Documents obtained by Reuters suggest that, in at least three cases, Google’s researchers were requested to refrain from being critical of AI technology.

A “sensitive topics” review was established by Google earlier this year to catch papers which cast a negative light on AI ahead of their publication.

Google asks its scientists to consult with legal, policy, and public relations teams prior to publishing anything on topics which could be deemed sensitive like sentiment analysis and categorisations of people based on race and/or political affiliation.

The new review means that papers from Google’s expert researchers which raise questions about AI developments may never be published. Reuters says four staff researchers believe Google is interfering with studies into potential technology harms.

Google recently faced scrutiny after firing leading AI ethics researcher Timnit Gebru.

Gebru is considered a pioneer in the field and researched the risks and inequalities found in large language models. She claims to have been fired by Google over an unpublished paper and sending an email critical of the company’s practices.

In an internal email countering Gebru’s claims, Head of Google Research Jeff Dean wrote:

“We’ve approved dozens of papers that Timnit and/or the other Googlers have authored and then published, but as you know, papers often require changes during the internal review process (or are even deemed unsuitable for submission). 

Unfortunately, this particular paper was only shared with a day’s notice before its deadline — we require two weeks for this sort of review — and then instead of awaiting reviewer feedback, it was approved for submission and submitted.

A cross-functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why.”

While it’s one word against another, it’s not a great look for Google.

“Advances in technology and the growing complexity of our external environment are increasingly leading to situations where seemingly inoffensive projects raise ethical, reputational, regulatory or legal issues,” Reuters reported one of Google’s documents as saying.

On its public-facing website, Google says that its scientists have “substantial” freedom—but that’s increasingly appearing like it’s not the case.

(Photo by Mitchell Luo on Unsplash)

The post Google is telling its scientists to give AI a ‘positive’ spin appeared first on AI News.

FRA’s report is titled Getting The Future Right and opens with some of the ways AI is already making lives better—such as helping with cancer diagnosis, and even predicting where burglaries are likely to take place.

“The possibilities seem endless,” writes Michael O’Flaherty, Director of the FRA, in the report’s foreword. “But how can we fully uphold fundamental rights standards when using AI?”

The FRA interviewed over a hundred public administration officials, private company staff, and a diverse range of experts, in a bid to answer that question.

With evidence of algorithms having biases which could lead to automating societal issues like racial profiling—it’s a question that needs answering if the full potential of AI is going to be unlocked for the whole of society.

O’Flaherty says:

“AI is not infallible, it is made by people – and humans can make mistakes. That is why people need to be aware when AI is used, how it works and how to challenge automated decisions. The EU needs to clarify how existing rules apply to AI. And organisations need to assess how their technologies can interfere with people’s rights both in the development and use of AI.

“We have an opportunity to shape AI that not only respects our human and fundamental rights but that also protects and promotes them.”

AI is being used in almost every industry in some form or another—if not already, it will be soon.

Biases in AI are more dangerous in some industries than others. Policing is an obvious example, but in areas like financial services it could mean one person being given a loan or mortgage compared to another.

Without due transparency, these biases could happen without anyone knowing the reasons behind such decisions—it could simply be because someone grew up in a different neighbourhood. Each automated decision has a very real human impact.

The FRA calls for the EU to:

• Make sure that AI respects ALL fundamental rights – AI can affect many rights – not just privacy or data protection. It can also discriminate or impede justice. Any future AI legislation has to consider this and create effective safeguards.
• Guarantee that people can challenge decisions taken by AI – people need to know when AI is used and how it is used, as well as how and where to complain. Organisations using AI need to be able to explain how their systems take decisions.
• Assess AI before and during its use to reduce negative impacts – private and public organisations should carry out assessments of how AI could harm fundamental rights.
• Provide more guidance on data protection rules – the EU should further clarify how data protection rules apply to AI. More clarity is also needed on the implications of automated decision-making and the right to human review when AI is used.
• Assess whether AI discriminates – awareness about the potential for AI to discriminate, and the impact of this, is relatively low. This calls for more research funding to look into the potentially discriminatory effects of AI so Europe can guard against it.
• Create an effective oversight system – the EU should invest in a more ‘joined-up’ system to hold businesses and public administrations accountable when using AI. Authorities need to ensure that oversight bodies have adequate resources and skills to do the job.

