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As scientists uncover drugs that can treat coronavirus infections, demand will almost certainly outstrip supplies—as is already happening with the antiviral remdesivir. To prevent shortages, researchers have come up with a new way to design synthetic routes to drugs now being tested in some COVID-19 clinical trials, using artificial intelligence (AI) software. The AI-planned new recipes—for 11 medicines so far—could help manufacturers produce medications whose syntheses are tightly held trade secrets. And because the new methods use cheap, readily available starting materials, licensed drug suppliers could quickly ramp up production of any promising therapies.

“If you are going to supply a drug to the world, your starting materials have to be cheap and as available as sugar,” says Danielle Schultz, a chemist at Merck. The new method, posted as a preprint this week, “is really solid,” she says. “I am impressed by the speed at which [the researchers] were able to find new solutions for making existing drugs.”

Patents give pharmaceutical companies the right to be the sole supplier of a new drug in a given country, usually for 20 years. Once a drug goes off patent, other companies can produce and sell it as a generic. The method to make the drug is often secret to discourage competition even after patents expire. But COVID-19 has changed all that, Schultz says. “We are at a time when it’s all hands on deck.”

Only two medicines—remdesivir and dexamethasone—are currently proven to fight COVID-19. That has led to supply shortages for both. On 4 August, attorneys general from 34 U.S. states wrote federal officials, calling remdesivir supplies “dangerously limited,” and urging states be given “march-in rights” to violate owner Gilead Sciences’ patents. Such rights would allow states to work with third-party manufacturers to make additional supplies of the drug.

To prevent future supply crunches, University of Michigan chemist Timothy Cernak and colleagues turned to a commercial drug synthesis AI program called Synthia. The software can help pharmaceutical manufacturers find the most efficient and cost-effective strategy for synthesizing medicines, most of which are fairly complex molecules that can be built in myriad ways—much as an artist can apply brush strokes in infinite combinations to paint the same landscape. “It’s more options than the human mind can comprehend,” Cernak says.

The original article can be found here.

In support of positive AI development for the society, the Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) established the Artificial Intelligence World Society (AIWS.net) in 2017. According to AIWS.net, AI can be an important technology and a potential tool for COVID-19 prediction. In this effort, MDI and BGF invite 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.

In June 2020, OpenAI launched GPT-3 (generative pre-training) that is the third generation of the artificial intelligence (AI) model to create documents in the form of API beta, which has attracted a great attention from the community. GPT-3 is the most powerful language model ever, but it has also raised some concerns.

As we know, OpenAI is a non-profit company working on Artificial Intelligence, which is behind big names like Peter Thiel, Elon Musk, Reid Hoffman, Marc Benioff, Sam Altman etc. OpenAI vision is to develop an AGI (Artificial General Intelligence) system, which capable of understanding or learning any intellectual task that humans can.

GPT-3 is essentially a machine learning system with an input of 45TB of textual data (trained on). The model has 175 billion parameters (the values that a neural network tries to optimize during training). It is useful to note that the entire English data of the Wikipedia network is only 0.6% of the amount of data used for model training in GPT-3. GPT-3 is known as the most powerful language model ever, far surpassing GPT-2 launched in late 2019 with only 1.5 billion parameters. After being trained, the system can create all kinds of smart text content that are no different from humans like a story, an article, a programming code, a poem, a piece of music, just with data input as a few words or sentences. Besides, GPT-3 can produce information that we cannot distinguish between human or written creation.

In particular, GPT-3 allows searching documents based on the natural language meaning of queries instead of just searching through keywords in Twitter. GPT-3 helps improve chatbot by enabling complex, fast and consistent natural language discussions, as well as covering a range of topics, from space travel to history. GPT-3 also helps to provide better customer service with the ability to quickly search, chat, respond and provide customers with relevant information through analysis and semantic search. Moreover, GPT-3 can create productivity tools that allow you to analyze text into excel spreadsheets, summarize email discussions and expand the content from bullet points.

With the powerful language model, GPT-3 is one step closer to AGI, although not perfect. This system seems closer to passing the Turing test than any other system so far. Previous AI systems worked well in narrow fields like gaming, but in many areas there was still a great distance. GPT-3 shows impressive ability in many areas, it can learn and perform tasks when only a few examples, when not pre-programmed, it can play chess and GO (all However, it is not very good as special programs), it can write code for computer programs, maybe even design machine learning models. With the rapid progress from GPT-2 to GPT-3, who knows what GPT-4 can do? Human-level AGI may still be decades away, but the timeline seems to be shortening.

