Category Archives: Ai

I find them incredibly irritating. Those images you have to click on to prove that you are not a robot. If you are just one click away from a nice weekend away, you first have to figure out where you can see the traffic lights on 16 tiny fuzzy squares. Google makes grateful use of these puzzling attempts. For one thing, the company uses artificial intelligence to train its image recognition software. Incidentally, patenting this type of software is commonly misunderstood, as it definitely can be patented, contrary to popular opinion.

First of all, lets come back to that recognition hurdle blocking your weekend getaway. Have you ever noticed that in the past, you used to have to recognize texts, but nowadays youre almost only presented with traffic situations? Traffic lights, road signs, pedestrian crossings, cyclists, and so on. Thats not for nothing. The captcha challenge-response texts were intended to improve text scanning for Google Books. Nowadays, Google is fully focused on image recognition for self-driving vehicles. Just a quick note: The official name is reCAPTCHA by the way, and remarkably enough, despite the use of TM, this name is not a protected trademark in Europe.

If you want to master something well, you have got to practice a lot. The same goes for artificial intelligence, i.e., AI. The captcha images are therefore displayed to an enormous number of people. But in order to do this, Google must have enough images at its disposal, which must also be sufficiently different. And there must also be images of rainy situations, falling darkness, or sharp sunlight.

Yet it is precisely these kinds of challenging images that you never see in captchas. Why is that? Very simple: We have software for that. There are programs that routinely make the existing images more complicated, for example by adding noise, different colors, or backlight. All incoming images are automatically edited so that the AI software is presented with more and more difficult training material and learns faster that way. Take a look at patent number EP1418509 to see how those more difficult images are created.

A few weeks ago I was talking about this with a young entrepreneur who makes image recognition software for the education sector. His software also includes smart image editing techniques so that AI systems learn more quickly and better. He was 100 percent convinced that you cannot patent software in Europe, consequently, he could not be held liable for patent infringement whenever he sold his software.

Understandable, because that is a very persistent myth. Its certainly not the first time that I have come across that kind of conviction. But it is not true. Software can also be patented perfectly well in Europe. Just something new and inventive has to be done in a technical way. A smart technique to train AI systems faster and more effectively can also be patented in Europe. The question is whether it is a technical solution to a technical problem that is also new and inventive at the time of applying for a patent.

Should this entrepreneur patent his image recognition software now? I have no idea. Sometimes it is better to keep smart software a secret. But if he wants to license the software or later sell his company to a multinational, a patent application might be worthwhile. But what he definitely has to do is keep a close eye on what others are patenting in this area. So that he can avoid infringing the rights of others and gain inspiration for his own ideas and designs.

By the way, The Netherlands Patent Office has a handy brochure on protecting digital innovations. Check it out. Kijk maar.

About this column

In a weekly column, written alternately byWendy van Ierschot, Eveline van Zeeland, Eugene Franken, Jan Wouters, Katleen Gabriels, Mary Fiers en Hans Helsloot, Innovation Origins tries to figure out what the future will look like. These columnists, occasionally joined by guest bloggers, are all working in their own way on solutions to the problems of our time. So that tomorrow is good. Here are all theprevious articles.

Excerpt from:

On image recognition software, AI, and patents - Innovation Origins

The U.S. needs to take a "strategic approach" as it competes with China on artificial intelligence, according to a Nasdaq executive.

AI is an area that is going to only develop in partnership with government, and U.S. authorities need to get involved, said Edward Knight, vice chairman of Nasdaq.

The Chinese government has already started "investing heavily" and working with their private sector to develop new technologies based on artificial intelligence, he said.

Beijing in 2017 said it wanted to become the world leader in AI by 2030 and aims to make the industry worth 1 trillion yuan ($152 billion). It included a roadmap about how AI could be developed and deployed.

"I think the U.S. already is leading, but it needs more of a strategic approach involving the government," Knight told CNBC's Dan Murphy as part of FinTech Abu Dhabi, which was held online this year. "The private sector alone cannot take on the entire Chinese government and private sector, which is very focused on this."

A U.S. and a Chinese flag wave outside a commercial building in Beijing.

Teh Eng Koon | AFP | Getty Images

Predicting that society will benefit from any innovation that comes from artificial intelligence, Knight added: "If the U.S. is going to continue to be a growing economy and innovative economy, it has to master that new technology."

Artificial intelligence refers to technology in which computers or machines imitate human intelligence such as in image and pattern recognition. It is increasingly being used in sectors from financial services to health care, but has been criticized as being "more dangerous than nukes" by Tesla CEO Elon Musk.

Musk fears that AI will develop too quickly for humans to safely manage, but researchers have pushed back, calling him a "sensationalist."

Separately, Knight weighed in on what a Biden presidency would mean for the initial public offering market.

He said the pipeline traditionally slows down when a new president comes into office because there's uncertainty about possible policy changes.

However, he sees low interest rates and the likelihood of a divided government as positive for the IPO market. "We expect there will not be radical, if you will, changes in public policy," Knight said. "Change will come incrementally, and I think that makes markets more predictable."

We cannot have a strong economy with unhealthy American people. Once we can restore their health and deal with the pandemic, I think you'll start to see the economy fully recover.

Meanwhile, the Federal Reserve this month said it would keep rates near zero for as long as necessary to help the economy recover from the effects of Covid-19.

"With more predictable markets and low interest rates, I think you'll continue to have a healthy demand and pipeline for IPOs," Knight said.

He also said the president-elect's priority is managing the coronavirus crisis and "hopefully getting to the place where we have a widely available vaccine," which would act as a foundation for a recovery.

"We cannot have a strong economy with unhealthy American people," he said. "Once we can restore their health and deal with the pandemic, I think you'll start to see the economy fully recover."

CNBC's Arjun Kharpal, Sam Shead and Catherine Clifford contributed to this report.

Read more:

The U.S. government needs to get involved in the A.I. race against China, Nasdaq executive says - CNBC

In its efforts to increase the footprint of Artificial Intelligence (AI) in the Indian Army and reap its benefits, the Military College of Electronics and Mechanical Engineering (MCEME) held a webinar on Artificial Intelligence Based Prescriptive Maintenance for the Armed Forces on Saturday.

Discerning readers will know that the Armys Training Command based out of Shimla is responsible for the Indian Armys doctrines and training and research development initiatives in all fields.

MCEME has been in the news for advancing the cause of technical research and education even amidst the COVID-19 pandemic and was the proud recipient of the Golden Peacock National Award for Training, an official release said.

The college has made contributions in the field of technical education and meaningful research for the troops fighting on the borders. Some of the notable advances made by MCEME in this niche domain of Artificial Intelligence include fielding a number of AI based field army oriented innovations as well as filing for intellectual property rights for the same.

