Editor

When COVID-19 struck Europe in March 2020, hospitals were plunged into a health crisis that was still badly understood. “Doctors really didn’t have a clue how to manage these patients,” says Laure Wynants, an epidemiologist at Maastricht University in the Netherlands, who studies predictive tools.

But there was data coming out of China, which had a four-month head start in the race to beat the pandemic. If machine-learning algorithms could be trained on that data to help doctors understand what they were seeing and make decisions, it just might save lives. “I thought, ‘If there’s any time that AI could prove its usefulness, it’s now,’” says Wynants. “I had my hopes up.”

It never happened—but not for lack of effort. Research teams around the world stepped up to help. The AI community, in particular, rushed to develop software that many believed would allow hospitals to diagnose or triage patients faster, bringing much-needed support to the front lines—in theory.

In the end, many hundreds of predictive tools were developed. None of them made a real difference, and some were potentially harmful.

That’s the damning conclusion of multiple studies published in the last few months. In June, the Turing Institute, the UK’s national center for data science and AI, put out a report summing up discussions at a series of workshops it held in late 2020. The clear consensus was that AI tools had made little, if any, impact in the fight against covid.

The article was published at MIT Technology 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.

The National Science Foundation (NSF) announced today that it was funding 11 new National Artificial Intelligence (AI) Research Institutes. The latest grants follow a first round of seven AI research institutes established in 2020. The new investment totals $220 million and expands the network of these institutes to a total of 40 states and the District of Columbia, according to NSF.

“I am delighted to announce the establishment of new NSF National AI Research Institutes as we look to expand into all 50 states,” said National Science Foundation Director Sethuraman Panchanathan in the agency’s news release. “These institutes are hubs for academia, industry and government to accelerate discovery and innovation in AI. Inspiring talent and ideas everywhere in this important area will lead to new capabilities that improve our lives from medicine to entertainment to transportation and cybersecurity and position us in the vanguard of competitiveness and prosperity.”

NSF is partnering with federal agencies (U.S. Department of Agriculture National Institute of Food and Agriculture, U.S. Department of Homeland Security) and private companies (Google, Amazon, Intel and Accenture) to create the National AI Research Institutes.

The original article was published at Forbes.

To support on positive AI technology and research development, Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) has established Artificial Intelligence World Society Innovation Network (AIWS.net). According to AIWS.net, AI can be an important tool for helping people achieve well-being and happiness, relieve them of resource constraints and arbitrary/inflexible rules and processes. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.

Most of the public discourse around artificial intelligence (AI) policy focuses on one of two perspectives: how the government can support AI innovation, and how the government can deter its harmful or negligent use. Yet there can also be a role for government in making it easier to use AI beneficially—in this niche, the National Science Foundation (NSF) has found a way to contribute. Through a grant-making program called Fairness in Artificial Intelligence (FAI), the NSF is providing $20 million in funding to researchers working on difficult ethical problems in AI. The program, a collaboration with Amazon, has now funded 21 projects in its first two years, with an open call for applications in its third and final year. This is an important endeavor, furthering a trend of federal support for the responsible advancement of technology, and the NSF should continue this important line of funding for ethical AI.

The FAI program is specifically oriented towards the ethical principle of “fairness”—more on this choice in a moment. While this may seem unusual, the program is a continuation of prior government funded research into the moral implications and consequences of technology. Starting in the 1970’s, the federal government started actively shaping bioethics research in response to public outcry following the AP’s reporting on the Tuskegee Syphilis Study. While the original efforts may have been reactionary, they precipitated decades of work towards improving the biomedical sciences. Launched alongside the Human Genome Project in 1990, there was an extensive line of research oriented towards the ethical, legal, and social implications of genomics. Starting in 2018, the NSF funded 21 exploratory grants on the impact of AI on Society, a precursor to the current FAI program. Today, it’s possible to draw a rough trend line through these endeavors, in which the government is becoming more concerned with first pure science, then the ethics of the scientific process, and now the ethical outcomes of the science itself. This is a positive development, and one worth encouraging.

The original article was posted at Brookings Institution.

To support for AI technology and development for social impact, Michael Dukakis Institute for Leadership and Innovation (MDI) and Artificial Intelligence World Society (AIWS.net) has developed AIWS Ethics and Practice Index to measure the ethical values and help people achieve well-being and happiness, as well as solve important issues, such as SDGs. Regarding to AI Ethics, AI World Society (AIWS.net) initiated and promoted to design AIWS Ethics framework within four components including transparency, regulation, promotion and implementation for constructive use of AI. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.

Imagine an orange cat. Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world.

In other words, as humans, it’s easy to envision an object with different attributes. But, despite advances in deep neural networks that match or surpass human performance in certain tasks, computers still struggle with the very human skill of “imagination.”

Now, a USC research team has developed an AI that uses human-like capabilities to imagine a never-before-seen object with different attributes. The paper, titled Zero-Shot Synthesis with Group-Supervised Learning, was published in the 2021 International Conference on Learning Representations on May 7.

“We were inspired by human visual generalization capabilities to try to simulate human imagination in machines,” said the study’s lead author Yunhao Ge, a computer science PhD student working under the supervision of Laurent Itti, a computer science professor.

“Humans can separate their learned knowledge by attributes–for instance, shape, pose, position, color–and then recombine them to imagine a new object. Our paper attempts to simulate this process using neural networks.”

The original article was published at Technology Networks.

To support on positive AI technology and applications, Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) has established Artificial Intelligence World Society Innovation Network (AIWS.net). According to AIWS.net, AI can be an important tool for helping people achieve well-being and happiness, relieve them of resource constraints and arbitrary/inflexible rules and processes. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.

