Category Archives: Ai

Update: HIMSS20 has been cancelled due to the coronavirus. Read more here.

The rise of artificial intelligence into the mainstream of healthcare information technology is one of the biggest trends at HIMSS20, according to analytics vendor Health Catalyst, which will be in booth 2428.

Healthcare IT News asked Jason Jones, chief data scientist officer at Health Catalystand a speaker at HIMSS20, about a few overarching trends surrounding AI that are important to HIMSS20 attendees. He says that a lack of results from healthcare AI implementations, algorithmic bias and difficulty attracting and retaining data science professionals are some key areas to watch.

Jones said the industry is not seeing healthcare AI results in the timeframe and to the magnitude hoped for. On a related note, there is the question of how healthcare-provider organizations deal with the crush from AI-powered health IT vendors in the space.

"It is very easy for individuals or organizations to get excited about their first AI project,"Jones said. "It is new, exciting and a bit magical. Out of dreams of doing good or pressure to perform, people would like to believe there is a solution. What is the problem? Building predictive models is very quick and easy."

Jones said the problems here are in four areas.

"First, ironically, the biggest obstacle toward solving a problem via leveraging AI can be that the problem to be solved is defined poorly or differently by different people,"he explained. "Start with a great problem statement and common understanding of what 'awesome'looks like across stakeholders. Second, technically, the difficult part is getting high-quality data to train the model commonly 50-100x more time and effort than building a predictive model."

Jason Jones, Health Catalyst

Evaluate whether the organization has the high-quality data it needs before starting an AI project, he advised; if not, acquire or improve available data or choose a different project, he cautioned.

"Third, most improvements in healthcare require behavior change on the part of physicians, nurses, administrators, members, patients, etc.," he said. "We do not need AI to tell us to eat and exercise well, it's just that it can be hard to do. When human behavior change is needed for success, we need tools and resources for change management."

And fourth, few AI efforts are set up for optimization or formal evaluation, Jones explained.

"If you fear you are being left behind in the AI race, consider the last time you felt left behind by an infomercial," he offered. "The claims of success for AI may not be much better founded. Focus on fundamentals, ask challenging questions, realize that AI typically fits into a workflow that requires multiple changes, and plan to monitor and improve over time."

Then there is the artificial intelligence problem known as algorithmic bias. How do healthcare-provider organizations deploy AI in such a way that they do not exacerbate health disparities?

"There has been an increase in concern that the 'move fast and break things'approach may have done more harm than good in particular and in aggregate," Jones stated. "People are intolerant of breaking things in healthcare in ways they feel could have been anticipated. We are justifiably and particularly angry when the nature of the failure involves disparity based upon personal characteristics such as gender, ethnicity, geographic location and socioeconomic status."

But healthcare does want algorithms to discriminate between people at greater or lesser risk for readmission or ready or not ready to quit smoking, for example.

"Remembering this helps us to think differently about AI," Jones said. "For algorithms to succeed, we should retain the right and accountability to define what we want the algorithm to do and not do and then measure against these desires. With that in mind, it is possible to go beyond fear of algorithmic bias to algorithms helping assure equity."

On whiteboards, healthcare-organization staff can convert equity from a balancing measure (possible harm) to an outcome (desired benefit) and then design and measure for that, he explained.

And Jones third healthcare AI trend surrounding HIMSS20 is how healthcare provider organizations attract and retain data science talent.

"It can feel as though it is very difficult and expensive to attract a data scientist," he said. "In healthcare, it can feel impossible to compete with the tech sector. If you feel this way, pause and consider your needs and assets. First, in healthcare, most of the technical time and effort is in gathering and preparing data data engineering. You may not need as many data scientists as you think, or you may be able to 'rent'one when you have the need."

Second, think about what the organization needs a data scientist to do for example, ask and answer questions better with data, and in a way staff can understand, he added.

"Test and evaluate for people who can do that," he advised. "Usually this means not using the 'Kaggle'(data competition) approaches. These are the aspects of data science that are both most technical and most easily automated."

And third, if a healthcare organization has a noble purpose, point this out and explain how the data scientist contributes, Jones advised.

"Give him or her opportunities to see that contribution firsthand from call centers, to boardrooms, to nurses' stations,"he concluded. "Taking these steps not only helps you attract and retain talent, but also helps you get better output through the data scientist better understanding the real problems and what solutions might look like."