The EU has increased its scrutiny of “big tech” companies like Google in recent years over concerns of invasive privacy practices and abusing their market positions. Last week, AI News reported that Google had controversially fired leading AI ethics researcher Timnit Gebru after she criticised her employer in an email.

Google chief executive Sundar Pichai wrote in a memo: “We need to accept responsibility for the fact that a prominent black, female leader with immense talent left Google unhappily.

“It’s incredibly important to me that our black, women, and under-represented Googlers know that we value you and you do belong at Google.”

Gebru gave an interview to the BBC this week in which she called Google and big tech “institutionally racist”. With that in mind, the calls made in the FRA’s report seem especially important to heed.

You can download a full copy of the FRA’s report here.

(Photo by Guillaume Périgois on Unsplash)

The post EU human rights agency issues report on AI ethical considerations appeared first on AI News.

Algorithmia’s 2021 Enterprise Trends in Machine Learning report features the views of 403 business leaders involved with machine learning initiatives.

Diego Oppenheimer, CEO of Algorithmia, says:

“COVID-19 has caused rapid change which has challenged our assumptions in many areas. In this rapidly changing environment, organisations are rethinking their investments and seeing the importance of AI/ML to drive revenue and efficiency during uncertain times.

Before the pandemic, the top concern for organisations pursuing AI/ML initiatives was a lack of skilled in-house talent. Today, organisations are worrying more about how to get ML models into production faster and how to ensure their performance over time.

While we don’t want to marginalise these issues, I am encouraged by the fact that the type of challenges have more to do with how to maximise the value of AI/ML investments as opposed to whether or not a company can pursue them at all.”

The main takeaway is that AI budgets are significantly increasing. 83 percent of respondents said they’ve increased their budgets compared to last year.

Despite a difficult year for many companies, business leaders are not being put off of AI investments—in fact, they’re doubling-down.

In Algorithmia’s summer survey, 50 percent of respondents said they plan to spend more on AI this year. Around one in five even said they “plan to spend a lot more.”

76 percent of businesses report they are now prioritising AI/ML over other IT initiatives. 64 percent say the priority of AI/ML has increased relative to other IT initiatives over the last 12 months.

With unemployment figures around the world at their highest for several years – even decades in some cases – it’s at least heartening to hear that 76 percent of respondents said they’ve not reduced the size of their AI/ML teams. 27 percent even report an increase.

43 percent say their AI/ML initiatives “matter way more than we thought” and close to one in four believe their AI/ML initiatives should have been their top priority sooner. Process automation and improving customer experiences are the two main areas for AI investments.

While it’s been all good news so far, there are AI deployment issues being faced by many companies which are yet to be addressed.

Governance is, by far, the biggest AI challenge being faced by companies. 56 percent of the businesses ranked governance, security, and auditability issues as a concern.

Regulatory compliance is vital but can be confusing, especially with different regulations between not just countries but even states. 67 percent of the organisations report having to comply with multiple regulations for their AI/ML deployments.

The next major challenge after governance is with basic deployment and organisational challenges. 

Basic integration issues were ranked by 49 percent of businesses as a problem. Furthermore, more job roles are being involved with AI deployment strategies than ever before—it’s no longer seen as just the domain of data scientists.

However, there’s perhaps some light at the end of the tunnel. Organisations are reporting improved outcomes when using dedicated, third-party MLOps solutions.

While keeping in mind Algorithmia is a third-party MLOps solution, the report claims organisations using such a platform spend an average of around 21 percent less on infrastructure costs. Furthermore, it also helps to free up their data scientists—who spend less time on model deployment.

You can find a full copy of Algorithmia’s report here (requires signup)

The post Algorithmia: AI budgets are increasing but deployment challenges remain appeared first on AI News.

Data from Dealroom was used for the State of European Tech report. Dealroom defines deep tech as 16 fields: Artificial Intelligence, Machine Learning, Big Data, Augmented Reality, Virtual Reality, Drones, Autonomous Driving, Blockchain, Nanotech, Robotics, Internet of Things, 3D Technology, Computer Vision, Connected Devices, Sensors Technology, and Recognition Technology (NLP, image, video, text, speech recognition).

In 2019, there was $10.2 billion capital invested in European deep tech. In 2020, that dropped to $8.9 billion:

I think it’s fair to say that 2020 has been a tough year for most people and businesses. Economic uncertainty – not just from COVID-19 but also trade wars, Brexit, and a rather tumultuous US presidential election – has naturally led to fewer investments and people tightening their wallets.