However, GPT-3 raises some concerns on ethical system for future GPT-3 deployment. Is GPT-3 really intelligent, contextual, or understandable with human-level? It is not easy to answer this question because of complex human language in enormous number of contexts and settings. Besides, there is a concern on how to avoid use of GPT-3 for malicious purposes such as creating more fake news like real, or helping children cheat in their homework or exam. Moreover, the GPT-3 requires intensive computing power and its performance improves with each iteration, which are might be costly for future commercial product deployment.

As our technology evolution, we probably remember bitcoin and block chain technology, when people find that bitcoin mining could consume electrical energy by a small nation. GPT-3 and machine learning may be impressive AI technology, but sooner or later we still need to figure out its impact on the society if any large scale deployment.

In support of positive AI development and ethics for the society, the Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) established the Artificial Intelligence World Society (AIWS.net) in 2018. AI can be an important tool to serve and strengthen democracy, human rights, and the rule of law. Its misuse could undermine those ideals. In this effort, 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 AI.

AI World Society (AIWS.net), the Boston Global Forum (BGF), and United Nations (UN) Academic Impact collaborate to organize the United Nations 2045 Roundtable. This is a content of the United Nations 2045 Initiative. Chief of United Nations Academic Impact and Editor-in-Chief of the UN Chronicle Ramu Damodaran will be the moderator of the roundtable. The content of UN Roundtable is an official content of UN Academic Impact and UN Chronicle Magazine; and will be posted on AIWS.net at AIWS Roundtable.

Guest speakers of UN 2045 Roundtable are leaders, distinguished thinkers, and thought leaders that will discuss new ideas, solutions, recommendations, and suggestions to shape a smart, effective, peaceful, and secure world in 2045. The first UN 2045 Roundtable is the UN Charter Day 2045 Roundtable June 26, 2020 with the topic “A New Social Contract in the Age of AI and Intellectual Society”.

On July 24 2020, the United Nations Academic Impact and the Boston Global Forum are very honored to invite Professor Alex Pentland from MIT as a distinguished speaker together with Mr. Ramu Damodaran as an esteemed moderator on the topic “Building A Trustworthy Economy”. Professor Alex Pentland, Faculty Founding Director of MIT Connection Science, Mentor of AI World Society Innovation Network (AIWS.net), and a co-author of the Social Contract 2020. He is one of the most-cited scientists in the world, and Forbes recently declared him one of the “7 most powerful data scientists in the world” along with Google founders and the Chief Technical Officer of the United States.

As we know, today’s financial systems are not trusted by citizens because they are inherently unstable and winner-take-all, so for most people the system offers only failure.  Today, new digital technologies allow the fine-grain feedback needed to build systems that are dramatically more stable, which reward everyone’s contribution to society, and provide everyone with a realistic opportunity to build a good life.

During the keynote presentation, Professor Alex Pentland presented a new term – “the Trustworthy Economy”, which is the new economy, based on data, digital technologies, and Artificial Intelligence (AI). Data becomes more important and is considered as a “new oil” in our current digital society, especially with the development of AI. Data is only valuable in aggregate, and the value depends on the context. However, it also requires data protection for privacy and data management for world society interests. Thus, Professor Alex initiated a new institution as Data Union for digital identity and ownership rights, audit and accountability and criminal system. Data Union could also monitor governments and companies, as well as support local collectives for leading social reform and innovation.

Following to keynote presentation, we have received well-rounded discussions from excellent panelists including Professor Alex Pentland (MIT), Ramu Damodaran (UN), Nguyen Anh Tuan (Co-founder AIWS.net), Bill Ottman (Co-Founder and CEO of Minds.com), Marc Rotenberg (Director of CAIDP, Michael Dukakis Institute), Anders Corr (Principal of Corr Analytics), Marcel Zutter (Chairman of Parsumo Invest), Allan Cytryn (Former CTO of Goldman Sachs), Anders Hjorth Agerskov (Lead Public Sector Specialist, World Bank), Barry Nolan (Senior Adviser to the US Congress), Jeff Saviano (Global Tax Innovation Leader of EY, MIT Connection Science Fellow), Lyndon Haviland (Chair of the Board of Directors for The CUNY Graduate School of Public Health and Health Policy Foundation), Quy Nguyen (a Member of Young Leaders, AIWS.net). During the roundtable, the panelists also shared the opinions and ideas on how to use and manage data for a good purpose because data is complicated and changing, so institution need to control and manage data such as GDPR for privacy data regulation, data audit for avoiding bias and misuse, as well as easy data transfer with the approval. This also requires a collaboration from citizen, corporation, as well as guidelines from government as well as united nation.

The event “Building A Trustworthy Economy” is a successful initiative on Trustworthy Economy and an important contribution for improving life outcomes and better society. The History of AI Initiative considers Trustworthy Economy a History of AI event also. This is very meaningful for the UN 2045 Initiative.