Jones will be at HIMSS20 on a panel entitled "Analytics to Algorithms: How to Maximize Impacts" on Monday March 9. He also will be presenting alongside Dr. Terri Steinberg during a presentation entitled "Machine Learning and Data Selection for Population Health"on Thursday, March 12.

Twitter:@SiwickiHealthITEmail the writer:bill.siwicki@himssmedia.comHealthcare IT News is a HIMSS Media publication.

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Health Catalyst points HIMSS20 attendees toward three AI trends - Healthcare IT News

Saadia Muzafarr founder of Tech Girls Canada

With her work focused on maximizing the public good and having built up Tech Girls Canada the hub for Canadian women in STEM Saadia Muzaffar is renowned for the vast amount of work that shes poured towards creating an environment in the AI industry that includes diversity.

Tech Girls Canada, a Canadian non-profit, is dedicated to conducting research and co-designing solutions that address barriers for diversity and equity in science and technology sectors by championing LGBTTQ+, women of colour, women of all abilities, refugee, immigrant, and indigenous women.

Muzaffar is a leading force in the tech scene of Canada, author, and passionate advocate of responsible innovation, decent work for everyone, and prosperity of immigrant talent in STEM. She is an advisor to the Government of Canadas Economic Strategy tables for the Access to Skilled Talent working group, and part of Canada Beyond 150: policy for a diverse and inclusive futures Feminist Government initiative.

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10 women advancing AI in Canada - IT World Canada

Vatican officials are calling for stricter ethical standards on the development of artificial intelligence, with tech giants IBM and Microsoft being the first companies to sign its new initiative.

The Rome Call for AI Ethics lays out six broad principles: transparency, inclusion, responsibility, impartiality, reliability, and security and privacy. These principles say that technology should protect people, particularly the weak and underprivileged. They also urge policymakers across the world to create new forms of regulation on advanced technologies that have a higher risk of impacting human rights, which includes facial recognition.

AI is incredibly promising technology that can help us make the world smarter, healthier, and more prosperous, IBM vice president John Kelly III said after the initiatives signing. But only if it is shaped at the outset by human interests and values.

The Vatican wants to ensure that companies are not using AI as a means to collect data without the consent of individuals and then using that data for commercial or political benefit. In one recent example, it was shown that thousands of federal government agencies and private companies were using software owned by face recognition company Clearview AI, which scraped facial data without peoples knowledge. The companys database, which features more than 3 billion images pulled from various online sites, is being used by law enforcement to catch persons of interest.

The document also says that a duty of explanation must be established and that AI-based algorithms should provide individuals with information on how these algorithms came to their decisions to ensure that there is no bias. Last year, US lawmakers introduced a bill that would do just that and allow the Federal Trade Commission to create rules that would force these companies to evaluate automated systems containing highly sensitive information.

Vatican officials hope to increase the number of signatories for its AI ethics initiative in the coming months. They also hope to collaborate with universities across the globe to promote more scientific research into ethical AI guidelines.

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The Catholic Church proposes AI regulations that protect people - The Verge

]]> Collaborating with QRI enables Emerson to enhance its capabilities in giving useful analytics to maximise production. Credit: Paul Lowry.

US-based companies Emerson and Quantum Reservoir Impact (QRI) have collaborated to develop applications for artificial intelligence (AI)-based analytics for oil and gas exploration and production (E&P).

The two companies will also develop and market decision-making tools for E&P analytics.

The collaboration aims to help oil and gas customers to harness data to optimise their reservoir management strategies.

It will also enable customers to enhance digital transformation technologies.

Emerson E&P software president Steve Santy said: Collaborating with QRI enhances our capabilities to give customers meaningful analytics to maximise production and capital efficiency and for better reserve assessment.

The partnership integrates Emersons E&P software portfolio with QRIs expertise in applying augmented AI, machine learning (ML) and advanced analytics for reservoir management.

Emerson and QRI will also apply advanced computational technologies to help geoscientists and engineers make actionable field development decisions to achieve higher productivity.

QRI chairman, CEO and co-founder Nansen Saleri said: People, process and data are as important as technology to the success of the solution. Our partnership with Emerson makes for a very powerful team to ensure that our offerings will become a prominent choice in the market.

