiative, released a major work entitled Remaking the World – Toward an Age of Global Enlightenment.   More than twenty distinguished leaders, scholars, analysts, and thinkers put forth unprecedented approaches to the challenges before us. These include President of the European Commission Ursula von der Leyen, Governor Michael Dukakis, Father of Internet Vint Cerf, Former Secretary of Defense Ash Carter, Harvard University Professors Joseph Nye and Thomas Patterson, MIT Professors Nazli Choucri and Alex ‘Sandy’ Pentland, and European Parliament Member Eva Kaili.  The BGF introduced core concepts shaping pathbreaking international initiatives, notably, the Social Contract for the AI Age, an AI International Accord, the Global Alliance for Digital Governance, the AI World Society (AIWS) Ecosystem, and AIWS City.

Determining the 3D shapes of biological molecules is one of the hardest problems in modern biology and medical discovery. Companies and research institutions often spend millions of dollars to determine a molecular structure – and even such massive efforts are frequently unsuccessful.

Using clever, new machine learning techniques, Stanford University PhD students Stephan Eismann and Raphael Townshend, under the guidance of Ron Dror, associate professor of computer science, have developed an approach that overcomes this problem by predicting accurate structures computationally.

Most notably, their approach succeeds even when learning from only a few known structures, making it applicable to the types of molecules whose structures are most difficult to determine experimentally.

Their work is demonstrated in two papers detailing applications for RNA molecules and multi-protein complexes, published in Science on Aug. 27, 2021, and in Proteins in December 2020, respectively. The paper in Science is a collaboration with the Stanford laboratory of Rhiju Das, associate professor of biochemistry.

The algorithm designed by the researchers predicts accurate molecular structures and, in doing so, can allow scientists to explain how different molecules work, with applications ranging from fundamental biological research to informed drug design practices.

The article was originally published at Stanford.

The Boston Global Forum (BGF), in collaboration with the United Nations Centennial Initiative, released a major work entitled Remaking the World – Toward an Age of Global Enlightenment.   More than twenty distinguished leaders, scholars, analysts, and thinkers put forth unprecedented approaches to the challenges before us. These include President of the European Commission Ursula von der Leyen, Governor Michael Dukakis, Father of Internet Vint Cerf, Former Secretary of Defense Ash Carter, Harvard University Professors Joseph Nye and Thomas Patterson, MIT Professors Nazli Choucri and Alex ‘Sandy’ Pentland, and European Parliament Member Eva Kaili.  The BGF introduced core concepts shaping pathbreaking international initiatives, notably, the Social Contract for the AI Age, an AI International Accord, the Global Alliance for Digital Governance, the AI World Society (AIWS) Ecosystem, and AIWS City.

When it comes to managing artificial intelligence, there is no shortage of principles and concepts aiming to support fair and responsible use. But organizations and their leaders are often left scratching their heads when facing hard questions about how to responsibly manage and deploy AI systems today.

That’s why, at the U.S. Government Accountability Office, we’ve recently developed the federal government’s first framework to help assure accountability and responsible use of AI systems. The framework defines the basic conditions for accountability throughout the entire AI life cycle — from design and development to deployment and monitoring. It also lays out specific questions to ask, and audit procedures to use, when assessing AI systems along the following four dimensions: 1) governance, 2) data, 3) performance, and 4) monitoring.

As organizations, leaders, and third-party assessors focus on accountability over the entire life cycle of AI systems, there are four dimensions to consider: governance, data, performance, and monitoring. Within each area, there are important actions to take and things to look for.

Assess governance structures. A healthy ecosystem for managing AI must include governance processes and structures. Appropriate governance of AI can help manage risk, demonstrate ethical values, and ensure compliance. Accountability for AI means looking for solid evidence of governance at the organizational level, including clear goals and objectives for the AI system; well-defined roles, responsibilities, and lines of authority; a multidisciplinary workforce capable of managing AI systems; a broad set of stakeholders; and risk-management processes. Additionally, it is vital to look for system-level governance elements, such as documented technical specifications of the particular AI system, compliance, and stakeholder access to system design and operation information.

Understand the data. Most of us know by now that data is the lifeblood of many AI and machine-learning systems. But the same data that gives AI systems their power can also be a vulnerability. It is important to have documentation of how data is being used in two different stages of the system: when it is being used to build the underlying model and while the AI system is in actual operation. Good AI oversight includes having documentation of the sources and origins of data used to develop the AI models. Technical issues around variable selection and use of altered data also need attention. The reliability and representativeness of the data needs to be examined, including the potential for bias, inequity, or other societal concerns. Accountability also includes evaluating an AI system’s data security and privacy.

