Monthly Archives: May 2020

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Unless you have been living under a rock for the last 10 years, its highly likely youve heard something about artificial intelligence.

This something might be along the lines of high-profile and exciting new innovations such as self-driving cars, computers capable of diagnosing cancer or automated customer support. Otherwise, it may be in connection to terms such as Industry 4.0 and the Internet of Things, terms intended to encapsulate how innovation is driving a change in the interactions and relationship between humans and technology and what an increasingly AI-powered future might look like as a result.

For business leaders, AI is a learning curve they will have to begin climbing sooner rather than later to remain competitive and relevant in an increasingly AI-driven future. The challenge faced by many business leaders isnt a lack of awareness of AI, accepting the changes AI is driving in their industry or acknowledging the opportunities. Rather the challenge is a question: where and how do I begin?

When confronting any new challenge, our instinct is often to focus on the problem and learning more about it. Thus, the answer to the question rapidly evolves into more questions such as what is AI, how does it work, what are the use cases and how can my business benefit from AI.

Whilst these are great questions with exciting answers, the problem with focusing on the answers to these questions too early in the AI journey can lead down a rabbit hole. AI is a highly complex and rapidly evolving field with new technologies and applications emerging virtually every day: what AI is and can do today is not likely to reflect what it is capable of achieving tomorrow.

The rapid growth of AI means starting the journey with learning about AI represents a double-edged sword to business leaders. On one hand, the rate of innovation makes AI appear increasingly complex and nebulous with new capabilities and possibilities constantly emerging, creating a feeling of never quite understanding enough and fostering a lets wait and see mentality. This makes it tempting and easy to delay decision making and taking action in the hopes that innovation will slow, the learning curve will flatten, any kinks will have been worked out and AI can be neatly packaged allowing businesses to simply plug and play to reap the benefits.

On the other, history often isnt kind to companies that read the writing on the wall and acted too late. Which puts us back to square one where and how to begin?

AI is an exciting field and I highly encourage you to ask all the questions mentioned previously and more. But for the reasons presented above, beginning your journey by trying to wrap your head around the wonderful world of opportunity afforded by AI isnt necessarily the best first step.

In his book Start With Why, Simon Sinek explains that asking why is a highly effective approach to creating a shared understanding, vision, purpose and goal for a social movement, idea or business. When it comes to AI, asking why shifts the focus from trying to comprehend the all the unknowns the different types of technology, how they work and the potential benefits - to understanding the fundamental motivations that drive your business to explore AI in the first place.

Getting to the heart of your business why can be a journey in and of itself, but its vital preparation that establishes the underlying values and principles that will shape and influence your AI strategy. All future discussions, activities and expectations should be clearly linked to these core principles the why and is essential to orienting your business on its AI journey. In the stormy seas of AI, your why is the compass that will help avoid distractions or becoming side-tracked and keep you heading true North. Realising any kind of value through AI will be significantly hindered without first determining the challenges and opportunities your business must address in order to thrive and defining clear objectives that will enable it to do so.

This may all seem rather obvious, but its worth reaffirming. AI is exciting, complex and en masse, poorly understood: a combination that can often result in people wasting significant time, energy and even money chasing their tails trying to understand what AI is and how to extract value from it without understanding what they really want to achieve from it.

Having a clear why will help you define goals and develop a strategy to achieve it. Your why will also help foster resilience. The fact is that AI does come with a learning curve and developing a new AI solution or integrating an existing solution into your business operations is unlikely to be a simple, straightforward or entirely pain free. AI is not a short term play, but part of a long term game: being clear on the fundamental business challenges it addresses will help you make smarter decisions from the start and maintain momentum when the going gets tough and the challenge seems insurmountable.