The webinar was flagged off by General Officer Commanding in Chief Army Training Command, Lt Gen Raj Shukla. It was attended by people from academia and industry, delegates from the Indian Navy and the Indian Air Force as well as scientists and leaders from various defence laboratories.

It provided a rare opportunity to all wherein all major stakeholders got together on a common platform and discussed new ideas for a roadmap for effective AI based Prescriptive Maintenance implementation in the Indian Army demonstrating MCEMEs resolve towards ensuring that the Make in India initiative sees success and it emerges as a dominant player on the AI landscape, it said.

The webinar was conducted over three sessions with focus on the relevance of Artificial Intelligence based Prescriptive Maintenance for the Indian defence forces. Speakers spoke eloquently and in great detail on how the next wave of Artificial Intelligence could revolutionise traditional maintenance paradigms in defence and civil domains.

While the second session saw discussions on use of Internet of Things to enable AI use in prescriptive maintenance, the third session explored the effects and benefits to be derived from the use of cloud technology in prescriptive maintenance.

The rest is here:

MCEME holds webinar on AI - The Hindu

The era of public consultations

In the last 3 years, several intellectual property offices have invited the global IP community to express our views on what we need from them and from legislators. The EPO has been very open consulting on its quality and efficiency initiatives (via SACEPO), an idea for flexible timing of examination, its strategic plan, its Guidelines for Examination, and the Rules of Procedure of its Boards of Appeal. All of WIPO, USPTO, EPO and the UKIPO have been consulting on artificial intelligence and IP.

This is excellent and AA Thornton attorneys have enjoyed the debates including learning from industry experts about your requirements from the global patent system that exists to serve you. For those industry experts who have not yet joined the debate on AI and IP, we call upon you to express your views lets tell the IP offices what you need. Some of the consultations will close very soon.

Blink and you missed it

On 13 November, the EPO Boards of Appeal proposed an amendment to their Rules of Procedure (RPBA) to codify their discretion to hold oral proceedings by videoconference. See https://www.epo.org/law-practice/consultation/ongoing.html

Send your comments to RPBAonlineconsultation@epo.org

The proposed new Article 15a of the Rules of Procedure of the Boards of Appeal (oral proceedings by videoconference) is:

Article 15a Oral proceedings by videoconference (1) The Board may decide to hold oral proceedings pursuant to Article 116 EPC by videoconference if the Board considers it appropriate to do so, either upon request by a party or of its own motion. (2) Where oral proceedings are scheduled to be held in person, the Chair may allow a party, representative or accompanying person to attend by videoconference. In exceptional circumstances, the Chair may decide that a party, representative or accompanying person shall attend by videoconference. (3) The Chair may allow any member of the Board in the particular appeal to participate by videoconference.

The decision for industry is whether to accept this or to recommend that appeal hearings are only held by videoconference if all parties to the proceedings agree.

AA Thornton praised the EPO for investing in its videoconferencing capacity for examination hearings long before the current pandemic. We have found videoconference oral proceedings to be efficient and effective for those proceedings, and we recognize the need to avoid long backlogs at the EPO. However, our support for videoconference hearings is dependent on the EPO maintaining a level playing field for all parties to an opposition, and we do not think this will be achieved if only one party to an opposition appeal is able to attend in person and another is required to attend by videoconference because of the global pandemic and travel restrictions imposed by national governments (for example, this could disadvantage a US patent proprietor whose patent has been challenged by a European competitor).

We therefore recommend revising the second sentence of proposed RPBA Article 15a to make it clear that a hybrid appeal hearing (with some parties physically present and some using videoconference) requires the consent of the parties.

Some attorneys have expressed a view that no appeal hearings should take place by videoconference without the consent of all parties to the appeal proceedings. Other attorneys think ViCos are the best possible answer to the global pandemic. Whatever your views, we encourage industry experts to share them with the EPO.

Saving me from domestic chores this weekend

The consultation which could rescue me from cold hard labour is the UKIPOs consultation on AI and IP, which is mentioned here: https://www.gov.uk/government/news/artificial-intelligence-and-intellectual-property-call-for-views

The consultation closes at 11:45pm UK time on 30 November 2020.

Please send me your recommendations for reply to the UKIPOs patent questions or email AIcallforviews@ipo.gov.uk.

The most fundamental question relating to patents is whether: i. you recommend that inventions must be devised by one or more humans to be patentable, with this human inventorship retained as an absolute requirement for patentability, or just that UK law should not be changed to account for inventions made using AI-based systems without a longer discussion with industry stakeholders about the economic and social impacts of any potential changes. or ii. you recommend a legislative change now, to allow patent protection for AI system-generated solutions to technical problems which would have qualified for patent protection if devised by a human, but which currently do not qualify because the contributions by human programmers and operators were too minor or peripheral to qualify as devising an invention under current UK national law.

We should take time to discuss the wording of any proposed legislative change, but I believe there will be significant benefits for applicant companies who invest in AI-driven innovation if UK law recognizes that inventions can be devised using an AI system, and should be protectable when there are significant contributions to the invention by the AI system that is programmed, implemented, trained or controlled by a human. This recognition of human + system contributions would be analogous to the recognition of different contributions of co-inventors under current UK law we do not require a single inventor to devise each invention in isolation.

AI experts are already identifying domain-specific inventions generated by their AI systems that would be patentable except for the difficulty identifying a human inventor who qualifies as the deviser. Should these AI-system-generated solutions be patentable? Are they inventions at all if there is no devising human inventor? Is it necessary to revise patent laws to encourage investment in AI innovation and/or to clarify ownership of inventions for which the human contribution is a small one that falls short of the current understanding of devising an invention under UK law?

Do you think patentability should be based on the contribution to the state of the art regardless of how an invention is devised, and that rules for ownership of AI-generated inventions are needed now; or do you think there must always be an identified (i.e. correctly identified) human inventor for a patent to be granted?

Do you agree that there is an intermediate position which allows for patent protection when a human contributes to devising an invention by making arrangements for an AI system to generate a new and non-obvious solution to a complex technical problem?

Please refer to the UKIPOs specific questions here: https://www.gov.uk/government/consultations/artificial-intelligence-and-intellectual-property-call-for-views/artificial-intelligence-call-for-views-patents and let me know if you wish to discuss your recommendations.

Harmonisation with EPO and USPTO, or a time for change?

This years decisions on AI-generated solutions at the EPO, USPTO, UKIPO and High Court of England and Wales1 were all interesting, but they show us how patent offices and courts are applying the current law rather than telling us whether that law needs to change. AA Thornton attorneys are very happy to discuss, explain and apply the current law, but the patent office consultations have a different purpose to allow stakeholders to guide those patent offices and government legislators on whether and how IP laws should be changed (1).