Define performance goals and metrics. After an AI system has been developed and deployed, it is important not to lose sight of the questions, “Why did we build this system in the first place?” and “How do we know it’s working?” Answering these important questions requires robust documentation of an AI system’s stated purpose along with definitions of performance metrics and the methods used to assess that performance. Management and those evaluating these systems must be able to ensure an AI application meets its intended goals. It is crucial that these performance assessments take place at the broad system level but also focus on the individual components that support and interact with the overall system.

Review monitoring plans. AI should not be considered a “set it and forget it” system. It is true that many of AI’s benefits stem from its automation of certain tasks, often at a scale and speed beyond human ability. At the same time, continuous performance monitoring by people is essential. This includes establishing a range of model drift that is acceptable, and sustained monitoring to ensure that the system produces the expected results. Long-term monitoring must also include assessments of whether the operating environment has changed and to what extent conditions support scaling up or expanding the system to other operational settings. Other important questions to ask are whether the AI system is still needed to achieve the intended goals, and what metrics are needed to determine when to retire a given system.

The original article was published at the Harvard Business Review.

The Boston Global Forum (BGF), in collaboration with the United Nations Centennial Initiative, released a major work entitled Remaking the World – Toward an Age of Global Enlightenment.   More than twenty distinguished leaders, scholars, analysts, and thinkers put forth unprecedented approaches to the challenges before us. These include President of the European Commission Ursula von der Leyen, Governor Michael Dukakis, Father of Internet Vint Cerf, Former Secretary of Defense Ash Carter, Harvard University Professors Joseph Nye and Thomas Patterson, MIT Professors Nazli Choucri and Alex ‘Sandy’ Pentland, and European Parliament Member Eva Kaili.  The BGF introduced core concepts shaping pathbreaking international initiatives, notably, the Social Contract for the AI Age, an AI International Accord, the Global Alliance for Digital Governance, the AI World Society (AIWS) Ecosystem, and AIWS City.

Teachers are one of the professions least at risk of being automated. A job that requires emotional intelligence and flexibility — in adapting lesson plans on-the-fly to a particular group of children, for example — is not one particularly well-suited to robots. Artificial intelligence, however, still deserves a role in the classroom. This is not as a replacement for teachers, but as a tool or an assistant that can aid them both in trying to close the gap in achievement between the rich and the poor and in making up for lost teaching time during the pandemic.

In the UK, school closures to stop the spread of coronavirus have helped to accelerate the adoption of AI-based learning platforms as teachers sought new online methods to keep students engaged and to track their progress. These new approaches, where they have been seen to work during the pandemic, will continue when pupils return to something that looks more like a normal education.

The right approach to such technologies is to see them not as substitutes for human labour, but as a way of improving its quality and productivity. Machine learning may be more sophisticated than a shovel but the principle is the same: tools enhance humans’ existing capabilities. The best AI applications promise to improve and accelerate teachers’ knowledge of what their students need, how best to deliver that teaching, and which students require the most help — from a human teacher — to keep up with their learning.  This is not unique to education. In the medical field, for example, AI promises to improve diagnostics, saving doctors’ time that is better spent on treatment and engagement with patients. The same potential should be sought across the economy. Eliminating tasks rather than jobs can make human roles both more productive and more rewarding.

That will take smart policy support. Those drawing up public sector budgets should resist the temptation to use AI as a quick fix to cut staff and economize on the wage bill. Increased productivity should be targeted in the form of higher quality output, not lower costs by replacing expensive workers with cheaper machines. In this respect, there is something special about education. The economic returns of learning in early life — for pupils themselves and for society as a whole — are now known to be enormous, and greater the earlier in life one starts. Poor and uneven schooling, leaving too many children behind, is one of the deepest roots of inequality and low productivity. If AI can help target existing teaching resources better, the benefits could be incalculable.

The original article was published at the Financial Times.

The Boston Global Forum (BGF), in collaboration with the United Nations Centennial Initiative, released a major work entitled Remaking the World – Toward an Age of Global Enlightenment.   More than twenty distinguished leaders, scholars, analysts, and thinkers put forth unprecedented approaches to the challenges before us. These include President of the European Commission Ursula von der Leyen, Governor Michael Dukakis, Father of Internet Vint Cerf, Former Secretary of Defense Ash Carter, Harvard University Professors Joseph Nye and Thomas Patterson, MIT Professors Nazli Choucri and Alex ‘Sandy’ Pentland, and European Parliament Member Eva Kaili.  The BGF introduced core concepts shaping pathbreaking international initiatives, notably, the Social Contract for the AI Age, an AI International Accord, the Global Alliance for Digital Governance, the AI World Society (AIWS) Ecosystem, and AIWS City.