Category Archives: Artificial Intelligence
Administration And Congressional Update On Artificial Intelligence In The US – Technology – United States – Mondaq News Alerts
29 April 2021
Akin Gump Strauss Hauer & Feld LLP
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On April 9, 2021, the Office of Management and Budget (OMB)submitted President Biden's discretionary funding request (the "Request") to Congressfor Fiscal Year (FY) 2022. The Request lays out the President'sdiscretionary funding recommendations across a wide range of policyareas, including a strategy for investing in emerging technologyareas, maintaining economic competitiveness and national securityand positioning the U.S. to out-compete China. The Request ishigh-level and did not include proposed legislative text.
The President's Request recommends:
On April 21, 2021, a group of bipartisan lawmakers reintroducedthe Endless Frontier Act (H.R.2731 and S.1260) to establish a new Directorate forTechnology (the "Directorate") at the NSF, a regionaltechnology hub program and require a strategy and report oneconomic security, research, innovation, manufacturing and jobcreation. The bill would authorize $100 billion over five years forthe Directorate to strengthen U.S. leadership in criticaltechnology areas through innovation, research, commercializationand education and ensure that the U.S. maintains its competitiveedge in technologies of the future.
The legislation identifies ten initial technology domains forthe new NSF Directorate to fund research, including AI and machinelearning, semiconductors, quantum computing, advancedcommunications technology, cybersecurity and synthetic biology.
Additionally, the Directorate is authorized to:
The Endless Frontier Act also establishes a novel Supply ChainResiliency and Crisis Response Program at the Department ofCommerce. The new program would monitor supply chainvulnerabilities and provide investments to diversify supply chainsfor products critical to national security. Lastly, the billproposes a $2.4 billion investment to enhance and expand theManufacturing USA network.
On April 21, 2021, Rep. Maxine Waters (D-CA), Chair of the HouseFinancial Services Committee, renewed the Committee's AI TaskForce. The Task Force was created during the 116th Congress toensure the responsible use of emerging and predictive technologiesin the financial sector. Rep. Bill Foster (D-IL) will continueleading the Task Force's work to examine whether emergingtechnologies in the financial services and housing industries servethe needs of consumers, investors, small businesses and thepublic.
Congress and the Biden-Harris administration continue to takeaction to ensure the U.S. maintains its global leadership intechnologies of the future, including AI. Additional investmentsand a new approach to accelerate U.S. science and technologydevelopments are beginning to materialize in light of growingconcerns that other countries are ready to challenge America'sposition on the innovation stage. The Akin Gump cross-practice AIteam continues to monitor forthcoming congressional, administrativeand private-stakeholder initiatives related to AI.
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Artificial Intelligence is on the Verge of Wearing the Hat of a Composer – Analytics Insight
Praising artificial intelligence a bit too much.
Artificial intelligence has successfully made its way in every industry including music. But it hits quiet strange to some as to how AI can be used in composing an art piece which requires acute human creativity.
But they say, Whatever you dream of is real. And this article is an exploration on that.
Apart from the fact that music serves as great source of entertainment, it also serves to quell several other disorders and issues of human mind, collectively termed as mental illness. Mental illness can be stress, over-thinking, loss of creativity, mind block and many other things that every human being faces.
Music is an effective way to overcome desolation. Researchers operating in the institute of brain, behavior and development called MAARCS have concluded music to be the best therapy for mental illness. Music is said to release endorphins and dopamines, chemicals responsible for happiness in adequate quantities. Psychologists heavily recommend music as an art therapy for individuals suffering from pathological problems such as too much stress and over thinking.
Given the fact that the music industry all over the world is prospering and growing by releasing new songs and albums every year and every month, the pressure that mounts up on lyricists and the composers is immense because meeting the deadline is an important objective.
A recent research conducted in an OpenAI laboratory on Jukebox, a famous state-of-art neural network has released its reports on experiments carried on AI to compose music and the results were promising. In this experiment, several songs with lyrics and metadata that bears the name of album, artists and the genre were fed to the jukebox and results were that new songs were produced by combining the lyrics and down-mixing of channels that produced mono audio.