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How Should Your Business Get Started With AI? Begin With Why - Forbes

Often time, we have faced equal excitement and dilemma over introducing Artificial Intelligence in the military. Even the Department of Defense (DoD) of the USA is caught up in the same predicament. However, the recent findings by the Defense Intelligence Agency (DIA) may finally have a solution to this problem. Apart from it, this study also proves who shall be a better judge, human, or AI in case of analyzing an enemy activity.

Throughout history, humans are perceived to be an expert in comprehending and deducing a situation, even in comparison to AI. But according to this research experiment by DIA shows that both AI and humans have different risk tolerances during data scarcity. AI can be more vigilant about concluding similar situations when data is inadequate. The early results showcase how machine and human analysts fare in understanding critical data-driven decision making and match one another in matters of vital national security fields.

In May 2019, DIA had announced the program of the Machine-Assisted Analytic Rapid-Repository System (MARS). The mission was proposed to reframe the agencys understanding of data centers and support the departments development of AI in the future. Hence, the system was designed to engage users from early development to cut back risks on national security challenges or priorities change and improve continuously.

Terry Busch, Division Chief of Integrated Analysis and Methodologies within the Directorate for Analysis at DIA and technical director of MARS, says, Earlier this year our team had set up a test between a human and AI. The program will ask both humans and machines to discern if a ship is in the United States based on a certain amount of information at an April 27 National Security Powered by AI webinar four analysts came up with four methodologies, and the machine came up with two different methodologies, and that was cool. They all agreed that this particular ship was in the United States.

Therefore first test results were positive as both AI and humans algorithms made identical observations based on the given dataset by Automatic Identification System (AIS) feed. The second stage results, however, had a change of opinions. The team disconnected the worldwide ships tracker, AIS. Now the objective was to identify how it impacts the confidence levels of the AI analyzing methods. This procedure was essential to understand what goes into AI algorithms and how it affects it and by what magnitude.

And the output was surprising. After the removal of information sources, both the machine and humans were left with access to common source materials like social media, open-source kinds of things, or references to the ship being in the United States. While the confidence level lowered in machines, human fed algorithms ended up coming off as pretty overconfident. Ironically both the systems deemed itself accurate.

This experiment highlights how military leaders shall base their reliance on AI for decision driven situations. While it does not infer defense intelligence work to be handled over software, it does emphasize the need to build insights in a deficient data scenario. That also means teaching analysts to become data literate to understand things like confidence intervals and other statistical terms. The chief concern from machine-based AI was bias and retraining itself to error. Addressing these issues can help to foster both AI systems into a collaborative and complementing platform.

Busch explains, The data is currently outpacing the tradecraft or the algorithmic work that were doing. And were focusing on getting the data readyWeve lifted places where the expert is the arbiter of what is accurate.

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Machine AI Is On The Race to Overtake Human AI - Analytics Insight

Harjinder Sandhu, CEO of Saykara

For at least a decade, healthcare luminaries have been predicting the coming AI revolution. In other fields, AI has evolved beyond the hype and has begun to showcase real and transformative applications: autonomous vehicles, fraud detection, personalized shopping, virtual assistants, and so on. The list is long and impressive. But in healthcare, despite the expectations and the tremendous potential in improving the delivery of care, the AI revolution is just getting started. There have been definite advancements in areas such as diagnostic imaging, logistics within healthcare, and speech recognition for documentation. Still, the realm of AI technologies that impact the cost and quality of patient care continues to be rather narrow today.

Why has AI been slow in delivering change in the care processes of healthcare? With a wealth of new AI algorithms and computing power ready to take on new challenges, the limiting function in AIs successful application has been the availability of meaningful data sets to train on. This is surprising to many, given that EHRs were supposed to have solved the data barrier.

The promise of EHRs was that they would create a wealth of actionable data that could be leveraged for better patient care. Unfortunately, this promise never fully materialized. Most of the interesting information that can be captured in the course of patient care either is not or is captured minimally or inconsistently. Often, just enough information is recorded in the EHR to support billing and is in plain text (not actionable) form. Worse, documentation requirements have had a serious impact on physicians, to whom it ultimately fell to input much of that data. Burnout and job dissatisfaction among physicians have become endemic.