For just one example, innovative satellite firm OneWeb was forced to declare bankruptcy earlier this year after crucial funding it was close to securing was pulled during the peak of the pandemic. Fortunately, OneWeb was saved following an acquisition by the UK government and Bharti Global—but not all companies have been so fortunate.

Many European businesses will now be watching the close-to-collapse Brexit talks with hope that a deal can yet be salvaged to limit the shock to supply lines, prevent disruption to Europe’s leading financial hub, and help to build a friendly relationship going forward with a continued exchange of ideas and talent rather than years of bitterness and resentment.

The report shows the UK has retained its significant lead in European tech investment and startups this year:

Despite the uncertainties, the UK looks unlikely to lose its position as the hub of European technology anytime soon.

Investments in European tech as a whole should bounce back – along with the rest of the world – in 2021, with promising COVID-19 vaccines rolling out and hopefully some calm in geopolitics.

94 percent of survey respondents for the report stated they have either increased or maintained their appetite to invest in the European venture asset class. Furthermore, a record number of US institutions have participated in more than one investment round in Europe this year—up 36% since 2016.

You can find a full copy of the State of European Tech report here.

The post State of European Tech: Investment in ‘deep tech’ like AI drops 13% appeared first on AI News.

Timnit Gebru is considered a pioneer in the field and researched the risks and inequalities found in large language models.

Gebru claims she was fired by Google over an unpublished paper and sending an email critical of the company’s practices.

The paper questions whether language models can be too big, who benefits from them, and whether they can increase prejudice and inequalities. Some recent cases validate her claims about large models and datasets in general.

For example, MIT was forced to remove a large dataset earlier this year called 80 Million Tiny Images. The dataset is popular for training AIs but was found to contain images labelled with racist, misogynistic, and other unacceptable terms.

A statement on MIT’s website claims it was unaware of the offensive labels and they were “a consequence of the automated data collection procedure that relied on nouns from WordNet.”

The statement goes on to explain the 80 million images contained in the dataset – with sizes of just 32×32 pixels – meant that manual inspection would be almost impossible and couldn’t guarantee all offensive images would be removed.

Gebru reportedly sent an email to the Google Brain Women and Allies listserv that is “inconsistent with the expectations of a Google manager.”

In the email, Gebru expressed her frustration with a perceived lack of progress at Google in hiring women at Google. Gebru claimed she was also told not to publish a piece of research and advised employees to stop filling out diversity paperwork because it didn’t matter.

On top of the questionable reasons for her firing, Gebru says her former colleagues were emailed saying she offered her resignation—which she claims was not the case:

Platformer obtained an email from Jeff Dean, Head of Google Research, which was sent to employees and offers his take on Gebru’s claims:

“We’ve approved dozens of papers that Timnit and/or the other Googlers have authored and then published, but as you know, papers often require changes during the internal review process (or are even deemed unsuitable for submission). Unfortunately, this particular paper was only shared with a day’s notice before its deadline — we require two weeks for this sort of review — and then instead of awaiting reviewer feedback, it was approved for submission and submitted.

A cross functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why. It ignored too much relevant research — for example, it talked about the environmental impact of large models, but disregarded subsequent research showing much greater efficiencies. Similarly, it raised concerns about bias in language models, but didn’t take into account recent research to mitigate these issues.”

Dean goes on to claim Gebru made demands which included revealing the identities of the individuals he and Google Research VP of Engineering Megan Kacholia consulted with as part of the paper’s review. If the demands weren’t met, Gebru reportedly said she would leave the company.

It’s a case of one word against another, but – for a company already in the spotlight from both the public and regulators over questionable practices – being seen to fire an ethics researcher for calling out problems is not going to be good PR.

(Image Credit: Timnit Gebru by Kimberly White/Getty Images for TechCrunch under CC BY 2.0 license)

The post Google fires ethical AI researcher Timnit Gebru after critical email appeared first on AI News.

The analysis was commissioned by the UK government in October 2018 and will receive a formal response.

Algorithms bring substantial benefits to businesses and individuals able to use them effectively. However, increasing evidence suggests biases are – often unconsciously – making their way into algorithms and creating an uneven playing field.

The CDEI is the UK government’s advisory body on the responsible use of AI and data-driven technology. CDEI has spent the past two years examining the issue of algorithmic bias and how it can be tackled.