As our industry continues to transform, we share Emersons vision of applying state-of-the-art deep learning tools to automate next-generation workflows and offer our customers a rapid means of generating value.

In July, Emerson acquired Zedis software and automation businesses to help accelerate digital transformation in the oil and gas industry.

In March 2019, Emerson formed a strategic alliance with Repsol to provide advanced subsurface geophysical technologies to expedite production.

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QRI and Emerson partner on AI-based oil and gas E&P analytics - Hydrocarbons Technology

Athena Pathways Consortium will be training 500 B.C. women in AI, machine learning and data science for at least 18 months | Credit: Shutterstock

What happened: Industry group launching new program to expand artificial intelligence expertise for hundreds of B.C. women

Why it matters: Initiative will try to improve gender balance in male-dominated tech sector

B.C.s artificial intelligence community wants to get smarter about correcting its considerable gender imbalance.

The Athena Pathways Consortium officially launched Thursday (March 5) with the goal of training 500 B.C. women in AI, machine learning and data science over the next 18 months.

Its the almost hidden costs of not doing this that are particularly pernicious, said AInBC executive director Steve Lowry, whose industry association is spearheading the initiative backed by a mix of private sector and post-secondary players.

When you look at organizations that arent gender-balanced or diverse enough, they underperform.

Athena Pathways sees the University of B.C., Simon Fraser University, the B.C. Institute of Technology and Northeastern Universitys new Vancouver campus developing courses and workshops for women at high school and post-secondary levels as well as those already in the workforce.

While the initial phase of the program, budgeted at $682,000, will offer 300 scholarships and is set to run 18 months, Lowry said Athena Pathways could extend beyond that as it hunts for additional donors.

In addition to AInBC and the aforementioned post-secondary institutions, Athena Pathways is receiving support from Vancouver-based Canadas Digital Technology Supercluster, D-Wave Systems Inc., Metaoptima Technology Inc. and Tech Resources Inc. (TSX:TECK.B), among others.

B.C.s technology sector is among the most male-dominated in Canada.

Women account for 18.3% of Vancouvers tech workforce, according to a 2019 report from real estate services firm CBRE Group Inc.

Thats the second-largest gender disparity among Canadian cities, tied with Saskatoon.

The only Canadian city to fare worse in the rankings is Victoria, where women compose 15% of the technology workforce.

The B.C. Tech Association unveiled 15 recommendations for the tech sector in December 2019in a bid to create an ecosystem much better at recruiting and retaining women.

Among the recommendations curated from a series of workshops, panels and roundtables:

Set targets and publish the results. Ask if your employee diversity mirrors that of your customers.

Leverage your teams network: ask women in your organizations to identify people theyve worked with in the past and would like to work with again.

Ask yourself if talent is promotable before someone takes parental leave consider promoting before they take leave.

Put in place salary bands for each role, track and communicate pay vs. industry averages (compa ratios). Establish and enforce minimum salaries for each role.

Our objective is to make this easy for people by giving concrete, actionable suggestions, B.C. Tech Association CEO Jill Tipping told Business in Vancouver in December.

Were not trying to be one size fits all. Were trying to give you a menu of options and challenge you to find at least one that would work for your business.

torton@biv.com

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B.C.'s Athena industry initiative aims to train 500 women in AI - Business in Vancouver

People with limb injuries severe enough to require an amputation have few options to regain meaningful function in their arms or legs. Commercial prostheses, even modern ones, dont come close to what nature created, enabling movement that feels disjointed and artificial.

In a new study published Wednesday in Science Translational Medicine, a team of University of Michigan surgeons and computational scientists report a new procedure that captures electrical signals from nerves in the arm severed during an amputation and uses them to guide fine movements of a prosthetic hand. The work is one of a number of efforts underway to better integrate human physiology with robotics to improve the functioning of artificial limbs.

In the new technique, a flap of muscle is wrapped around the severed nerves to allow the nerves to grow and fire electrical signals. The signals are then picked up by an implant placed in patients the implant serves as a connection between the nervous system and a computer that patients are later hooked up to. Finally, a machine learning program interprets those nerve signals to allow patients to move a prosthetic hand seamlessly.

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The procedure described in the paper was initially tried on four patients, and the researchers report that the implant worked well in these volunteers for up to 300 days, which is how long the patients were observed. The patients were able to pick up small toy blocks and food cans and make a fist or pinch fingers together. The technology will need to be miniaturized and made wireless.