EHRs didnt create the documentation challenge. But using an EHR in the exam room can significantly detract from patient care. Speech recognition has come a long way since then, although it hasnt changed that fundamental dynamic of the screen interaction that takes away from the patient. Indeed, using speech recognition, physicians stare at the screen even more intently as they must be mindful of mistakes that the speech recognition system may generate.

Having been involved in the advancement of speech recognition in the healthcare domain and been witness to its successes and failures, I continue to believe that the next stage in the evolution of this technology would be to free physicians from the tyranny of the screen. To evolve from speech recognition systems to AI-based virtual scribes that listen to doctor-patient conversations, creating notes, and entering orders.

Using a human scribe solves a significant part of the problem for physicians scribes relieve the physician of having to enter data manually. For many physicians, a scribe has allowed them to reclaim their work lives (they can focus on patients rather than computers) as well as their personal lives (fewer evening hours completing patient notes). However, the inherent cost of both training and then employing a scribe has led to many efforts to build digital counterparts, AI-based scribes that can replicate the work of a human scribe.

Building an AI scribe is hard. It requires a substantially more sophisticated system than the current generation of speech recognition systems. Interpreting natural language conversation is one of the next major frontiers for AI in any domain. The current generation of virtual assistants, like Alexa and Siri, simplify the challenge by putting boundaries on speech, forcing a user, for example, to express a single idea at a time, within a few seconds and within the boundaries of a list of skills that these systems know how to interpret.

In contrast, an AI system that is listening to doctor-patient conversations must deal with the complexity of human speech and narrative. A patient visit could last five minutes or an hour, the speech involves at least two parties (the doctor and the patient), and a patients visit can meander to irrelevant details and branches that dont necessarily contribute to a physician making their diagnosis.

As a result of the complexity of conversational speech, it is still quite early for fully autonomous AI scribes. In the meantime, augmented AI scribes, AI systems augmented by human power, are filling in the gaps of AI competency and allowing these systems to succeed while incrementally chipping away at the goal of making these systems fully autonomous. These systems are beginning to do more than simply relieve doctors of the burden of documentation, though that is obviously important. The real transformative impact will be from capturing a comprehensive set of data about a patient journey in a structured and consistent fashion and putting that into the medical records, thereby building a base for all other AI applications to come.

About Harjinder Sandhu

Harjinder Sandhu, CEO of Saykara, a company leveraging the power and simplicity of the human voice to make delivering great care easier while streamlining physician workflow

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The Impending Artificial Intelligence Revolution in Healthcare - Op-Ed - HIT Consultant

In the last decade, artificial intelligence (AI) has progressed from near-science fiction to common reality across a range of business applications. In intelligence analysis, AI is already being deployed to label imagery and sort through vast troves of data, helping humans see the signal in the noise. But what the intelligence community is now doing with AI is only a glimpse of what is to come. The future will see smartly deployed AI supercharging analysts ability to extract value from information.

Exploring new possibilities

We expect several new tasks for AI, which will likely fall into one of these three categories:

Delivering new models. The rapid pace of modern decision-making is among the biggest challenges leaders face. AI can add value by helping provide new ways to more quickly and effectively deliver information to decision-makers. Our model suggests that by adopting AI at scale, analysts can spend up to 39 percent more time advising decision-makers.

Developing people. Analysts need to keep abreast of new technologies, new services, and new happenings across the globenot just in annual trainings, but continuously. AI could help bring continuous learning to the widest scale possible by recommending courseware based on analysts work.

Maintaining the tech itself. Beyond just following up on AI-generated leads, organizations will likely also need to maintain AI tools and to validate their outputs so that analysts can have confidence when using them. Much of this validation can be performed as AI tools are designed or training data is selected.