Buying off-the-shelf AI (Artificial Intelligence) software is a good first step for those companies that are new to the technology. There should be little need to make investments in technical infrastructure or to hire expensive data sciences. There will also be the benefit of getting a solution that has been tested by other customers. For the most part, there should be confidence in the accuracy levels as the algorithms will probably be implemented properly.

But when it comes to a new AI solution, it can be tough to find the right sources of data, wrangle it and integrate it. Thus, when evaluating an application, you need to make sure that there are ways to handle this process.

“The most complex task in an AI solution is not to implement the machine learning algorithm anymore—this is usually available as a set of functions in every tool—but to collect the data,” said Rosaria Silipo, a Ph.D. and a principal data scientist at KNIME. “That is, to connect to a variety of data sources, on premise, on the web, or on the cloud, and extract the data of interest.”

This is why it is important to see if the application is built to handle your particular vertical or situation.

“Take search, for instance, where AI can be used to re-rank results and improve relevance,” said Ciro Greco, who is the Vice President of Artificial Intelligence at Coveo. “When applied to ecommerce, search is searching on semi-structured records, such as products with little text available and we can count on reasonable amounts of behavioral data produced by users who browse the website. A strategy based on user behavior can be very effective, because we can count on having enough data to learn from.”

Yet AI-search for customer service use cases is often much different. It’s often about finding technical documents. “There is plenty of unstructured text, such as Knowledge Articles, and fewer behavioral data points, because customer service websites usually are less visited than e-commerce platforms,” said Greco. “So in that case, a strategy based on NLP for topic modeling will probably be more effective, because we need to maximize the gain from the information we have, in this case free text.”

The article was originally posted at Forbes.

To support for AI positive development in business and society, Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) has established Artificial Intelligence World Society Innovation Network (AIWS.net). In this effort, MDI and BGF invite participation and collaboration with governments, think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society. This initiative is to develop positive AI for helping people achieve well-being and happiness, relieve them of resource constraints and arbitrary/inflexible rules and processes, and solve important issues, such as SDGs.

Artificial intelligence (AI) is just a supporting player in the wider world of robotics. Unlike sensationalist sci-fi flicks, robots have had an important role since the earliest days of industrialization. From manufacturing line efficiencies to support in surgery, robots are integral to many core social constructs. The never-actualized dynamic of robots with intuition could finally be on the horizon.

AI, algorithms and technological applications are accelerating into new realms. With the proper implementation of ethics, diversity, and inclusive design – humans stand to benefit significantly.

Tasks that can be automated are easily replaced by forays into new fields, such as alternative energy or tech. One expert puts it this way: “Security robots, including drones, will increase in use and scale within the next decade. As a result, the cost will decrease, and new capabilities will come to light. This technology offers security professionals the unique ability to enhance procedures without replacing human workers.”

Each day, we are watching companies use AI and robotics to achieve good:

• Scientific advancements and breakthroughs
• Solving complex problems
• Generating millions of new skilled jobs in better working conditions

The original article was published at Forbes.

To support AI technology and development for human-centric applications, Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) established Artificial Intelligence World Society Innovation Network (AIWS.net). In this effort, MDI and BGF invite participation and collaboration with governments, think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society. This initiative is to develop positive AI for helping people achieve well-being and happiness, relieve them of resource constraints and arbitrary/inflexible rules and processes, and solve important issues, such as SDGs.

I’m driven by two realities today. First, we’re generating more data than ever before—we’re on track to quadruple the current amount of data by 2025. And second, data is handing us an amazing opportunity to make our world a better place. Data fuels artificial intelligence (AI) technologies that are changing the face of healthcare, climate research, community resilience, and even space. AI can literally save the world.

Look at the role AI has played in our COVID-19 response, from tracing to variant prediction to rapid vaccine development. Consider the breakthroughs in improved cancer diagnostics thanks to AI revealing subtle patterns that humans can’t perceive. Notice the momentum in outer space, where my team is working on AI for autonomous satellite failure detection that will support ground operators as they carry out missions thousands of miles away. We haven’t even touched on how AI is driving climate research.

All these innovations in AI are based on massive amounts of data. Algorithms depend on data to “learn” and produce reliable outputs that we use to better understand our world and devise solutions for some of our greatest challenges. The wealth of data that’s fueling rapid progress, however, is under threat of being cut off if we don’t fix a mega problem in tech: trust.

If we’ve learned anything about digital technologies this past decade, it’s that conversations about ethical practices haven’t kept pace. People are concerned, rightfully so, about how their information is tracked.

The issue has grabbed national attention. Notable documentaries, news articles, and congressional hearings expose our failings to ensure transparency and ethical standards for data collection, AI, and automation. Some of the most prominent issues include unintended bias in algorithms, job replacement, and implications of social media’s influence on our behaviors and information flow.

Now’s the time for us to get smarter—as consumers, technologists, and policymakers—before data abuses undermine the promise of AI. Without this three-pronged approach, we’re eroding trust in the very technology that can help us solve our most urgent issues.

The article was originally published here.

To support for AI Ethics, Michael Dukakis Institute for Leadership and Innovation (MDI) and Artificial Intelligence World Society (AIWS.net) has developed AIWS Ethics and Practice Index to measure the ethical values and help people achieve well-being and happiness, as well as solve important issues, such as SDGs. Regarding to AI Ethics, AI World Society (AIWS.net) initiated and promoted to design AIWS Ethics framework within four components including transparency, regulation, promotion and implementation for constructive use of AI. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.