This is not the first such attempt to improve the state of prosthetics. Researchers at Brigham and Womens Hospital and MIT, for instance, have developed a new amputation method thus far tested in people requiring below- and above-knee injuries to help preserve patients sense of proprioception; the team has reported that patients who have undergone the experimental surgery feel as though the advanced prosthetics they wear are a natural extension of their body.

STAT spoke with Cynthia Chestek, a biomedical engineer at the University of Michigan and an author of the new study, to learn more. This interview has been lightly edited and condensed.

Why is it difficult to integrate prosthetic control interfaces with nerves?During amputation, a nerve is cut and while that nerve continues to carry signals about intended movements, its really hard to get those signals out. People for many, many years have tried to get prosthetic control down from a nerve, but the physics is just terrible. Its very hard to record these very small [nerve] signals, and its hard to put anything inside the nerve because it causes a lot of scarring. The state of the art right now is to record from whatever muscles are left at the skin, and that both gives you a very small signal and usually its not the signal that you want. So, for example, youre often using an elbow signal to make a hand open and close or operating a foot switch to make a hand open and close.

What is the advancement here?Our surgeon developed a technique where he takes a very small piece of muscle and he wraps it around the end of the amputated nerve. And then the nerve goes into that piece of muscle, and then what would have been an extremely small signal becomes a large electrical signal.

What happens next?So, then my team takes these electrical signals and we apply a variety of machine learning algorithms to them. And this enables our study participants to then control a prosthetic hand in real time. And these are the largest ever nerve signals ever recorded from a human being, and were showing what that enables us to do with a prosthetic hand. If youre trying to get a signal from a nerve, youre actually closer to 10 to 20 microvolts, whereas we get hundreds of microvolts and sometimes more.

Why does just wrapping a piece of muscle lead to this expanded nerve signal?The nerve is looking for a target to regrow into. And the nerve keeps growing until it finds something. Another major benefit of the surgery is as the nerve is growing into the muscle, it prevents a lot of pain [in a condition known as neuroma]. But now the nerve is happy, its found its muscle and the reason the signals get larger is because as muscles fire, they create this big electric field around them. So one nerve might be controlling a whole piece of muscle and thats going to create a much bigger signal.

What do you ask the volunteers in the study to do, and how do they feel about the prosthetic?We just ask the person to move as they normally would. We calibrate the machine learning [to these movements]. But we learn from that information and then use those signals to control the hand. So if the volunteers want to move the thumb across the hand, they just have to imagine it because the learning is in our algorithm, and not in the person. And importantly, after this surgery, they have so far all felt like they can move their fingers when theyre flexing that small muscle graft.

Does it take long for people to get used to it?It works on the first try. We were able to just ask people to make a bunch of movements that were showing them on the screen and then theyre able to replicate that with the hand. Weve increased the difficulty [of the movements] over the last couple of years.

Whats new about the algorithm?Were using a lot of the same sort of algorithms that are under the hood of autonomous vehicles. But whats new that patients are able to do is theyre able to control the position of their fingers better. Theres no commercially available system in which [they] can precisely control the location of the end of [their] thumb. So I think probably the coolest thing that we were able to show is our few participants positioning the point of their thumbs in two dimensions, and thats really useful if youre trying to pick up things. If you cant orient your thumb around an object, its very hard to grasp anything. And Im not aware of any other way of doing that because it really requires nerve signals since so much of your thumb muscles are in the hand.

Whats next?The cool thing about this surgery is it works on any type of amputation. Thats the good news. To take this [technology] home, this is still a report of the pilot study this still needs to be replicated to work in more people moving forward.

We are trying to figure out how to get people off the computer cart. Everything that weve done so far has been six feet away from a computer cart [and people come in once or twice a week for the trial]. We want people to be able to do this with an implantable device so we can move away from the cart. We hope that, however many years it takes, this is something that one day enables people to control the fingers of the prosthetic hand at home and in their daily lives.

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Surgery and AI give amputees better control of prosthetic hand - STAT

Nine days before the World Health Organization alerted the world to the threat posed by COVID-19, an artificial intelligence-powered startup led by the University of Torontos Kamran Khan had already spotted the first signs of an unusual outbreak.