Avoiding pitfalls

Intelligence organizations must be clear about their priorities and how AI fits within their overall strategy. Having clarity about the goals of an AI tool can also help leaders communicate their vision for AI to the workforce and alleviate feelings of mistrust or uncertainty about how the tools will be used.

Intelligence organizations should also avoid investing in empty technologyusing AI without having access to the data it needs to be successful.

Survey results suggest that analysts are most skeptical of AI, compared to technical staff, management, or executives. To overcome this skepticism, management will need to focus on educating the workforce and reconfiguring business processes to seamlessly integrate the tools into workflows. Also, having an interface that allowed the analyst to easily scan the data underpinning a simulated outcome or view a representation of how the model came to its conclusion would go a long way toward that analyst incorporating the technology as part and parcel of his or her workflow.

While having a workforce that lacks confidence in AIs outputs can be a problem, however, the opposite may also turn out to be a critical challenge. With so much data at their disposal, analysts could start implicitly trusting AI, which can be quite dangerous.

But there are promising ways in which AI could help analysts combat human cognitive limitations. They would be very good at continuously conducting key assumptions checks, analyses of competing hypotheses, and quality of information checks.

How to get started today

Across a government agency or organization, successful adoption at scale would require leaders to harmonize strategy, organizational culture, and business processes. If any of those efforts are misaligned, AI tools could be rejected or could fail to create the desired value. Leaders need to be upfront about their goals for AI projects, ensure those goals support overall strategy, and pass that guidance on to technology designers and managers to ensure it is worked into the tools and business processes. Establishing a clear AI strategy can also help organizations frame decisions about what infrastructure and partners are necessary to access the right AI tools for an organization.

Tackling some of the significant nonanalytical challenges analyst teams face could be a palatable way to introduce AI to analysts and build their confidence in it. Today, analysts are inundated with a variety of tasks, each of which demands different skills, background knowledge, and the ability to communicate with decision-makers. For any manager, assigning these tasks across a team of analysts without overloading any one individual or delaying key products can be daunting. AI could help pair the right analyst to the right task so that analysts can work to their strengths more often, allowing work to get done better and more quickly than before.

AI is not coming to intelligence work; it is already there. But the long-term success of AI in the intelligence community depends as much on how the workforce is prepared to receive and use it as any of the 1s and 0s that make it work.

Learn how to assess your AI readiness

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The impact of artificial intelligence on intelligence analysis - Reuters

Glaucoma, the leading global cause of irreversible blindness, currently affects over 60 million people, which is predicted to double by 2040 as the global population ages. The new test, which is combined with Artificial Intelligence (AI) technology could help accelerate clinical trials, and eventually may be used in detection and diagnostics.

Lead researcher Professor Francesca Cordeiro, UCL Institute of Ophthalmology, Imperial College London, and Western Eye Hospital Imperial College Healthcare NHS Trust, said: We have developed a quick, automated and highly sensitive way to identify which people with glaucoma are at risk of rapid progression to blindness.

The clinical trial has been sponsored by UCL, funded by Wellcome and published in the Expert Review of Molecular Diagnostics.

The test, called DARC (Detection of Apoptosing Retinal Cells), involves injecting a fluorescent dye into the bloodstream that attaches to retinal cells and illuminates the cells that are in the process of apoptosis, a form of programmed cell death.

Currently, a major challenge with detecting eye diseases is that specialists often disagree when viewing the same scans, so the researchers have incorporated an AI algorithm into their method.

The AI was initially trained by analysing the retinal scans of the healthy control subjects. The AI was then tested on the glaucoma patients. Those taking part in the AI study were followed up 18 months after the main trial period to see whether their eye health had deteriorated.

The researchers were able to accurately predict progressive glaucomatous damage 18 months before that seen with the current gold standard OCT retinal imaging technology, as every patient with a DARC count over a certain threshold was found to have progressive glaucoma at follow-up.

