For the past several years, the World Economic Forum (WEF) has published an annual list of the Top Ten Emerging Technologies that would be potentially disruptive over the next three to five years while also providing significant benefits to economies and societies. Starting in 2017, the Top Ten list has been chosen in collaboration with Scientific American and published in an issue of the magazine.
The Top Ten Emerging Technologies 2018 was released in September, 2018. The final list was selected by a steering group of experts from an initial list with more than 50 submissions. Here are the 10 technologies comprising the 2018 list, along with the reasons cited in the report for their selection:
Advanced Diagnostics for Personalized Medicine - “Advanced diagnostic tools are set to tailor your medicines to you, detecting and quantifying multiple signs of a disorder to decide how likely you are to contract a disease.”
AI for Molecular Design - “The days of science relying on educated predictions - or guesses - to create new drugs and materials may become a thing of the past as artificial intelligence… will help the pharmaceutical industry identify and develop new drugs at a rapid pace.”
AI that Can Argue and Instruct - “[Y]ou’ll soon be able to access far more sophisticated digital aides. Powered by AI, the latest technology will mine the cloud and outline various arguments on topics that are important to you, without prior training.”
Implantable Drug-Making Cells - “Now the technology is sophisticated enough to work without being rejected by the immune system and could transform the treatment of long-term conditions, such as cardiovascular disease, tuberculosis, diabetes, cancer and chronic pain.”
Lab-Grown Meat - “Meat grown from cultured cells could cut the environmental costs of producing meat and eliminate the unethical treatment suffered by animals that are raised for food.”
Electroceuticals - Electroceuticals offer “the ability to treat ailments using electrical impulses. One approach, targeting the vagus nerve - the system that sends signals from the brain to most organs - is poised to transform care for many conditions, since it has the potential to regulate the immune system.”
Gene Drive - “[G]ene drives - natural or engineered genetic elements that spread through populations quickly… - offer enormous power to fight disease or eliminate species of pests such as mosquitoes that transmit malaria.”
Plasmonic Materials - [P]lasmonic devices that manipulate electron clouds and light at the nanoscale are set to increase magnetic memory storage and the sensitivity of biological sensors… Light-activated nanoparticles are also being investigated for their ability to treat cancer without damaging healthy tissue.”
Algorithms for Quantum Computers - “Computers that use quantum mechanics to perform calculations can solve some problems far more efficiently than a conventional computer… a growing number of academics are developing programs and quantum software.”
The full report includes a longer, one-page description of each of these technologies. Let me briefly discuss the two AI-based technologies: AI for Molecular Design and AI that Can Argue and Instruct.
AI-based Molecular Design
A 2017 paper, The Impact of Artificial Intelligence on Innovation, argued that beyond its growing list of applications, - from machine translation to predictions of all kinds, - AI is also becoming a new kind of research tool which will potentially reshape the very nature of innovation and R&D. Machine learning may be able to expand the set of problems that can be feasibly addressed through automation, lowering the costs of discovery across broad set of domains where classification and predictions play a major role. Molecular design is one such domain.
“Want to design a new material for solar energy, a drug to fight cancer or a compound that stops a virus from attacking a crop?,” asks the 2018 Top Ten report. “First, you must tackle two challenges: finding the right chemical structure for the substance; and determining which chemical reactions will link up the right atoms into the desired molecules or combinations of molecules. Traditionally, answers have come from sophisticated guesswork aided by serendipity. The process is extremely time-consuming and involves many failed attempts… Now, though, AI is starting to increase the efficiency of both design and synthesis, making the enterprise faster, easier and cheaper while reducing chemical waste.”
Machine learning algorithms are now able to analyze past experiments that attempted to find new drugs and material, - both those that worked and those that didn’t - and predict which new molecular structures are likely to work best. The selected predictions can then be further validated by the researchers involved.
Pharmaceutical companies generally store millions of compounds to be screened as potential new drugs, a process that’s slow and yields relatively few hits. Moreover, these libraries collectively include a tiny fraction of the more than 1030 theoretically possible molecules. Machine learning tools can not only rapidly search these libraries, but can also generate virtual libraries of new compounds that have similar properties to the most promising molecules. In addition, machine learning can flag those drugs and materials that while promising, may also be accompanied by potentially harmful risks and side effects.
Protein structure prediction is one of the most important problems in drug design, as well as being one of the most complex problems in bioinformatics. Every two years since 1994, the performance of current methods is assessed in a community-wide experiment and competition, the Critical Assessment of protein Structure Prediction or CASP. The latest CASP meeting took place in Cancun, Mexico in December, 2018. As this recent NY Times article noted, the contest was not won by any of the academic research teams, but by DeepMind, the AI startup founded in 2010 and acquired by Google in 2014. DeepMind gained global notoriety in 2016 after its AlphaGo program, based on advanced deep learning algorithms, defeated one of the top professional Go players.
“A growing number of companies are applying similar methods to other parts of the long, enormously complex process that produces new medicines”, said the NYT article. “These A.I. techniques can speed up many aspects of drug discovery and, in some cases, perform tasks typically handled by scientists… ‘It is not that machines are going to replace chemists,” said Derek Lowe, a longtime drug discovery researcher… ‘It’s that the chemists who use machines will replace those that don’t.’”
More Capable AI-based Digital Helpers
AI-based virtual assistants, - e.g., Siri, Alexa, Google Assistant, Microsoft Cortana, - have come a long way over the past few years. When they first came to market, they were able extract a question or command using natural language processing and then do a limited set of useful things, such as getting driving directions, looking for a restaurant, placing a phone call, and searching the web.
Open AI Ecosystems was selected as one of the 2016 Top Ten Technologies because virtual assistants were becoming much more capable. Previously, these assistants were largely oblivious to the real life details of a person’s work, life, health, preferences, likes and dislikes. The key missing ingredient was context. Instead of an isolated piece of software, the assistants were now part of an AI ecosystem, giving them the ability to connect to mobile device, personal computers, cloud-based services and IoT devices, and through them to emails, text messages, contacts, financial information, and other kinds of data. Such a contextual intelligence has led to the development of more powerful human-like virtual assistant.
Virtual assistants made the 2018 Top Ten list because new algorithms are now enabling them to learn any topic well enough to act on it as well debate it. The next generation of such systems will be able mine the cloud, outline its various topics and arguments, and “absorb and organize unstructured data (raw text, video, pictures, audio, emails and so on) from myriad sources and then autonomously compose cogent advice - or debate an opponent - on a subject they have never been trained to handle… Such systems could, for instance, help physicians to quickly find research relevant to a complex case and then debate the merits of a given treatment protocol.”
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