If you think the greatest prize is international harmonisation of laws, you may wish to contribute to WIPOs conversation on AI and IP. Earlier submissions are available here: https://www.wipo.int/about-ip/en/artificial_intelligence/conversation.html

Many WIPO member states and stakeholders are involved.

You may also wish to glance at the October 2020 report on the USPTOs consultation on artificial intelligence and IP policy. The report mentions that it is a priority of the USPTO to maintain US leadership in innovation in emerging technologies including AI, and to encourage further innovation. The UKIPO has the same objective.

The USPTO report is available here: https://www.uspto.gov/sites/default/files/documents/USPTO_AI-Report_2020-10-07.pdf

The majority of comments received by the USPTO suggested that current AI systems cannot invent without human intervention and that, while humans remain integral to the operation of AI, there is no urgency to modify current US IP laws. A lot of AI innovation is currently patentable in the US, and the USPTO report notes that human contributions may involve designing an AI algorithm, developing an AI system, implementing hardware that is adapted to process the algorithm, or preparing inputs to an AI algorithm i.e. they recognize various contributions that allow a human inventor to be named. The report then refers to US statutes and comments from the Federal Circuit courts as an explanation of current inventorship law.

A majority of comments submitted to the USPTO agreed that AI-related patent applications should be assessed as a subset of computer-implemented inventions (as is done at the EPO), and noted that USPTO guidance is available to help applicants and examiners to assess subject matter eligibility and disclosure requirements for computer-implemented inventions. We agree with this and have previously noted the consistency between some of the USPTOs 2019 guidance and examples within the EPOs Guidelines for Examination we believe there is a genuine opportunity for international harmonisation of patent law relating to AI and computer simulation and AI inventorship, because all major patent offices are facing the same issues at the same time.

Other comments received by the USPTO include concerns about enabling disclosure and the impact of AI on determining the knowledge of a person having ordinary skill in the art, and the potential proliferation of prior art issues that have also been noted in Europe.

However, the USPTO report seems to suggest that the question about whether US law needs to change was answered by many respondents by referring to current US law, rather than focussing on the needs of industry, so further industry input is desirable (a justification based on current law will always tend to maintain the status quo). Also, the report equates invention with artificial general intelligence (AGI) and this seems unnecessary. We are hearing from AI experts that their existing narrow AI systems can, once trained with vast amounts of domain-specific data, generate new and non-obvious solutions even when there is not an easily identified human deviser of an invention.

So there are companies that currently feel unable to apply for UK patent protection for AI-generated solutions to complex technical problems solutions that would have been patentable if devised solely by a human inventor, and some of which may be patentable in the US in view of a growing recognition of the different ways in which humans are contributing to inventions being made using AI systems.

We have also heard strong views that UK law and European Patent Office practice needs to change to allow patent protection for core AI technologies including machine learning algorithms that deliver technical advantages, instead of only the EPO-defined specific technical applications of those algorithms (with claims functionally limited to the particular technical purpose) and quite narrowly-defined specific technical implementations (where algorithms are adapted to take account of the capabilities or constraints of particular hardware). This is not the main focus of the UKIPOs consultation questions, but perhaps UK legislators can be encouraged to improve this situation via replies to the consultation?

We are hearing an increasing number of industry voices suggesting the need to review current legislation to resolve patentability and ownership issues for AI-generated inventions, to ensure that the patent system encourages investment in AI-based innovation and to remove the current expectation of future validity and ownership questions. Some industry leaders are happy for this conversation about AI and IP to proceed at a pace that will allow their AI experts, economists and IP directors to be fully consulted before IP laws are changed, and of course some AI-based disruptor companies are keen to see more rapid change.

We applaud the patent offices for consulting, since the best way to deal with the wide range of views is to give all stakeholders an opportunity to express them.

Similar to the US consultation, the UKIPOs call for views on AI and IP refers to the UK Governments ambition to encourage growth in transformational new technology sectors and remain at the forefront of the AI and data revolution. It includes a statement that the UKIPO wishes to make sure the UKs IP environment is adapted to accommodate AI technologies such as machine learning, which suggests an open mind about the possibility of legislative change. We are also open minded, and keen to hear from you.

Mike Jennings is a Member of the CIPA Computer Technology Committee, epi, AIPPI, and SACEPO working group on quality. The above article is not intended to represent views of CIPA, epi, AIPPI, the EPO or specific clients of AA Thornton.

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Call for industry views on AI and IP and ViCo - Lexology

Recently a news item caught my eye: there is a project to use artificial intelligence to recreate the smells of Europe of 500 years ago. The idea is to have AI scan all that has been written in that period to gather information related to the description of smells and collate them for a composite scent scape of those times. The team will then collaborate with chemists and perfumers to recreate those smells, to display them at interactive museums and historical sites.

My mind, of course, jumped to Queen Elizabeth Is oft-quoted remark that she bathed only once a month, as also The Great Stink rising from the Thames river in London in 1858. Would Londoners want to be reminded of these glorious incidents from their past, if only to be more grateful for their modern-day bath showers and clean(er) air? Even Europes smells may not be exactly what its current residents want to remember or experience.

That said, the link between smells and memory is deep and universal. There is nothing like a familiar whiff to set off all sorts of thoughts. For me, every place has a smell, as do events. People also have specific smells grandparents and parents, spouses, children, friends, enemies and so on. My mother used to tell me that when I was a toddler, if my father was out of town I would sniff all the pillows till I found one that smelt of him and sleep on that!

While many of my generation write about 4711 the famous Kolnisch Wasser or Eau de Cologne, of which my mother always had a bottlein relation to their female forbears, for me my mother is inextricably linked to sandalwood oil, the only perfume she used throughout her life abroad, confounding men and women alike with that mysterious spicy aroma! And I associate my grandmother with the oddly era-specific aroma of paan and talcum powder.

New cars, new notebooks, new clothes all have special aromas. The Durga Puja I attended in London over 20 years ago did not assuage my homesick heart simply because the venue was not redolent of marigolds, camphor, and coconut coir smoke. Delhi sarkari offices generally smell of badly plumbed toilets and damp airconditioning, and their Calcutta counterparts reek of stale mustard oil. Even pollution smells distinctly different in both. And of course, there is food. Mas and Didas signature dishes, the whiff of heeng and saffron emanating from kachoris and biryanis, the sulphurous goodness of rock salt, the sweet floral bouquet of Bengals short-grained rice, the complex scent of garam masalas many spices and panchphorons five seeds, fermented bamboo shoots and curry leaves each evokes a memory for me.