Professor Francesca Cordeiro said that adding biomarkers are urgently needed for glaucoma, to speed up clinical trials as the disease progresses slowly so it can take years for symptoms to change: These results are very promising as they show DARC could be used as a biomarker when combined with the AI-aided algorithm.

What is really exciting, and actually unusual when looking at biological markers, is that there was a clear DARC count threshold above which all glaucoma eyes went on to progress.

The team is also applying the test to rapidly detect cell damage caused by a number of other conditions such as neurodegenerative conditions that involve the loss of nerve cells, including age-related macular degeneration, multiple sclerosis, and dementia, as well as testing people with lung disease. The team hope that by the end of this year the test may help to assess people with breathing difficulties from COVID-19.

First author of the study Dr Eduardo Normando, Imperial College London and Western Eye Hospital Imperial College Healthcare NHS Trust, said: Being able to diagnose glaucoma at an earlier stage, and predict its course of progression, could help people to maintain their sight, as treatment is most successful if provided at an early stage of the disease.

After further research in longitudinal studies, we hope that our test could have widespread clinical applications for glaucoma and other conditions.

The AI-supported technology has recently been approved by both the UKs Medicines and Healthcare products Regulatory Agency and the USAs Food and Drug Administration as an exploratory endpoint for testing a new glaucoma drug in a clinical trial.

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Discover how AI can help with early detection of glaucoma - Health Europa

One Stop Systems, Inc., a leader in specialized high-performance edge computing, will host a webinar on how to bring supercomputing performance to data at the edge for AI applications with leaders from NVIDIAandMarvell.

The panel will be moderated by OSS chief sales and marketing officer, Jim Ison. He will be joined by Ying Yin Shih, director of product management at NVIDIA and Larry Wikelius, vice president, ecosystem and solutions at Marvell.

The webinar will discuss solving hard problems in defense, aerospace, autonomous vehicles, security, personalized medicine and more by leveraging massive NVIDIA enabled AI solutions designed for the unique size, power and rugged requirements of the edge.

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A new computing paradigm is emerging that puts computing and storage resources for AI and HPC workflows not in the datacenter but on the edge near the data source. Applications continue to emerge for this new paradigm in diverse areas, including autonomous vehicles, precision medicine, battlefield command and control, industrial automation, and media and entertainment.

The common elements of these solutions are high data rate acquisition, high-speed and low-latency storage, and efficient, high-performance compute analyticsall configured to meet the unique environmental conditions of edge deployments.

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This webinar will explain the challenges and solutions for meeting these requirements by describing real world use cases being developed and deployed today. OSS will present use cases of its edge-focusedAI on the Flyproducts currently deployed in intelligence, surveillance and reconnaissance (ISR), genomic analysis, location-based entertainment, and autonomous driving.

NVIDIA and Marvell will describe their collaboration to support NVIDIA CUDAand CUDA-X software platform on the high performance, low power Armarchitecture with Marvells ThunderX2server processor. The combination provides a powerful tool in the expanding set of solutions for edge-focused AI infrastructure. The panel will discuss how CUDA for Arm provides an effectiveAI on the Flybuilding block for edge-oriented solutions where high performance, memory bandwidth and low power are essential.

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OSS to Host AI at the Edge Webinar with Leaders from NVIDIA and Marvell - AiThority

AI is rapidly transforming the way that companies interact with their customers. MIT Technology Review Insights survey of 1,004 business leaders, The global AI agenda, found that customer service is the most active department for AI deployment today. By 2022, it will remain the leading area of AI use in companies (say 73% of respondents), followed by sales and marketing (59%), a part of the business that just a third of surveyed executives had tapped into as of 2019.

In recent years, companies have invested in customer service AI primarily to improve efficiency, by decreasing call processing and complaint resolution times. Organizations known as leaders in the customer experience field have also looked toward AI to increase intimacyto bring a deeper level of customer understanding, drive customization, and create personalized journeys.