Many such experiments were performed with AI to produce music and AI-powered gadgets were trained to produce lyrics could successfully produce results on genres like hip-hop, jazz and classical, opening a new door art production and creativity for musicians and the music industry on a whole.
While AI has been successful in demonstrating its powers in producing creative lyrics and musical pieces, it has certain limitations that cannot be overlooked. These limitations are as same as what is faced by the other industries the fundamental knowledge of artificial intelligence on a whole.
Many musicians, who are mostly familiar with the traditional methods of producing music, feel intimidated by the new techniques and methods, especially AI, which can be complex to be grasped at first. However, an eventual or gradual realization of its benefits can transform the music industry for good.
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Artificial Intelligence is on the Verge of Wearing the Hat of a Composer - Analytics Insight
Puget to Introduce Proprietary Software that Utilizes Artificial Intelligence to Optimize Distribution and Transportation Systems – GlobeNewswire
BOCA RATON, Fla., April 30, 2021 (GLOBE NEWSWIRE) -- Puget Technologies, Inc. (Puget; OTC PINK: PUGE), a Nevada corporation subject to reporting pursuant to Sections 13 and 15(d) of the Securities Exchange Act of 1934, as amended, announces that the companys Chief Technologies Officer (CTO), Victor Germn Quintero Toro has contributed proprietary software to Puget, subject to retained royalty rights, designed to improve the functioning of logistics in transportation and distribution systems. The methodology involved is believed to be unique and subject to protection as trade secrets, however, Puget may elect to reinforce such protection through patents in the near future.
The solutions currently available in the marketplace to manage distribution and transportation logistics are limited to just a few specifically customized applications. In contrast, Pugets software can solve extremely complex problems for its end users by customizing the myriad of variables not currently included in out-of-the-box modular software. It does so in a seamlessly integrated environment without the need for additional capital expenditures. By data mining in big data environments with advanced artificial intelligence algorithms and other proprietary trade secrets, Pugets newly acquired software is the only technology on the market today, in my opinion, that supports the majority of variables that affect these end users, commented Mr. Quintero Toro.
Designed specifically to seamlessly integrate functionality within the big data environments of existing distribution and transportation systems, the software does not replace existing technology. One of the main advantages of this solution is the optimization of companys operations since this software complements and enhances existing platforms to deliver efficiencies, enabling cost reduction without the need for a significant capital outlay. Im looking forward to commercializing this technology with Pugets assistance, Mr. Quintero Toro explained.
Mr. Quintero Toros past experience working to solve similar problems at Walmart distribution centers around the world contributed to the domain expertise needed to come up with such an innovative, integrated solution.
The software has already been beta tested in the public transportation system of the City of Manizales in the Republic of Colombia, where it achieved a 30% reduction in hydrocarbon emissions as a result of better route management. The beta test results were presented at the Congreso Latino-Iberoamericano de Investigacion de Operaciones (CLAIO), and a summary was published in the publication Annals of Operations Research and in the Journal of Heuristics.
Puget intends to commercialize this technology through licensing agreements, leveraging Puerto Rico as a springboard for rollout to Latin America and other parts of the world. The transportation and distribution problems on the Island, aggravated by unfortunate recent weather disasters, provide an opportunity for the technology to make a significant positive impact there. In addition, because of the substantial incentives provided by the Puerto Rico Incentives Code (Law No. 60 of July 1, 2019), Puget believes that the Commonwealth of Puerto Rico would be an ideal site as a worldwide research and development center, which will enable Puget to have a local presence as the team works directly with local business and government leaders to improve the Islands infrastructure.
For additional information, please contact Puget at 1-561-210-8535, by email at info@pugettechnologies.com or visit our website for continuing updates at https://pugettechnologies.com.