Yet there is also a timelessness to some smells. The onset of autumn in northern India has always smelt of harsinghar shiuli for Bengalis. The aroma of cool rain on parched soil, so unromantically called petrichor in English, evokes the same emotion today in Indians as it did millennia ago. And agarwoods resinous bouquet is as heady today as during the time of Kalidasa, who wrote of ladies perfuming their tresses with its smoke 1,500 years ago.

But the long olfactory link between past and present is weakening. As we become more urban, globalised and I daresay deracinated, the ancient smells of flowers, wood, resins and the seasons etc are supplanted in collective memory by aromas of perfumes, detergents, pesticides, pollution and other chemical emanations. If the scents of the 21st century are recreated in the 26th century, they may be even less pleasant than those unearthed from the 16th century.

DISCLAIMER : Views expressed above are the author's own.

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AI to give us a scent of the past - Economic Times

Written by Abhishek Singh | Updated: November 26, 2020 7:20:22 pmIndian farms and farmers provide vast and rich data to help create AI solutions for not just the country but the world at large. And this is one of the factors that makes the opportunity for AI in Indian agriculture unparalleled.

I see a big role for AI in empowering agriculture, healthcare, education, creating next-generation urban infrastructure and addressing urban issues, Prime Minister Narendra Modi said while inaugurating the Responsible AI for Social Empowerment Summit, RAISE 2020. Artificial Intelligence-based agri-tech applications are set to unleash value in agriculture, especially in wake of the recent farm reforms that have opened doors to private sector investments in agriculture.

In the financial year 2019-20, Indian agri-food tech start-ups raised more than $1 billion through 133 deals. Indias exports of agricultural products rose to $37.4 billion in 2019 and with investments in supply chain and better storage and packaging, this is set to increase further. All these steps will go a long way in ensuring remunerative prices for farmers and reduce agrarian stress.

This growth in agricultural output and productivity is being further enhanced by investments in technology. Disruptive technologies like AI are making big positive changes across Indian agriculture, and an increasing number of agri-tech startups in the country are working to develop and implement AI-based solutions. Globally, AI applications in agriculture reached a valuation of $852.2 million in 2019 and this is estimated to grow to almost $8.38 billion by 2030, a nearly 25 per cent growth. The Indian agri-tech market, presently valued at $204 million, has reached just 1 per cent of its estimated potential of $ 24 billion.

Use of technology in agriculture will improve farmers access to markets, inputs, data, advisory, credit and insurance. Timely and accurate data coupled with analytics can help build a robust demand-driven efficient supply chain. With the use of sensors, photographs through phones, IoT devices, drones and satellite images, agricultural data can be collected and matched with weather data, soil health card data, mandi prices and help build predictive models that can greatly enhance decisions about seeds, fertilisers, pesticides that are of critical importance in both pre-harvest and post-harvest stages. Most of these AI models are low-cost and affordable and can add a lot of value to the agriculture ecosystem.

India has made rapid strides in the services sector, yet, agriculture continues to employ 49 per cent of the workforce and contributes 16 per cent of the countrys GDP. Improvement in agriculture would, therefore, positively impact the well-being of a very large section of the Indian population, apart from delivering food security to our country. Feeding over a billion Indians on limited land resources is a big challenge, a task that requires technological intervention on a large scale, to enable a giant leap in agricultural productivity.

Indian agriculture is faced with multiple challenges like high dependence on monsoon, resource intensiveness heavy use of resources (water, inorganic fertilisers and pesticides), degradation of land and loss of soil fertility, and low per hectare yield, among others.

AI can play a catalytic role in improving farm productivity, removing supply chain constraints and increasing market access. It can positively impact the entire agrarian value chain. It is estimated that AI in global agriculture could be a $4 billion-opportunity by 2026.

Greater use of AI would increase mechanisation of Indian agriculture. It would increase productivity by introducing precision agriculture. Indian agriculture technology startups are trying to integrate AI-based technological solutions across a range of use cases such as monitoring crop productivity and soil fertility, predictive agricultural analytics and ensuring supply chain efficiencies.

In predictive agricultural analytics, various AI and machine learning tools are used to predict the right time to sow seeds, get alerts on impending pest attacks etc. AI in agriculture powers the optimum utilization of farming data to help devices like smart drones, autonomous tractors, soil sensors and Agri-bots function and deliver superlative efficiency in farming.

In what is a great example of innovation for agriculture using AI, industry has joined hands with the government to develop an AI-powered crop yield prediction model to provide real-time advisory services to farmers. The system employs AI-based predictive tools to help increase crop productivity, enhance soil yield, control the wastage of agricultural inputs and warn of pest or disease outbreaks.

This system uses remote sensing data provided by the Indian Space Research Organisation (ISRO), data from soil health cards, the India Meteorological Departments (IMD) weather prediction and analysis of soil moisture and temperature etc. to give accurate information to farmers.

This project is being implemented in 10 aspirational districts in the country across Assam, Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh.

Similarly, an increasing number of Indian startups are already implementing AI-based solutions in agriculture. A startup has used data science, AI and machine learning algorithms, along with data sets from ISRO to assess damage to crops, compensation payable based on value of the damage that has taken place. Questions around what is being grown, what is the damage, what would the value of the crops damaged be, are answered with reasonably high accuracy.

Another AI startup in India maps farmers zones in remote areas, answering questions like who has been farming which land, what is being grown, what is the quality of soil on the land, with great accuracy. Crop insurers, seed suppliers, state governments all want this data and its possible to build a business model around this as the data and information has value for everyone. Farmers are also able to get all this valuable information and insights which helps them in making better decisions about their agricultural practices and create value.

other Agri-tech startups who are using predictive analytics and machine learning to solve the problem of volatility in input prices and suboptimal input utilization. Imaging and AI, traceability solutions are being developed for large scale quality testing and post-harvest produce handling and monitoring.

Data is helping to create platforms for price transparency to check malpractices in the supply chain. Similarly, agricultural bots (ag-bots) and drones are being developed to ensure seamless execution of cultivation and harvesting.

In order to help these AI solutions scale, what is needed is to increase investments both public and private especially from venture capitalists.

With the recent reforms in the Agriculture sector, there is a likelihood of increased investments in contract farming and infusion of technology for better yields and productivity. This will further push the adoption of AI in agriculture. The recently concluded Responsible AI for Social Empowerment Summit RAISE 2020 Summit has helped provide a platform for global stakeholders to come together to finalize the roadmap for using AI for public good. As many as 321 global AI experts from 21 countries, and sectors including agriculture, converged onto the RAISE 2020 platform to firm up plans for developing path-breaking AI-based tools and for improving the adoption of AI across sectors.

Thanks to the diversity of its soil types, climate and topography, India provides a great opportunity for the data scientists and AI experts to develop state of the art AI tools and solutions for agriculture. Indian farms and farmers provide vast and rich data to help create AI solutions for not just the country but the world at large. And this is one of the factors that makes the opportunity for AI in Indian agriculture unparalleled.