Genesys, a software company with solutions for contact centers, voice, chat, and messaging, works with thousands of organizations all over the world. The goal across each one of these 70 billion annual interactions, says CEO Tony Bates, is to delight someone in the moment and create an end-to-end experience that makes all of us as individuals feel unique.

Experience is the ultimate differentiator, he says, and one that is leveling the playing field between larger, traditional businesses and new, tech-driven market entrantsproduct, pricing, and branding levers are ineffective without an experience that feels truly personalized. Every time I interact with a business, I should feel better after that interaction than I felt before.

In sales and marketing processes, part of the personalization involves predictive engagementknowing when and how to interact with the customer. This depends on who the customer is, what stage of the buying cycle they are at, what they are buying, and their personal preferences for communication. It also requires intelligence in understanding where the customer is getting stuck and helping them navigate those points.

Marketing segmentation models of the past will be subject to increasing crossover, as older generations become more digitally skilled. The idea that you can create personas, and then use them to target or serve someone, is over in my opinion, says Bates. The best place to learn about someone is at the businesss front door [website or call center] and not at the backdoor, like a CRM or database.

The survey data shows that for industries with large customer bases such as travel and hospitality, consumer goods and retail, and IT and telecommunications, customer care and personalization of products and services are among the most important AI use cases. In the travel and hospitality sector, nearly two-thirds of respondents cite customer care as the leading application.

The goal of a personalized approach should be to deliver a service that empathizes with the customer. For customer service organizations measured on efficiency metrics, a change in mindset will be requiredsome customers consider a 30-minute phone conversation as a truly great experience. But on the flip side, I should be able to use AI to offset that with quick transactions or even use conversational AI and bots to work on the efficiency side, says Bates.

With vast transaction data sets available, Genesys is exploring how they could be used to improve experiences in the future. We do think that there is a need to share information across these large data sets, says Bates. If we can do this in an anonymized way, in a safe and secure way, we can continue to make much more personalized experiences. This would allow companies to join different parts of a customer journey together to create more interconnected experiences.

This isnt a straightforward transition for most organizations, as the majority of businesses are structured in silosthey havent even been sharing the data they do have, he adds. Another requirement is for technology vendors to work more closely together, enabling their enterprise customers to deliver great experiences. To help build this connectivity, Genesys is part of industry alliances like CIM (Cloud Innovation Model), with tech leaders Amazon Web Services and Salesforce. CIM aims to provide common standards and source code to make it easier for organizations to connect data across multiple cloud platforms and disparate systems, connecting technologies such as point-of-sale systems, digital marketing platforms, contact centers, CRM systems, and more.

Data sharing has the potential to unlock new value for many industries. In the public sector, the concept of open data is well known. Publicly available data sets on transport, jobs and the economy, security, and health, among many others, allow developers to create new tools and services, thus solving community problems. In the private sector there are also emerging examples of data sharing, such as logistics partners sharing data to increase supply chain visibility, telecommunications companies sharing data with banks in cases of suspected fraud, and pharmaceutical companies sharing drug research data that they can each use to train AI algorithms.

In the future, companies might also consider sharing data with organizations in their own or adjacent industries, if it were to lead to supply chain efficiencies, improved product development, or enhanced customer experiences, according to the MIT Technology Review Insights survey. Of the 11 industries covered in the study, respondents from the consumer goods and retail sector proved the most enthusiastic about data sharing, with nearly a quarter describing themselves as very willing to share data, and a further 57% being somewhat willing.

Other industries can learn from financial services, says Bates, where regulators have given consumers greater control over their data to provide portability between banks, fintechs, and other players, in order to access a wider range of services. I think the next big wave is that notion of a digital profile where you and I can control what we do and dont want to shareI would be willing to share a little bit more if I got a much better experience.