About Puget Technologies, Inc.Puget Technologies, Inc.(pugettechnologies.com) aspires to evolve into an innovation-focused holding company operating through a group of subsidiaries and business units that work together to empower ground-breaking companies to reach their next stage of growth. With a strategy that combines acquisitions, strategic investment strategies, and operational support,Pugetintends to provide a one-stop shop for growing companies who need access to both capital and growth resources, while enablingPugetand its stockholders to generate synergies and derive profit through pooled resources and shared goals. Pugetscurrent investment focus ranges from traditional industries like health care that are ripe for business model innovation to new markets that strive to solve big societal problems such as climate change. Publicly traded on the Pink Open Market under the ticker symbol PUGE,Pugetis committed to delivering a competitive return to investors.
DeepMap Named to Forbes AI 50 List of Most Promising Artificial Intelligence Companies of 2021 – PRNewswire
To create the list, Forbes evaluated hundreds of submissions from the U.S. and Canada. An algorithm identified the top 100 companies with the highest quantitative scores. A panel of expert AI judges then reviewed the finalists to hand-pick the 50 most compelling companies.
"We are honored to be included on the Forbes AI 50 list for the second year in a row," said Mark Wheeler, DeepMap Co-Founder and CTO. "Over the past year, we have executed on our vision to offer a global map-engine-as-a-service for a full range of autonomous driving, from hands off, to eyes off, to mind off. Our customers include the world's leading automakers and suppliers, who work with us because we enable them to develop solutions that are reliable and affordable, and offer faster time-to-market."
DeepMap recently announced DeepMap HDR (High-Definition Reference), a service for companies who are building hands-free Level 2+ driving systems using crowd-sourced maps. Complementing existing perception-based autonomy platforms, DeepMap HDR registers and aligns myriad crowd-sourced perception outputs to generate and update live, high-fidelity maps with absolute accuracy and better relative accuracy. DeepMap HDR solves a critical piece of the puzzle for companies seeking to validate and improve crowd-sourced mapping data.
Forbes partnered with venture firms Sequoia Capital and Meritech Capital to create the third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see").
About DeepMapDeepMap is accelerating safe autonomy by providing the world's best autonomous mapping and localization solutions. DeepMap delivers the technology necessary for self-driving vehicles to navigate in a complex and unpredictable environment. The company addresses three important elements: precise high-definition (HD) mapping, ultra-accurate real-time localization, and the server-side infrastructure to support massive global scaling. DeepMap was founded in 2016 and is headquartered in Palo Alto, Calif., with offices in Beijing and Guangzhou, China. Investors include Andreessen Horowitz, Accel, GSR Ventures, Generation, Goldman Sachs, NVIDIA, and Robert Bosch Venture Capital. For more information, see http://www.deepmap.ai.
Contact info: [emailprotected]
SOURCE DeepMap, Inc.
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DeepMap Named to Forbes AI 50 List of Most Promising Artificial Intelligence Companies of 2021 - PRNewswire
Gatik Named to Forbes AI 50 List of Most Promising Artificial Intelligence Companies of 2021 – GlobeNewswire
PALO ALTO, Calif., April 26, 2021 (GLOBE NEWSWIRE) -- Gatik, the market leader in automating on-road transportation networks for B2B middle-mile logistics, announced today that it has been named to the Forbes AI 50 list which highlights private companies using artificial intelligence in meaningful, business-oriented ways to create innovative services and solve complex technology problems.
Operating on fixed, repeatable routes is the most effective way to deploy autonomous vehicles safely and at scale, said Gautam Narang, Co-Founder, and CEO of Gatik. At Gatik, we take a radically divergent hybrid approach towards the system architecture, implementation & validation of our Autonomous Box Trucks. We decompose the massive, monolithic Deep Neural Networks into micro-models whose intended functionality is restricted to a very specific explainable task, and build rule-based fallback & validation systems around them. Forbes recognition of our solution validates the strength of this approach - were honored to be included on this prestigious list and be recognized as a technology innovator.
Forbes, in partnership with Sequoia Capital, evaluated hundreds of companies serving a range of industries to recognize 50 private, U.S.-based companies for their innovative use of artificial intelligence. Gatik was selected for its leadership in using AI and machine learning to establish the first autonomous Middle Mile logistics network in North America, based on factors including technology, business model, growth, customers, revenue history, and valuation.