The writer is CEO MyGov; President & CEO NeGD; MD & CEO Digital India Corporation (DIC), Government of India

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AI for the farmer - The Indian Express

Lexology GTDT Market Intelligence provides a unique perspective on evolving legal and regulatory landscapes. This interview is taken from the Artificial Intelligence volume featuring discussion on various topics including, government national strategy on AI, ethics and human rights, AI-related data protection and privacy issues, trade implications for AI and more, within key jurisdictions worldwide.

1 What is the current state of the law and regulation governing AI in your jurisdiction? How would you compare the level of regulation with that in other jurisdictions?

According to our observations, Taiwans government sector is aware of the AI trends and has proceeded to explore whether any adjustment to the current regulatory regimes in multiple aspects would be needed. In early 2018, to promote fintech services and companies, the legislators in Taiwan passed the Financial Technology Development and Innovative Experimentation Act (the Fintech Sandbox Act), which was enacted to allow fintech businesses to test their financial technologies in a controlled regulatory environment. Although the Fintech Sandbox Act is not specifically designed for AI, the creators of new financial-related business models with AI technology may test their new ideas and applications under such a mechanism while enjoying exemptions from certain laws and regulations.

Adopting a similar spirit to the Fintech Sandbox Act, the legislators in Taiwan passed another sandbox law for autonomous and self-driving vehicles, the Unmanned Vehicle Technology Innovation and Experiment Act (the Unmanned Vehicle Sandbox Act), in late 2018, which took effect from June 2019. The act is to provide a friendlier environment to test the applications of AI and the Internet of Things (IOT) in transportation. Vehicle, as defined in this act, covers cars, aircraft, ships or boats, and any combination thereof.

In addition to the above-mentioned legislation, the impacts on the current regulatory regimes as a result of the application of AI technologies have also been widely discussed, such as whether AI technology can be protected by intellectual property rights, what would be the consequences of and how to mitigate algorithmic bias in AI systems, whether data protection issues will be triggered when personal data are shared for the purpose of AI-related developments, among other things. However, as at the time of writing, no explicit court precedents or rulings have been issued on such topics.

It is also worth noting that, according to the Taiwan AI Action Plan, announced by the Executive Yuan in 2018, the Taiwan government has been evaluating relevant issues so as to further determine whether any laws need to be enacted or amended to address AI development. Such issues include, among others, the rights and obligations derived from the application of AI technology (eg, whether AI should be considered a person from the perspective of certain legal fields, whether there will be intellectual property rights in an AI-created work, among other things), open data, restrictions on AI applications, government procurement (eg, the outsourcing concerning AI issues), industry regulatory challenges and approaches to AI, among other things. Given so, we think that Taiwan has been actively examining the current regulatory regime in relation to AI in order to establish a good foundation for developments of AI technology.

2 Has the government released a national strategy on AI? Are there any national efforts to create data sharing arrangements?

The Executive Yuan announced the Digital Nation and Innovative Economic Development Plan and the Taiwan AI Action Plan in 2016 and 2018 to declare Taiwans goal to become an important partner in the value chain of global AI technology and intelligence systems and to leverage the advantages in software and hardware techniques to promote AI technology across industries with, among others, test fields, regulations and a data-sharing environment. According to the Taiwan AI Action Plan, the governments view is that Taiwan is well positioned to take advantage of the opportunities in developing AI-related industries.

In addition, the Taiwan government views AI as having an indispensable role in the 5+2 Industrial Innovation Plan (5+2 Plan), as declared by the Taiwan government in 2018. The 5+2 Plan is considered the core generator for Taiwans next generation of industrial development, which mainly focuses on seven industries: intelligent machinery, Asia Silicon Valley, green energy, biomedicine, national defence and aerospace, new agriculture and the circular economy. To facilitate the 5+2 Plan, the government has launched the AI Talent Programme, which aims to:

With respect to data sharing, the National Development Council prescribed the Guidelines for Trial Operation of Data Interface on MyData Platform to promote personalised digital services called MyData in February 2020. The main purpose of this service, in a similar to open data and open banking, is to create a platform for individuals to authorise the government or the participating companies to collect their personal data in order for the government and such companies to develop and render more personalised services to the individuals with such data.

3 What is the government policy and strategy for managing the ethical and human rights issues raised by the deployment of AI?

The Ministry of Science and Technology under the Executive Yuan announced the AI Technology R&D Guidelines in September 2019 to demonstrate the Taiwan governments commitment to improve Taiwans AI R&D environment. Pursuant to the AI Technology R&D Guidelines, considering that AI developments may bring changes to various aspects of human existence, the Taiwan government expects the participants to always pay attention to when conducting relevant activities and endeavouring to build an AI-embedded society with three core values: human-centred values, sustainable developments and diversity and inclusion.

Deriving from the three core values, eight guidelines were given under the AI Technology R&D Guidelines for all AI participants to follow so that a solid AI R&D environment and society that connects to the global AI trends may be established. The eight guidelines are:

4 What is the government policy and strategy for managing the national security and trade implications of AI? Are there any trade restrictions that may apply to AI-based products?

To date, no laws or regulations have been specifically promulgated or amended to deal with the national security and trade implications of AI. These matters are still handled in accordance with the existing regulatory regime (eg, the National Security Act, Cyber Security Management Act, trade regulations, among others).

5 How are AI-related data protection and privacy issues being addressed? Have these issues affected data sharing arrangements in any way?

In Taiwan, personal data is protected by Taiwans Personal Data Protection Act (PDPA). The collection, processing and use of any personal data are generally subject to notice and consent requirements under the PDPA. Pursuant to the PDPA, personal data is defined broadly as the name, date of birth, ID card number, passport number, characteristics, fingerprints, marital status, family information, education, occupation, medical record, medical treatment and health examination information, genetic information, information about sex life, criminal record, contact information, financial conditions, social activities and other information that may directly or indirectly identify an individual.

Under the PDPA, unless otherwise specified under law, a company is generally required to give notice to (notice requirement) and obtain consent from (consent requirement) an individual before collecting, processing or using any of said individuals personal data, subject to certain exemptions. To satisfy the notice requirement, certain matters must be communicated to the individual, such as the purposes for which his or her data is collected, the type of the personal data and the term, area and persons authorised to use the data, among other things.

AI technology has not changed the said requirements. If a company wishes to collect, process and use any individuals personal data using AI technology or exploring the data with AI technology, it will be subject to the obligations under the PDPA as advised above. Please note that the MyData platform described in question 2 should also be subject to the PDPA regime.

6 How are government authorities enforcing and monitoring compliance with AI legislation, regulations and practice guidance? Which entities are issuing and enforcing regulations, strategies and frameworks with respect to AI?