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How AI is changing the customer experience - MIT Technology Review

The Mulholland family at home: Kyle, Conor, Melissa and Emma. (Photo by Scott Eklund/Red Box Pictures)

Conor, now 5, is already benefitting from AI in his life. After undergoing 12 surgeries by the age of 2 to deal with the effects caused by PUV and related issues, he was diagnosed with autism at age 3, and has had difficulty speaking. He is working with speech therapists, and is also benefitting from an app called Helpicto that uses AI to convert spoken words into a series of images.

Created by French company Equadex, the app, too, came from the heart several of the companys employees have personal experiences with autism. Equadex created Helpicto using Azure Cognitive Services and the Microsoft Azure cloud platform.

Its very hard to have a child not be able to speak or communicate with you, Mulholland says. He was 4 when he first said, Mama. When he did, it was amazing to hear it.

Conor also learned how to say Dada and Amma, or Emma, for his 7-year-old sister, who dotes on him.

Its really sweet to see how caring she is and how much she wants him to be successful, Mulholland says. She helps him practice on words. I can see her being in a field of study someday thats very focused on helping others, whether its as a teacher or doctor, or something along those lines. Her life will be forever changed because of having a brother like him.

Mulholland says she is humbled by all the support she has had from her husband, Kyle, an accountant who stays at home with Conor, to Microsoft for giving her a platform to tell her story, to the companies that want to hear and embrace it.

I always encourage people, Dont pigeonhole yourself, think of ways that you can really harness technology to drive greater good, because sometimes those solutions are right in front of you, she says. And imagine how great of a world we could live in if we had more stories like this.

Top photo: Melissa Mulholland with son Conor. (Photo by Scott Eklund/Red Box Pictures)

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How one Microsoft mom inspired health care companies to embrace the life-saving potential of AI | Transform - Microsoft

Monitors display a video showing facial recognition software in use at the headquarters of the artificial intelligence company Megvii, in Beijing, May 10, 2018. Beijing is putting billions of dollars behind facial recognition and other technologies to track and control its citizens.

Gilles Sabri | The New York Times

The world is facing its biggest health crisis in decades but one of the world's most promising technologies artificial intelligence (AI) isn't playing the major role some may have hoped for.

Renowned AI labs at the likes of DeepMind, OpenAI, Facebook AI Research, and Microsoft have remained relatively quiet as the coronavirus has spread around the world.

"It's fascinating how quiet it is," said Neil Lawrence, the former director of machine learning at Amazon Cambridge.

"This (pandemic) is showing what bulls--t most AI hype is. It's great and it will be useful one day but it's not surprising in a pandemic that we fall back on tried and tested techniques."

Those techniques include good, old-fashioned statistical techniques and mathematical models. The latter is used to create epidemiological models, which predict how a disease will spread through a population. Right now, these are far more useful than fields of AI like reinforcement learning and natural-language processing.

Of course, there are a few useful AI projects happening here and there.

In March, DeepMind announced that it hadused a machine-learning technique called "free modelling" to detail the structures of six proteins associated with SARS-CoV-2, the coronavirus that causes the Covid-19 disease.Elsewhere, Israeli start-up Aidoc is using AI imaging to flag abnormalities in the lungs and a U.K. start-up founded by Viagra co-inventor David Brown is using AI to look for Covid-19 drug treatments.

Verena Rieser, a computer science professor at Heriot-Watt University, pointed out that autonomous robots can be used to help disinfect hospitals and AI tutors can support parents with the burden of home schooling. She also said "AI companions" can help with self isolation, especially for the elderly.

"At the periphery you can imagine it doing some stuff with CCTV," said Lawrence, adding that cameras could be used to collect data on what percentage of people are wearing masks.

Separately, a facial recognition system built by U.K. firm SCC has also been adapted to spot coronavirus sufferers instead of terrorists.In Oxford, England, Exscientia is screening more than 15,000 drugs to see how effective they are as coronavirus treatments. The work is being done in partnership withDiamond Light Source, the U.K.'s national "synchotron."