Adrian Weller, Board Member for the Centre for Data Ethics and Innovation, said:

“It is vital that we work hard now to get this right as adoption of algorithmic decision-making increases. Government, regulators, and industry need to work together with interdisciplinary experts, stakeholders, and the public to ensure that algorithms are used to promote fairness, not undermine it.

The Centre for Data Ethics and Innovation has today set out a range of measures to help the UK to achieve this, with a focus on enhancing transparency and accountability in decision-making processes that have a significant impact on individuals.

Not only does the report propose a roadmap to tackle the risks, but it highlights the opportunity that good use of data presents to address historical unfairness and avoid new biases in key areas of life.”

The report focuses on four key sectors where algorithmic bias poses the biggest risk: policing, recruitment, financial services, and local government.

Today’s facial recognition algorithms are relatively effective when used on white males, but research has consistently shown how ineffective they are with darker skin colours and females. The error rate is, therefore, higher when facial recognition algorithms are used on some parts of society over others.

In June, Detroit Police chief Editor Craig said facial recognition would misidentify someone around 96 percent of the time—not particularly comforting when they’re being used to perform mass surveillance of protests.

Craig’s comments were made just days after the ACLU (American Civil Liberties Union) lodged a complaint against Detroit Police following the harrowing wrongful arrest of black male Robert Williams due to a facial recognition error.

And that’s just one example of where AI can unfairly impact some parts of society over another.

“Fairness is a highly prized human value,” the report’s preface reads. “Societies in which individuals can flourish need to be held together by practices and institutions that are regarded as fair.”

Ensuring fairness in algorithmic decision-making

Transparency is required for algorithms. In financial services, a business loan or mortgage could be rejected without transparency simply because a person was born in a poor neighbourhood. Job applications could be rejected not on a person’s actual skill but dependent on where they were educated.

Such biases exist in humans and our institutions today, but automating them at scale is a recipe for disaster. Removing bias from algorithms is not an easy task but if achieved would lead to increased fairness by taking human biases out of the equation.

“It is well established that there is a risk that algorithmic systems can lead to biased decisions, with perhaps the largest underlying cause being the encoding of existing human biases into algorithmic systems. But the evidence is far less clear on whether algorithmic decision-making tools carry more or less risk of bias than previous human decision-making processes. Indeed, there are reasons to think that better use of data can have a role in making decisions fairer, if done with appropriate care.

When changing processes that make life-affecting decisions about individuals we should always proceed with caution. It is important to recognise that algorithms cannot do everything. There are some aspects of decision-making where human judgement, including the ability to be sensitive and flexible to the unique circumstances of an individual, will remain crucial.”

The report’s authors examined the aforementioned four key sectors to determine their current “maturity levels” in algorithmic decision-making.

In recruitment, the authors found rapid growth in the use of algorithms to make decisions at all stages. They note that adequate data is being collected to monitor outcomes but found that understanding of how to avoid human biases creeping in is lacking.

“More guidance is needed on how to ensure that these tools do not unintentionally discriminate against groups of people, particularly when trained on historic or current employment data.”

The financial services industry has relied on data to make decisions for longer than arguably any other to determine things like how likely it is an individual can repay a debt.

“Specific groups are historically underrepresented in the financial system, and there is a risk that these historic biases could be entrenched further through algorithmic systems.”

CDEI found limited use of algorithmic decision-making in UK policing but found variance across forces with regards to both usage and managing ethical risks.

“The use of data analytics tools in policing carries significant risk. Without sufficient care, processes can lead to Review into bias in algorithmic decision-making: Executive summary Centre for Data Ethics and Innovation 8 outcomes that are biased against particular groups, or systematically unfair.

In many scenarios where these tools are helpful, there is still an important balance to be struck between automated decision-making and the application of professional judgement and discretion.”

Finally, in local government, CDEI noted an increased use of algorithms to inform decision-making but most are in their early stages of deployment. Such tools can be powerful assets for societal good – like helping to plan where resources should be allocated to maintain vital services – but can also carry significant risks.

“Evidence has shown that certain people are more likely to be overrepresented in data held by local authorities and this can then lead to biases in predictions and interventions.”