In the past few years, the Executive Yuan has published several guidelines and plans for AI developments, such as the Digital Nation and Innovative Economic Development Plan, the Taiwan AI Action Plan, the AI Technology R&D Guidelines and the 5+2 Plan, as stated in the answers to previous questions.

However, considering AI is more of a technology, which could be applied in various industries, there is no single central competent authority for the actual enforcement and monitoring of AI technology and such enforcement and supervisory tasks fall under the jurisdictions of relevant competent authorities. For example, the Ministry of Economic Affairs is assigned as the competent authority for the Unmanned Vehicle Sandbox Act, while the Financial Supervisory Commission is the authority for the FinTech Sandbox Act.

7 Has your jurisdiction participated in any international frameworks for AI?

To our knowledge, the Taiwan government has not participated in any international frameworks for AI. However, according to the relevant public announcement by the Ministry of Science and Technology, the AI Technology R&D Guidelines, as outlined in question 3, were set out to, among other things, follow the international trends with respect to AI developments and were prescribed by referencing the relevant principles and guidelines of the European Union, Japan, the Organisation for Economic Co-operation and Development, among others.

8 What have been the most noteworthy AI-related developments over the past year in your jurisdiction?

Taiwan is well known for its information and communications technology and semiconductor industry, and it is reported that there have been AI-related developments in these areas, such as AI-related chips, integrated circuit design, systems, software and relevant peripheral products. In recent years, there have been also certain AI and data analysis-focused start-ups reportedly having the potential to become the next unicorns, with main products used in the areas of digital marketing, advertising, video analytics, among others. It is also noteworthy that there are more and more associations and non-profit organisations offering educational and training programmes and courses to various industry players that are interested in exploring the possibility of exerting AI as a tool for improving their existing products or operations or to create new applications or business models with AI as underlying technology.

9 Which industry sectors have seen the most development in AI-based products and services in your jurisdiction?

In addition to those stated in question 8, please see below the recent trends relating to developments of AI-based products and services in Taiwan.

Transportation

As mentioned above, the aim of the Unmanned Vehicle Sandbox Act is to provide a friendlier environment for testing the application of AI in transportation. Pursuant to the news releases in November 2019, the Ministry of Economic Affairs plans to invest around US$8 million in four years to expedite the industrialisation of unmanned vehicle technology.

Healthcare

The Ministry of Science and Technology has driven the AI for Health plan, which assisted major medical research institutions in Taiwan in developing AI algorithms to be used for cardiovascular risk assessment, diagnosing cancer lesions at an early stage, accelerating the image recognition, among other things.

Financial services

The main application of AI in financial business in Taiwan involves correspondence with clients, such as ChatBot and Robo-Adviser Services. In June 2017, the Securities Investment Trust and Consulting Association of Taiwan, the self-disciplinary organisation of the asset management industry, issued Operating Rules for Securities Investment Consulting Enterprises Using Automated Tools to Provide Consulting Service (the Robo-Adviser Rules). Pursuant to the Robo-Adviser Rules, securities investment consulting enterprises may provide online securities investment consulting services by using automated tools through algorithm (Robo-Adviser Services).

10 Are there any pending or proposed legislative or regulatory initiatives in relation to AI?

As indicated in question 1, according to the Taiwan AI Action Plan, the Taiwan government is still evaluating the following issues so as to further determine whether any laws need to be enacted or amended to address AI development:

In addition to the above, some legislators proposed the draft Basic Act for Developments of Artificial Intelligence in 2019, which is intended to set out fundamental principles for AI developments, to drive the government to promote the development of AI technologies. The draft is still under review by the Legislative Yuan and it is uncertain whether this draft will be passed.

11 What best practices would you recommend to assess and manage risks arising in the deployment of AI?

As stated in question 1, Taiwan promulgated two regulatory sandbox laws, the FinTech Sandbox Act and the Unmanned Vehicle Sandbox Act. These regulatory sandbox laws were enacted to allow the relevant businesses to test their new ideas and technologies within a safe harbour. After completion of the approved experiments, the relevant competent authority will analyse the result of the experiment. If the result is positive, the relevant competent authority will actively examine the existing laws and regulations to explore the possibility of amending them with a view to making feasible the business models or activities previously tested in the sandbox. Therefore, for any business models that will involve the application of AI, relevant risks, especially legal risks, may be mitigated in case they are tested under either of the two sandbox laws.

As to any proposed business models or activities not falling within the sandbox scope permitted by the above sandbox laws, we would recommend that relevant risks, especially legal risks, be analysed as early as possible, and certainly well before the time any product is officially launched. The application of AI technology or AI-related products may involve various issues under traditional as well as emerging legal areas such as potential liabilities under civil and criminal laws, the ownership of AI products-related IP rights, privacy, among others. With respect to any products to be sold to end-customers, more detailed analysis on issues such as consumer protection and product liabilities, product inspection and testing, and liability insurance are also advised.

The Inside Track

What skills and experiences have helped you to navigate AI issues as a lawyer?

We were engaged by the National Development Council to conduct a research project, focusing on exploring the necessary adjustments to the existing legal regime to create a more friendly environment for AI developments. As to AI technology and its applications and its various areas of legal practice, Lee and Li, known for expertise in all legal fields and offering a full range of services, has the competitive advantage in offering valuable insight and best solutions from a Taiwan law perspective in every legal practice area.

Which areas of AI development are you most excited about and which do you think will offer the greatest opportunities?

AI applications may be used across many industries. As lawyers, what we are most excited about is that we may see industry experts come up with creative ideas associated with the technology and assist clients in exploring how to put technology innovations and actual AI applications into practical use in the real world. With respect to Taiwan, Taiwans well-known information and communications technology has established a good foundation for AI development. Taiwan has also been one of the major players in the semiconductor manufacturing industry. Given this, we think Taiwan has great opportunities to play an important role in AI trends.

What do you see as the greatest challenges facing both developers and society as a whole in relation to the deployment of AI?

We think an important challenge facing the developers would be how to commercialise AI technology and make AI applications address the needs of industry players and the general public. From the perspective of wider society, we think that the greatest challenge might be the replacement of human resources. Take the legal profession as an example, where topics widely discussed include how AI may impact the legal profession (eg, whether AI will replace some of the jobs that lawyers do). One can imagine AI applications to replace some jobs in multiple professional settings in many industries. Where AI applications can replace most of the jobs currently done by humans, it would be inevitable that the whole society would have to face issues arising from human resource surplus.

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An interview with Lee and Li Attorneys at Law discussing artificial intelligence in Taiwan - Lexology

AI is still in its early stages in Southeast Asia. Some countries are implementing initiatives to build the digital infrastructure and data ecosystems, with Singapore, Malaysia, and Vietnam leading the charge, according to a McKinsey report.