But AI's role in this pandemic is likely to be more nuanced than some may have anticipated. AI isn't about to get us out of the woods any time soon.

"It's kind of indicating how hyped AI was," said Lawrence, who is now a professor of machine learning at the University of Cambridge. "The maturity of techniques is equivalent to the noughties internet."

AI researchers rely on vast amounts of nicely labeled data to train their algorithms, but right now there isn't enough reliable coronavirus data to do that.

"AI learns from large amounts of data which has been manually labeled a time consuming and expensive task," said Catherine Breslin, a machine learning consultant who used to work on Amazon Alexa.

"It also takes a lot of time to build, test and deploy AI in the real world. When the world changes, as it has done, the challenges with AI are going to be collecting enough data to learn from, and being able to build and deploy the technology quickly enough to have an impact."

Breslin agrees that AI technologies have a role to play. "However, they won't be a silver bullet," she said, adding that while they might not directly bring an end to the virus, they can make people's lives easier and more fun while they're in lockdown.

The AI community is thinking long and hard about how it can make itself more useful.

Last week, Facebook AI announced a number of partnerships with academics across the U.S.

Meanwhile, DeepMind's polymath leader Demis Hassabis is helping the Royal Society, the world's oldest independent scientific academy, on a new multidisciplinary project called DELVE (Data Evaluation and Learning for Viral Epidemics). Lawrence is also contributing.

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A.I. can't solve this: The coronavirus could be highlighting just how overhyped the industry is - CNBC

Machine learning has quickly gone from niche field to crucial component of innumerable software stacks, but that doesnt mean its easy. The tools needed to create and manage it are enterprise-grade and often enterprise-only but Determined AI aims to make them more accessible than ever by open-sourcing its entire AI infrastructure product.

The company created its Determined Training Platform for developing AI in an organized, reliable way the kind of thing that large companies have created (and kept) for themselves, the team explained when they raised an $11 million Series A last year.

Machine learning is going to be a big part of how software is developed going forward. But in order for companies like Google and Amazon to be productive, they had to build all this software infrastructure, said CEO Evan Sparks. One company we worked for had 70 people building their internal tools for AI. There just arent that many companies on the planet that can withstand an effort like that.

At smaller companies, ML is being experimented with by small teams using tools intended for academic work and individual research. To scale that up to dozens of engineers developing a real product there arent a lot of options.

Theyre using things like TensorFlow and PyTorch, said Chief Scientist Ameet Talwalkar. A lot of the way that work is done is just conventions: How do the models get trained? Where do I write down the data on which is best? How do I transform data to a good format? All these are bread and butter tasks. Theres tech to do it, but its really the Wild West. And the amount of work you have to do to get it set up theres a reason big tech companies build out these internal infrastructures.

Determined AI, whose founders started out at UC Berkeleys AmpLab (home of Apache Spark), has been developing its platform for a few years, with feedback and validation from some paying customers. Now, they say, its ready for its open source debut with an Apache 2.0 license, of course.

We have confidence people can pick it up and use it on their own without a lot of hand-holding, said Sparks.

You can spin up your own self-hosted installation of the platform using local or cloud hardware, but the easiest way to go about it is probably the cloud-managed version that automatically provisions resources from AWS or wherever you prefer and tears them down when theyre no longer needed.

The hope is that the Determined AI platform becomes something of a base layer that lots of small companies can agree on, providing portability to results and standards so youre not starting from scratch at every company or project.

With machine learning development expected to expand by orders of magnitude in the coming years, even a small piece of the pie is worth claiming, but with luck, Determined AI may grow to be the new de facto standard for AI development in small and medium businesses.

You can check out the platform on GitHub or at Determined AIs developer site.

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Determined AI makes its machine learning infrastructure free and open source - TechCrunch