The CDEI makes a number of recommendations in its report but among them is:

• Clear and mandatory transparency over how algorithms are used for public decision-making and steps taken to ensure the fair treatment of individuals.
• Full accountability for organisations implementing such technologies.
• Improving the diversity of roles involved with developing and deploying decision-making tools.
• Updating model contracts and framework agreements for public sector procurement to incorporate minimum standards around the ethical use of AI.
• The government working with regulators to provide clear guidance on the collection and use of protected characteristic data in outcome monitoring and decision-making processes. They should then encourage the use of that guidance and data to address current and historic bias in key sectors.
• Ensuring that the Equality and Human Rights Commission has sufficient resources to investigate cases of alleged algorithmic discrimination.

CDEI is overseen by an independent board which is made up of experts from across industry, civil society, academia, and government; it is an advisory body and does not directly set policies. The Department for Digital, Culture, Media & Sport is consulting on whether a statutory status would help the CDEI to deliver its remit as part of the National Data Strategy.

You can find a full copy of the CDEI’s report into tackling algorithmic bias here (PDF)

(Photo by Matt Duncan on Unsplash)

The post CDEI launches a ‘roadmap’ for tackling algorithmic bias appeared first on AI News.

Everyone knows how important adequate sleep is for recovery. However, patients in pain – or just insomniacs like me – can struggle to get the sleep they need.

“Rest is a critical element to a patient’s care, and it has been well-documented that disrupted sleep is a common complaint that could delay discharge and recovery,” said Theodoros Zanos, Assistant Professor at Feinstein Institutes’ Institute of Bioelectronic Medicine.

When a patient finally gets some shut-eye, the last thing they want is to be woken up to have their vitals checked—but such measurements are, well, vital.

In a paper published in Nature Partner Journals, the researchers detailed how they developed a deep-learning predictive tool which predicts a patient’s stability overnight. This prevents multiple unnecessary checks being carried out.

Vital sign measurements from 2.13 million patient visits at Northwell Health hospitals in New York between 2012 and 2019 were used to train the AI. Data included heart rate, systolic blood pressure, body temperature, respiratory rate, and age. A total of 24.3 million vital signs were used.

When tested, the AI misdiagnosed just two of 10,000 patients in overnight stays. The researchers noted how nurses on their usual rounds would be able to account for the two misdiagnosed cases.

According to the paper, around 20-35 percent of a nurse’s time is spent keeping records of patients’ vitals. Around 10 percent of their time is spent collecting vitals. On average, a nurse currently has to collect a patient’s vitals every four to five hours.

With that in mind, it’s little wonder medical staff feel so overburdened and stressed. These people want to provide the best care they can but only have two hands. Using AI to free up more time for their heroic duties while simultaneously improving patient care can only be a good thing.

The AI tool is being rolled out across several of Northwell Health’s hospitals.

The post AI helps patients to get more rest while reducing staff workload appeared first on AI News.

Most of us are hardwired to hate not speaking directly to a human when we have a problem—following years of irritating voicemail systems. However, perhaps the only thing worse is being on hold for an uncertain amount of time due to overwhelmed call centres.

Chatbots have come a long way and can now quickly handle most queries within minutes. Where a human is required, the reduced demand through using virtual agent technology (VAT) means customers can get the assistance they need more quickly.

The COVID-19 pandemic has greatly increased the adoption of VAT as businesses seek to maintain customer service through such a challenging time.

According to IBM’s study, 99 percent of organisations reported increased customer satisfaction by integrating virtual agents. Human agents also report increased satisfaction and IBM says those “who feel valued and empowered with the proper tools and support are more likely to deliver a better experience to customers.”

68 percent of leaders cite improving the human agent experience as being among their key reasons for adopting VAT. There’s also economic incentive, with the cost of replacing a dissatisfied agent who leaves a business estimated at as much as 33 percent of the exiting employee’s salary.

IBM claims that VAT performance in the past has only been studied through individual case studies. The company set out, alongside Oxford Economics, to change that by surveying 1,005 respondents from companies using VAT daily.

Businesses wondering whether virtual assistants are worth the investment may be interested to know that 96 percent of the respondents “exceeded, achieved, or expect to achieve” their anticipated return.

On average, companies which have implemented VAT have increased their revenue by three percent.

IBM is one of the leading providers of chatbots through its Watson Assistant solution. While there’s little reason to doubt the claims made in the report, it’s worth keeping in mind that it’s not entirely unbiased.

Watson Assistant has gone from strength-to-strength and appears to have been among the few things which benefited from the pandemic. Between February and August, Watson Assistant usage increased by 65 percent.

You can download a full copy of IBM’s report here.