In an interview with CNBC, Khan explained how his company, BlueDot, was able to scour big data and spot the emergence of the novel coronavirus before anyone else.

He said BlueDotuses machine learning and natural language processing to comb through masses of data, which are then reviewed by doctors and computer programmers who create threat reports.

We dont use artificial intelligence to replace human intelligence, we basically use it to find the needles in the haystack and present them to our team, said Khan, an associate professor at the Institute of Health Policy, Management and Evaluation at the Dalla Lana School of Public Health and an infectious disease physician at St. Michaels Hospital.

He said his experience treating patients during the SARS outbreak in 2003 inspired him to start BlueDot.

What I learned during SARS is, lets not get caught flatfooted, lets anticipate rather than react.

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T's Kamran Khan on how his startup used AI to spot the coronavirus before anyone else: CNBC - News@UofT

Healthcare artificial intelligence market is estimated to be US$ 3,120 Mn in 2018 and is anticipated to grow at a CAGR of 25.9% over the forecast period owing to digitalization of medical device and patient registries

PUNE, India, March 5, 2020 /PRNewswire/ --In terms of revenue, the global healthcare artificial intelligence market is estimated to be US$ 3,120 Mn in 2018 and is anticipated to reach US$ 24,700 Mn by 2027 growing at a CAGR of 25.9% over the forecast period.

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The increasing use of electronic patient registry and medical device registries is leading to generation of potential datasets for application of AI technologies and deriving predictive insights. Electronic patient registry or electronic health record (EHR) are used by hospitals and clinics to collect observational medical data of their patients. This data is collected and analyzed by a web-based software and can be made available to the medical community, government agencies and research organizations as per their requirement. It allows professionals in healthcare and other industries to analyze available treatments and how patients with various characteristics and medical history respond to these treatments. In a similar way, medical device registry is used to collect, store and retrieve data to medical devices and equipment used for healthcare delivery. The trend of electronically storing patient and device data in healthcare sector has been witnessing growth in past few years due to the digital revolution.

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Major players such as McKesson Corporation, IBM and others have introduced their EHR products. The rising adoption of electronic patient and medical device registry has led to generation of huge datasets which can be optimally utilized for analytical predictive purposes. AI and advanced analytics enable healthcare providers to extract patient-specific information from connected medical devices instead of having to analyze large, time-consuming and complicated datasets, thus propelling the growth of global healthcare artificial intelligence market. Such specific patient information can aid them to offer personalized medicines and diagnostics. For instance, Qualetics Data Machines Inc. offers an intelligence platform for healthcare industry which provides incisive insights using artificial intelligence, machine learning, natural language processing and predictive analysis coupled with data obtained from patient registries.

In other such instance, Saykara, Inc. has developed an AI based virtual assistant for physicians utilizing speech recognition technology, which listens in the background during attending any patient and automatically generates notes which is later updated in the EHR system. These application of AI technologies in combination with EHR systems are enhancing healthcare delivery and user experience thus enhancing the growth of global healthcare artificial intelligence market. Going forward deployment of patient and medical device registries on cloud platform further deepens the market penetration of these electronic registries thus creating extensive potential application for AI technologies. For instance, SyTrue in partnership with Microsoft has introduced Azure, cloud platform of Microsoft, based solution to manage health records through natural language processing technology. Thus, growing digitalization of patient and medical device registries are expected to boost the growth of global healthcare artificial intelligence market globally.

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The detailed research study provides qualitative and quantitative analysis of healthcare artificial intelligence market. The market has been analyzed from demand as well as supply side. The demand side analysis covers market revenue across regions and further across all the major countries. The supply side analysis covers the major market players and their regional and global presence and strategies. The geographical analysis done emphasizes on each of the major countries across North America, Europe, Asia Pacific, Middle East & Africa and Latin America.

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Global Healthcare Artificial Intelligence Market was Estimated to Grow at 25.9% CAGR During the Forecast Period Due to the Rising Adoption of...

DeepMind is best known for AI that easily defeated the worlds best Go and StarCraft II players in recent years. The Alphabet-owned research lab is now applying its existing work to help researchers combating COVID-19.

To test for a virus and develop a vaccine, scientists must first understand how it functions, specifically the structure of viral proteins. Its a lengthy process that takes months and might not always yield results. Researchers in recent years have turned to computer predictions, with DeepMinds deep learning system known as AlphaFold.