Gatiks technology focuses on 3 technical pillars to maximize safety and efficiency for short-haul logistics: using exponentially less data for training and validation by overfitting the modular stack for known routes; using a learning-first but deterministic approach, through hyper-optimization of micro-models using rich priors from known routes; and ensuring redundancies at system and component level. Combined with the companys operational expertise, this commercial grade autonomous solution enables Gatiks customers to reduce costs, maintain capacity and keep delivery times short.
About Gatik
Gatik was founded in 2017 by veterans of the autonomous technology industry and has established offices in Palo Alto and Toronto. The company focuses on short-haul, B2B logistics for Fortune 500 retailers such as Walmart and Loblaw, and has established the first autonomous Middle Mile logistics network in North America. Gatik enables its customers to optimize their hub-and-spoke supply chain operations, enhance inventory pooling across multiple locations, reduce labor costs and meet an unprecedented demand for contactless delivery.
Media Contact
Richard SteinerT: 416-836-9185E: richard@gatik.ai
Allison MatthewsT: 952-836-9626E: allison@skyya.com
A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/b96fe7fe-dfb6-47f6-9864-0ef923320f7e
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Gatik Named to Forbes AI 50 List of Most Promising Artificial Intelligence Companies of 2021 - GlobeNewswire
The Global Artificial Intelligence (AI) Market is expected to grow by $ 76.44 bn during 2021-2025, progressing at a CAGR of almost 21% during the…
Global Artificial Intelligence (AI) Market 2021-2025 The analyst has been monitoring the artificial intelligence (AI) market and it is poised to grow by $ 76. 44 bn during 2021-2025, progressing at a CAGR of almost 21% during the forecast period.
New York, April 26, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Market 2021-2025" - https://www.reportlinker.com/p04886893/?utm_source=GNW Our report on artificial intelligence (AI) market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.The report offers an up-to-date analysis regarding the current global market scenario, latest trends and drivers, and the overall market environment. The market is driven by the prevention of fraud and malicious attacks and chatbots in AI. In addition, the prevention of fraud and malicious attacks is anticipated to boost the growth of the market as well.The artificial intelligence (AI) market analysis includes end-user segment and geographic landscape.
The artificial intelligence (AI) market is segmented as below:By End-user Retail Banking Manufacturing Healthcare Others
By Geography North America Europe APAC South America MEA
This study identifies the increased employee productivity as one of the prime reasons driving the artificial intelligence (AI) market growth during the next few years.
The analyst presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters. Our report on artificial intelligence (AI) market covers the following areas: Artificial intelligence (AI) market sizing Artificial intelligence (AI) market forecast Artificial intelligence (AI) market industry analysis
This robust vendor analysis is designed to help clients improve their market position, and in line with this, this report provides a detailed analysis of several leading artificial intelligence (AI) market vendors that include Alphabet Inc., CognitiveScale, Intel Corp., International Business Machines Corp., Microsoft Corp., Nuance Communications Inc., NVIDIA Corp., Oracle Corp., Tesla Inc., and Wipro Ltd. Also, the artificial intelligence (AI) market analysis report includes information on upcoming trends and challenges that will influence market growth. This is to help companies strategize and leverage all forthcoming growth opportunities.The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.
The analyst presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research - both primary and secondary. Technavios market research reports provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast the accurate market growth.Read the full report: https://www.reportlinker.com/p04886893/?utm_source=GNW
About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.
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The Global Artificial Intelligence (AI) Market is expected to grow by $ 76.44 bn during 2021-2025, progressing at a CAGR of almost 21% during the...
Is This How the World Ends? Artificial Intelligence and American Security – Legal Talk Network
Autonomous killer robots have certainly grabbed a lot of screen time over the years, but is our world really going to end in an AI-fueled war? Sharon Nelson and John Simek welcome Brigadier General Patrick Huston to discuss his role at the Pentagon and dispel some of the most common myths about AI and its military applications. As a self-described near pacifist, General Huston shares his perspective on the militarys commitments to ethical and legal development of AI defenses and emphasizes the importance of creating partnerships between the government and the best and brightest AI experts in private industry.