(Photo by Volodymyr Hryshchenko on Unsplash)

The post IBM study highlights rapid uptake and satisfaction with AI chatbots appeared first on AI News.

Researchers from Microsoft explained their machine learning model in a paper on preprint repository arXiv.

The model uses VIsual VOcabulary pre-training (VIVO) which leverages large amounts of paired image-tag data to learn a visual vocabulary.

A second dataset of properly captioned images is then used to help teach the AI how to best describe the pictures.

“Ideally, everyone would include alt text for all images in documents, on the web, in social media – as this enables people who are blind to access the content and participate in the conversation. But, alas, people don’t,” said Saqib Shaikh, a software engineering manager with Microsoft’s AI platform group.

Overall, the researchers expect the AI to deliver twice the performance of Microsoft’s existing captioning system.

In order to benchmark the performance of their new AI, the researchers entered it into the ‘nocaps’ challenge. As of writing, Microsoft’s AI now ranks first on its leaderboard.

“The nocaps challenge is really how are you able to describe those novel objects that you haven’t seen in your training data?” commented Lijuan Wang, a principal research manager in Microsoft’s research lab.

Developers wanting to get started with building apps using Microsoft’s auto-captioning AI can already do so as it’s available in Azure Cognitive Services’ Computer Vision package.

Microsoft’s impressive SeeingAI application – which uses computer vision to describe an individual’s surroundings for people suffering from vision loss – will be updated with features using the new AI.

“Image captioning is one of the core computer vision capabilities that can enable a broad range of services,” said Xuedong Huang, Microsoft CTO of Azure AI Cognitive Services.

“We’re taking this AI breakthrough to Azure as a platform to serve a broader set of customers,” Huang continued. “It is not just a breakthrough on the research; the time it took to turn that breakthrough into production on Azure is also a breakthrough.”

The improved auto-captioning feature is also expected to be available in Outlook, Word, and PowerPoint later this year.

(Photo by K8 on Unsplash)

The post Microsoft’s new AI auto-captions images for the visually impaired appeared first on AI News.

AI is helping with COVID-19 research around the world and much of it is being powered by NVIDIA GPUs. It’s a daunting task, new drugs often cost over $2.5 billion in research and development — doubling every nine years — and 90 percent of efforts fail.

Nvidia wants to help speed up discoveries of vital medicines while reducing costs

“COVID-19 hits home this urgency [for new tools],” Huang says.

Huang announced NVIDIA Clara Discovery—a suite of tools for assisting scientists in discovering lifesaving new drugs.

NVIDIA Clara combines imaging, radiology, and genomics to help develop healthcare AI applications. Pre-trained AI models and application-specific frameworks help researchers to find targets, build compounds, and develop responses.

Dr Hal Barron, Chief Scientific Officer and President of R&D at GSK, commented:

“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise.

NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients.

Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the UK’s outstanding scientists.”

Researchers can now use biomedical-specific language models for their work, thanks to a breakthrough in natural language processing. This means researchers can organise and activate large datasets, research literature, and sort through papers or patents on existing treatments and other vital real-world data.

“Where there are popular industry tools, our computer scientists accelerate them,” Huang said. “Where no tools exist, we develop them—like NVIDIA Parabricks, Clara Imaging, BioMegatron, BioBERT, NVIDIA RAPIDS.”

We’re all hoping COVID-19 research – using such powerful new tools available to scientists – can lead to a vaccine within a year or two, when they have often taken a decade or longer to create.

“The use of big data, supercomputing, and artificial intelligence has the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” commented Editor Weatherall, Ph.D., Head of Data Science and AI at AstraZeneca.

During his keynote, Huang provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer – which will be used to further healthcare research – the Cambridge-1.

NVIDIA has established partnerships with companies leading the fight against COVID-19 and other viruses including AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. These partners can harness Cambridge-1 for their vital research.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

And, for organisations wanting to set up their own AI supercomputers, NVIDIA has announced DGX SuperPODs as the world’s first turnkey AI infrastructure. The solution was developed from years of research for NVIDIA’s own work in healthcare, automotive, healthcare, conversational AI, recommender systems, data science and computer graphics.

While Huang has a nice kitchen, I’m sure he’d like to be back on the big stage for his GTC 2021 keynote. We’d certainly all love COVID-19 to be well and truly in the rear-view mirror.

(Photo by Elwin de Witte on Unsplash)

The post GTC 2020: Using AI to help put COVID-19 in the rear-view mirror appeared first on AI News.