Work on the coronavirus is under way at labs around the world. DeepMind hopes to aid that research by releasing structure predictions of several understudied proteins associated with SARS-CoV-2, the virus that causes COVID-19.

We emphasize that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific communitys interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics.

The team is quick to note that the data shared today is not the main focus of current therapeutic efforts, but might help general understanding. There are several other caveats, with DeepMind providing confidence scores and only tackling proteins that are a challenge for conventional modeling approaches.

Its important to note that our structure prediction system is still in development and we cant be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlierCASP13 system. We confirmed that our system provided an accurate prediction for theexperimentally determinedSARS-CoV-2 spike protein structure shared in theProtein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful.

According to DeepMind, it was encouraged to share its COVID-19 research with the general scientific community after consulting with structural biologists and virologists in the UK.

Normally wed wait to publish this work until it had been peer-reviewed for an academic journal. However, given the potential seriousness and time sensitivity of the situation, were releasing the predicted structures as we have them now, under anopen licenseso that anyone can make use of them.

Interested researchers can download the structureshere, and can read more technical details about these predictions in a document included with the data.

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Alphabets DeepMind hopes to aid researchers with AI insight into COVID-19 virus structure - 9to5Google

There is no doubt that Artificial Intelligence (AI) has captured global imagination and attention. But in the business world, the rate of adoption of artificial intelligence has lagged behind the level of interest through 2019. Even though we hear that most business leaders believe AI provides a competitive advantage, up until recently, some industry watchers have pegged enterprise adoption at between 4% and 14%.

Clare Mortimer is the Cognitive & Analytics Leader at IBM Services.

But as we enter 2020, we are seeing an uptick, not only in interest but in AI adoption. And that uptick is reflected in the results of a new survey commissioned by IBM in late 2019. From Roadblock to Scale: The Global Sprint Towards AI polled more than 4,500 technology decision makers including over 500 from the UK. We wanted to gauge the current and future states of AI deployment to better understand the landscape and the challenges. As youll see, its about to change dramatically.

UK results from the survey indicate that while there is still work to be done, advances in data discovery and IT management, skills training and new innovations in AI explainability are driving the rate of AI adoption faster than many predicted.

Slightly over one in five (21%) of the UK audience surveyed report that their company has actively deployed AI as part of business operations, while nearly half (46%) say that their company is exploring but have not yet deployed AI. These numbers are significantly higher than some industry watchers have estimated to date. Some of the more telling data points from the UK survey include:

Based on our interactions and the results of this study, we expect to see organisations not only adopt AI but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work in a multitude of ways. Specifically, the UK audience cites data security (34%), process automation (25%), and customer care (22%) as the top three ways that AI is being used by their organisations.

I see the excitement building with clients every day as they realize the potential of AI. Just last year we announced that SPF Private Clients, one of the UKs leading financial services firms, has adopted IBM Watson and IBM Cloud to develop Ava, a new AI virtual Help-to-Buy mortgage adviser. Ava helps first time home buyers onto the property ladder by offering round the clock support for queries and a mortgage indication in just three minutes.

When I look at insights from the report, which was conducted by the firm Morning Consult, and think about my interactions with clients, the roadblocks to AI adoption have been a prime concern. Theyre the reason weve worked to lower the barriers of entry and make AI more accessible to businesses.

Its why we have invested in building capability in our services teams across Europe and launched the Data Science Elite Team in 2018, to build a global group of experienced technical professionals who help companies solve and scale AI solutions to real problems. Its what drove us to introduce innovations like Watson OpenScale, to help mitigate bias in AI models; Watson AutoAI, which literally uses AI to build AI models; and its what led us to create the first-of-its-kind container-based data analytics platform, Cloud Pak for Data, that lets people run Watson with any cloud services.

Weve also, taken skills training and support to whole new levels, with robust data science work with several open source standards bodies, such as The Open Group and The Linux Foundation. And in response to the need for transparency and explainability in AI, IBM has been directly involved in working with The European Commission to shape its Ethics Guidelines for Trustworthy AI designed to set a global standard in advancing AI ethically and responsibly.

There is no doubt that 2019 was a productive year for AI, but 2020 is shaping up to bring an exciting new level of commitment and with it, exciting new outcomes for business and society.

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AI: from experimentation to adoption - TechRadar