Brigadier General Patrick Huston is the Assistant Judge Advocate General for Military Law and Operations in the Pentagon, where he is keenly focused on privacy and the legal and ethical development of AI, cybersecurity and other emerging technologies.
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Is This How the World Ends? Artificial Intelligence and American Security - Legal Talk Network
A Citizens Guide To Artificial Intelligence: A Nice Focus On The Societal Impact Of AI – Forbes
Artificial Intelligence
A Citizens Guide to Artificial Intelligence, by a cast of thousands (John Zerilli, John Danaher, James Maclaurin, Colin Gavaghan, Alistair Knott, Joy Liddicoat, and Merel Noorman) is a nice high level view of some of the issues surrounding the adoption of artificial intelligence (AI). The author bios describe them as all lawyers and philosophers except for Noorman, and with that crowd its no surprise the book is much better at discussing the higher level impacts than AI itself. Luckily, theres a whole lot more of the latter than there is the former. The real issue is theyre better at explaining things than at coming to logical conclusions. Well get to that, but its still a useful read.
The issue about understanding of AI is shown early, when they first give a nice explanation of false positives and false negatives, but then write Its hard to measure the performance of unsupervised learning systems because they dont have a specific task. As this column has repeatedly mentioned, the key use of unsupervised learning is the task of detecting anomalous behavior, especially when anomalies are sparse. The difference between supervised and unsupervised learning is in knowing what youre looking for:
Supervised: Hey, heres attack XYZ!
Unsupervised learning: Hey, heres this weird thing that might be an attack!
So skim chapter one to get to the good stuff. Chapter two is about transparency, and Figure 2.1 is a nice little graphic about the types of transparency they are describing. What I really like is that accessibility is in the top tier. It doesnt matter if the designers and owners of a system are claiming to be responsible and are also inspecting the results to check accuracy; if the information isnt accessible to all parties involved in and impacted by the AI system, theres a problem.
The one issue I have with the transparency chapter is in the section human explanatory standards. They seem to be claiming that since were hard to understand, why should we expect better from AI systems? They state, A crucial premise of this chapter has been that standards of transparency should be applied consistently, regardless of whether were dealing with humans or machines. Yes, a silly premise. We didnt create ourselves. Were building AI systems for the same reasons weve built other thing in order to do things easier or more accurately than we can do them. Since were building the system, we should expect to be able to require more transparency to be built into a system.
The next three chapters are on bias, responsibility & liability, and control. They are good overviews of those issue. The control chapter is intriguing because its not just about us controlling the systems, but also covers issues about giving up control to systems.
Privacy is a critical issue, and chapter six is nice coverage of that. The most interesting section is on inferred data. We talk about inference engines, making inferences on the data; but the extension of that to privacy is to say there might be ethical limits to what engines should be allowed to infer. Theres the old case of a system knowing a young woman is pregnant and sending pregnancy sales pitches to her home before she had told her parents, but there are far worse situations. Consider societies that are intolerant of sexual orientation, but that can be inferred from other data. A government could use that to persecute people. Theres a wide spectrum in between those examples, and the chapter does a nice job of getting people to think about the issue.
The next chapter covers autonomy and makes some very good points. One is that humans have always challenged each others autonomy, but that AI and lack of laws and regulations make it far easier for governments and a few companies to remove our autonomy in much more opaque ways than have previously been available.
Algorithms in government and employment are given a good introduction in the next chapters, but with a lot of the same information seen elsewhere. The most interesting part of the back portion of the book comes in chapter ten, about oversight and regulation. There is a suggestion that, given the complexity of AI, there is logic to creating a new oversight agency for the national government. As they point out, an FDA for AI. Think of it in business terms, its a center of excellence in AI, able to formulate national policy for business and citizens, while also serving to help other agencies adapt the general policies to their specific oversight areas. That makes excellent sense.
No book is perfect, but Im partially surprised that a book with so many authors attached flows as well. Then I remember they all are academics, used to research papers with multiple authors. Of course, with that many academics, the risk is always that a book will sound like a research paper. Fortunately, they seem to have escaped that problem. A Citizens Guide is a good read to help people understand key issues in having AI make the major impact on society that it will. More people need to realize that quickly and get governments to focus on protecting people.