Machine learning, a subset of AI that trains computers to find patterns in big data sets to draw conclusions and make predictions, is the bedrock of most use casesfrom chatbots to driverless cars. Annotating one hour of video content can take eight hoursor morewhile the AI software has to process millions of data inputs in order to be effective and reliable.

Malaysia-based Supahands has carved out a niche for itself as an end-to-end data labeling partner that provides training data for AI and machine learning. It does so via a crowdsourced workforce of 16,000 SupaAgents across Southeast Asia, as well as their own proprietary technology to prepare different types of training data sets.

The in-house software includes SupaAnnotator, an image interpretation tool with customizable labels such as 2D bounding boxes and polygon annotations, and SupaMiner, a data management platform for image-to-text, video, and audio transcription projects, which can determine metadata for tagging and categorization.

Looking at the machine learning process as a whole, data labeling is an important part of the puzzle and simultaneously one of the most time-consuming and laborious tasks to be done when developing a model, said co-founder and COO Susian Yeap. Given the more repetitive nature of the tasks, the workforce we already had on hand, the infrastructure we could put together, and the general market opportunityit was the perfect fit.

Supahands wants to free up the bulk of an AI engineer or data scientists time by taking over the labor-intensive tasks of data preparation and labeling, helping their clients deploy their AI models faster. Use cases range from autonomous vehicles to facial landmark recognition and geospatial imaging across several industries, though a chunk of the revenue comes from retail AI and agtech verticals.

To give you an example, we have a retail client whom we helped with real-time shelf tagging, said Yeap. For them, we reviewed over 750,000 images a month with a team of more than 350 SupaAgents and an image annotation accuracy of over 95%.

For all the glitz and glamour associated with the promises of AI, the collection and labeling of huge, localized datasets are the necessary grunt work in the AI supply chain. But it presents a large market opportunity. Industry analyst Cognilyticaputs the third-party data labeling services market at USD 1.7 billion in 2019 and is predicted to grow to over USD 4.1 billion by 2024.

Supahands has helped Ibotta, a US-based consumer tech company process 300,000 to 600,000 receipts per month and AI firm Visionary to categorize 200,000 images saving its client 2030% of time. In 2019, it raised an undisclosed amount of Series A funding led by social venture capital firm Patamar Capital alongside Cradle Seed Ventures, for further expansion in the region.

But theres also another side of the technology Supahands is not shying away from. The AI/ML world has a history of not only injecting gender bias into software, but also to amplify it in the algorithm. While the future of AI shines bright, it is especially worrying that women will not only have to contend with everyday sexism, but also from that of technology.

If five 20-year-old men were to create a chatbot, the finished chatbot will sound like a 20-year old man, Yeap asserts. How could it not?

In an industry where professionals are overwhelming men, Supahands is setting an example. Female employees make up about 50% of the workforce, and this is consistent across its leadership, in tech, as well as in the SupaAgent teams.

From an AI development standpoint, its crucial that the technology provider is aware of the high tendency to create biased machines and to actively ensure as many variances and as much diversity as possible in the process, explains Yeap. We play our part by providing quality training data to our clients with as little bias as possible. In fact, our tech team consists of 51% women and five different nationalitiesan accomplishment that is rare in the industry.

However, when asked if Supahands hires with a diversity objective in mind, she responds that they dont. The company emphasizes the importance of hiring people who think differently, and along with that, naturally, come those who have different backgrounds in terms of culture, race, and gender.

We dont just look for female engineers and female business operation managers to tip the scales, said Yeap. We are just looking for the best engineers and the best designers, and based on these hiring principles and the people who walked through our door, this happens to be how our team looks like today.

This starts for her with the individuals mindset. Its crucial for women to stop putting a label on ourselves and seeing ourselves as women in tech,' she said. We should present ourselves as the working professionals and industry experts we really aresoftware engineers, data scientists, programmers, and CEOs.

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In the glamorous new AI world, it pays to do the tedious work: Startup Stories - KrASIA

Expected to be the most significant business advantage in the future by 72% of executives, artificial intelligence (AI) is predicted to be incorporated into 47% of organisations HR functions by 2022.

While it is feared that employment rates will drop as the use of intelligent technologies rises, when speaking with Business Chief in October, Arun Shenoy, SVP Global Sales and Marketing at Serverfarm reflected on the best way to deploy technology, software and hardware tools. Most organisations find this challenging because they are only solving one part of the problem - the technology. Simply buying and deploying a platform isnt enough; you have to change and refine the processes and ensure that you have the right people, commented Shenoy.

In fact, speaking with executive experts in HR operations, the consensus highlights that benefits of AI in HR operations come from a collaborative approach between AI and humans, with a core use case being to provide efficiency gains. It has allowed us to do the same thing we always did - but faster and more cost effective, comments Andi Britt, Senior Partner at IBM Talent & Transformation, IBM Services Europe. While the internet brought the capability of fast recruitment, both Britt and Chris Huff, CSO at Kofax identify that AI can apply the same speed to the assessment of potential candidates, the likelihood of future success and the expected timeframe to fill a given role. This is an example of the ways in which AI is changing the situation so that technology enables the HR function to solve critical business challenges, building on earlier contributions from workforce analytics, added Britt.

With COVID-19 placing organisation and business operations on the edge of a pivotal moment when it comes to innovation and digital transformation, AI and automation have transitioned from a nice to have to a necessity for survival. COVID-19 has created a digital awakening that has accelerated the adoption of AI and automation technologies, comments Huff. It is expected by those in HR that COVID-19 will not only accelerate the overall digital journey for organisations, but the role of HR in the modern workplace. This acceleration will ultimately move organisations closer to HR 3.0 with employee experience at its centre. CHROs at high performing organisations are taking immediate action to achieve this vision. They are leveraging real-time unstructured data from inside and outside organisations, and pairing that with analytics and AI to improve talent and workforce decisions while enabling more personalised employee experiences, says Britt.

Statistics reported during the height of the pandemic, identified that many organisations are embracing AI tools to attract diverse talent and to enhance and personalise recruitment. In an IBM HR executive survey, the company identified that more than half of high performing companies are using AI to identify behavioral skills to build diverse and adaptable teams. Currently, High performing organisations are leveraging AI across talent acquisition at a rate of 6 times more than all other companies. During the pandemic, IBM saw its clients rely heavily on AI enabled HR applications such as chatbots and skill building recommendation platforms. These technologies enable organisations to free up HR leaders time for more meaningful work. C-Suite leaders surveyed expect to see nearly tenfold growth with regard to automating HR processes between 2018 and 2022, comments Britt. However, while the rate of adoption has increased, IBM found that only 30% of companies have the skills and capabilities in AI in the HR function.