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A Citizens Guide To Artificial Intelligence: A Nice Focus On The Societal Impact Of AI - Forbes
Artificial Intelligence used to Automate Assessment of Mesothelioma – The FINANCIAL
The FINANCIAL -- Patients receiving treatment for the asbestos cancer, Mesothelioma, are being assessed with Artificial Intelligence (AI) as part of a prototype imaging system which could revolutionise the way people with the disease are cared for. Scotland currently has the highest incidence of Mesothelioma in the world, a reflection of the historical use of asbestos in many UK industries, including shipbuilding and construction.
Canon Medical Research Europe, a Scottish firm specialising in next generation medical imaging software, and the University of Glasgow are set to publish clinical findings from a study evaluating a new, world-leading AI-driven cancer assessment tool, developed as part of the Cancer Innovation Challenge.
The study team, which comprises AI and data scientists at Canon Medical and University of Glasgow clinical researchers based at the Queen Elizabeth University Hospital, and NHS Greater Glasgow and Clyde Research and Innovation staff, created a prototype AI system able to automatically find and measure Mesothelioma on CT scans, which are used to assess patients response to drug treatments like chemotherapy. The AI was trained by showing it over 100 CT scans, on which an expert clinician had drawn around all areas of the tumour showing the AI what to look for. The trained AI was then shown a new set of scans and was able to find and measure the tumour extremely accurately, without any human input,University of Glasgow notes.
The tool, which could revolutionise the fight against cancer, intentionally focused on Mesothelioma given its prominence in Scotland and because it is one of the most difficult-to-measure cancers on CT scans. This is because it grows like a rind around the surface of the lung, forming a complex shape - rather than a round ball like most tumours. The successful results of the project will provide a strong foundation for similar tools to be developed in the assessment of other cancers.
At present, treatment options for Mesothelioma are limited and clinical trials are critical for discovery of new, more effective treatments. The AI tool streamlines tumour measurements, potentially making clinical trials of new drugs less expensive, less time-consuming and more accurate. After further validation work, which is now ongoing as part of an international accelerator network funded by Cancer Research UK, the AI tool may soon be available to help doctors measure Mesothelioma on scans during treatment with greater precision and at a reduced cost.
Keith Goatman, Principal Scientist at Canon Medical, said: The speed and accuracy of the AI algorithm could have a wide-reaching impact on Mesothelioma treatment. Accurate tumour volume measurements are much too time-consuming to perform by hand. Automating these measurements will open the way for clinical trials of new treatments, by detecting even small changes in the tumour size. Ultimately, it could be used routinely in hospitals to decide the best treatment for each individual.
The funding and support from the Cancer Innovation Challenge has been vital in bringing this idea to life, and we are looking forward to continuing our work with the excellent team at the University of Glasgow in the years to come. This work is a strong first step towards real change in the treatment of all cancers not just Mesothelioma.
Professor Kevin Blyth, Professor of Respiratory Medicine in the University of Glasgow, and Honorary Consultant Respiratory Physician at Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, said: To our knowledge, this study is world-leading in its successful use of AI to assess treatment response in Mesothelioma. Using external data sets to validate our results, we have shown that tumours can be accurately measured by AI, giving us a new tool that will help us make better decisions for patients on treatment and reducing barriers to development of new treatments in clinical trials,University of Glasgow notes.
The results, which are testament to the expertise of Canon Medical and made possible by the Cancer Innovation Challenge funding, have acted as a springboard towards our next project, the PREDICT-Meso Accelerator, which is now allowing us to further develop the AI so that it can start benefiting patients soon.
Launched in 2017, the Cancer Innovation Challenge is a 1 million project funded by the Scottish Government through the Scottish Funding Council to encourage collaboration between innovation centres, medical professionals and cutting-edge healthcare businesses to help Scotland become a world-leader in cancer care.
The project brings together three Innovation Centres, led by The Data Lab in collaboration with Digital Health and Care Institute (DHI) and Precision Medicine Scotland.