To be successful in adopting AI in HR operations - or any technology - culture is identified as an all to often underestimated barrier. It is important for organisations to ensure that they include their employees in the transformation journey. When employees understand the reasoning for change they are more receptive, making it easier to implement and adopt technology. Ultimately, Progress has to start from the top, with good leadership and open conversation to dispel fears and misunderstandings about the technology, states Britt. Not only is it important to engage with employees to showcase the business needs, it is also important to listen to the needs of the employees conducting the tasks.

By combining the best of what AI can provide, with employee hopes for the technology, Huff explains that this approach is a win-win that will increase adoption of AI and lead to a collaborative person-machine future to drive productivity for the organisation and individual. With this collaborative approach to AI and humans, HR is on the cusp of a new digital era in which employees adopt a more behind-the-scenes role to create the scenarios carried out by AI. People will find themselves in more creative, strategy, problem defining and problem escalation roles as opposed to transactional activities, concludes Huff.

The benefits of artificial intelligence (AI) in HR

Today, AIs capabilities are being used to augment business operations and consumer solutions, comments Andi Britt, Senior Partner at IBM Talent & Transformation, IBM Services Europe. At IBM, the company has identified five reasons for implementing AI in HR operations:

The challenges of artificial intelligence (AI) in HR

Current HR and AI trends point to a promising Future of Work thats richer in experience, but also brings with it the need for strong governance to account for unintended consequences, comments Chris Huff, CSO at Kofax. When it comes to the successful adoption of AI to deliver on its promising future, IBM identifies four key prevention barriers:

For more information on business topics in the United States and Canada, please take a look at the latest edition of Business Chief North America.

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Reinventing HR operations with humans and AI collaboration | Human Capital | Business Chief North America - Business Chief

COVID-19 has been a gut punch. Our response? Largely frantic, like deer caught in the headlights. Researchers are racing to find a vaccine, as we pause in lockdown mode. But the process of drug discovery is lengthy and expensive, just like the process of discovering and designing any material crucial to fighting existential problems.

But these problems are piling up: pandemics, climate change, antibiotic resistance, food security, cyber-challenges, shared-economic prosperity and so on. We urgently need to change our traditional approach to science.

We have a rare and narrowing window of change to build a better world after the pandemic.

The World Economic Forum's inaugural Pioneers of Change meeting will bring together leaders of emerging businesses, social entrepreneurs and other innovators to discuss how to spark and scale up meaningful change.

To follow the Summit as an individual, you can become a digital subscriber here. As a company, you can participate in the summit by becoming a member of our New Champions Community.

For centuries, weve done science in a linear way: an issue prompts a hypothesis, followed by a model and a test. If the result is a failure, the process starts again, and iterations may take years. And its got us far; its how weve developed better plastics, more efficient solar panels and lighter-but-stronger composites for modern aircraft.

But the world is changing rapidly; in order to tackle todays global challenges with the speed and effectiveness they demand, we need a new way to do science.

Science is an inherently creative process; scientists are constantly expanding their imagination to explore new designs of drugs and chemicals. But the human brain has its limits. After all, there are more possible designs of a molecule than there are atoms in the universe. No human can sift through all of them to come up with the best option.

The good news is we do have the ingredients to give science or our brains limits a boost: cutting-edge computing technology and talent. The real challenge is to apply them strategically, in both public and private sectors.

Image: IBM Research

Helping science determine a new path

The world is witnessing a revolution in computing. Artificial Intelligence (AI) is enhancing traditional computing and could soon boost the emerging quantum ones: the very machines that could allow us to solve some of the worlds greatest problems. They can be accessed from anywhere on the planet through a hybrid cloud.

More and more companies and labs are now using AI, whose deep neural networks are able to extract scientific knowledge at scale from all the literature published on a specific topic.

Say a scientist needs to create a new catalyst for better artificial fertilizers. Instead of blindly trying to determine the catalysts chemical structure, AI would first sift through a multitude of patents, academic papers and other publications to see what had already been done on this topic.

Next, AI would automatically generate hypotheses based on the data it found, to expand the search for new molecular designs. Based on the most promising hypothesis, high-performance computers and quantum computers would simulate a new molecule.

Digital work done, the simulation would be confirmed or refuted during increasingly autonomous lab tests. Finally, AI would assess the result, identify anomalies and extract new knowledge. New questions would surface and the loop would continue.

To shift the paradigm of scientific discovery, we need to enable AI, hybrid cloud, and eventually quantum computing to converge. We also need a second ingredient new types of scientific collaborations or communities of discovery to be added to the mix.

What would we gain? An accelerated scientific method, fit for catalysing major transformations in science, and with unprecedented speed and automation. We could design new materials faster than ever before, impacting all aspects of our lives from healthcare to manufacturing, to agriculture and beyond.

For the first time, closing the loop in scientific discovery seems a very real and imminent possibility. When it does happen, we will have achieved the dream of scientific advancement being a self-propelled and never-ending process.

The need for new communities of discovery

But its not just technology that that will drive this new level of discovery; people will too. The world is teeming with the talent and creativity of millions of scientists spread across academia and industry, who shouldnt be tackling the numerous global crises they face independently. Indeed, no single company or university lab can overcome a pandemic on its own.

National and international private-public collaborations share knowledge, data and the latest technology, speeding up the process of discovery. Our need for more of them has never been greater.

They also need to be diverse. In science, problems can be big and complex, or small and more focused. For instance, CERN (the European Organization for Nuclear Research) requires a deeply coordinated community with scientists from 42 countries to run some two-million experiments every day across about 170 labs and thats just for the science coming from Large Hadron Collider.

And yet, science is becoming more open, with researchers from private and public sectors increasingly sharing papers, experiments, data, results and resources.

One successful example of such a smaller, new community of discovery is the COVID-19 High-Performance Computing Consortium. A collaboration of 87 partners from academia, industry and national labs, it has been granting researchers from around the world who are fighting the current pandemic access to supercomputers.

Industry partners are often rivals, but not in the current coronavirus vaccine endeavour. Every member of the Consortium is united by a common goal: to accelerate our search for a new treatment or vaccine against COVID-19. The benefits of collaboration are greater speed and accuracy; a freer exchange of ideas and data; and full access to cutting-edge technology. In sum, it supercharges innovation and hopefully means the pandemic will be halted faster than otherwise.

But material design isnt the limit.

With continuing evolution as an AI-accelerated approach that builds on data, advanced compute in hybrid cloud, progress in quantum computing and growing communities of discovery, the upgraded, self-propelled continuous scientific method should greatly impact multiple aspects of our lives. And with all the global crises of today and tomorrow, the need for it has never been greater.

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Scientific discovery must be redefined. Quantum and AI can help - World Economic Forum