Steph Wright, Director of Health & Wellbeing Engagement at The Data Lab, added: The work to develop this world-leading tool from Canon Medical and the University of Glasgow, represents an incredibly exciting healthcare innovation. Not only does it have the potential to revolutionise Mesothelioma cancer care through more targeted treatment, but it may also be able to be applied to a number of other cancer types in the future.
Its been a privilege to play a part in helping to deliver the Scottish Funding Councils Cancer Innovation Challenge initiative, supporting and spotlighting the companies carrying out valuable work that can help make Scotland a leader in data-driven cancer support. Through projects like this, we really can show that data saves lives.
Following publication of the initial study results, the team will continue to work together, supported by part of a 5million funding award made by CRUK, for the PREDICT-Meso Accelerator led by Prof Blyth. In addition to AI optimisation, this project aims to understand how asbestos-driven inflammation develops into Mesothelioma and develop new treatments for the disease. Canon Medical is a key collaborator on this project,University of Glasgow notes.
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Artificial Intelligence used to Automate Assessment of Mesothelioma - The FINANCIAL
Proteins, artificial intelligence, and future of pandemic responses – Dailyuw
The Institute for Protein Design (IPD) at the UW announced March 31 a $5 million grant from Microsoft to collaborate on applying artificial intelligence to protein design.
Microsofts chief scientific officer Eric Horvitz and the IPDs director David Baker, in an article with GeekWire, said they believe that this collaboration will lead to major strides in medicine and technology, and accelerate the scientific response to future pandemics.
The IPD designs proteins molecules that carry out a wide range of functions from defending against pathogens to harnessing energy during photosynthesis from scratch, with the goal of making a whole new world of synthetic proteins to address modern challenges, according to the institutes website.
Researchers at the IPD have developed promising anti-viral and ultra-potent vaccine candidates against SARS-CoV-2, the virus that causes COVID-19, that are currently in human clinical trials.
And in protein design, form follows function.
We use 3D protein structures on the computer to design the protein sequences, Brian Coventry, a research scientist in the Baker Lab at the IPD, said. When we order the protein sequence, its function in real life should exactly mirror that on the computer.
But that does not always happen.
The problem with this method, which is based on the first principles of both physics and chemistry, is that it produces an abundance of possible proteins which must be tested, the majority of which do not have the exact desired form, Coventry said.
Coventry recently worked on a team that developed a SARS-CoV-2 antiviral medication candidate, and he stressed that for antivirals, it is important that the designed protein be precisely atomically correct.
In the context of a pandemic, the fast development of highly accurate therapeutic synthetic proteins is desirable. This is where deep learning, a subset of artificial intelligence modeled after the brains neural networks, comes into play.
There is a lot of room for improvement, Minkyung Baek, a postdoctoral scholar in the Baker Lab at the IPD, said about the first principles-based method of protein design. Baek believes that deep learning methods can be used to quickly discriminate between possible proteins and optimize design to produce proteins that are more stable and bind more tightly to targets.
Deep learning models are given a training data set, in this case experimental results of the structures of designed proteins, and then can learn based on real-world data. They use that information to predict and design protein structures, Baek said.
Microsoft has given the IPD access to their cloud computing service Azure, which will enable them to train and test deep learning models about 10 times faster, according to Baek.
Baek hopes that this will speed up the development of effective deep learning models, which will be helpful not only for designing proteins that match existing biological proteins, but also for discovering the structure of naturally occurring proteins.
There are many real-world situations where the structure of the target is not precisely known. In these situations, researchers must predict the shape of the metaphorical lock and design the key simultaneously.
Being able to better predict the structure of a protein when given its genetic code is important, with Baek using the variants of the COVID-19 virus as an example.
Using our deep learning base, we can predict the protein structure of the variant, and starting from there we may get some clue [about] why that variant may have been more severe or easy to spread, Baek said.
But these deep learning models have some limitations. They are limited by the available training data set, are not always generalizable to multiple situations, and do not explain the reasoning behind their decisions, Coventry said.
Despite these factors, Coventry and Baek are both optimistic about the potential for deep learning to improve the protein design process.
At the end of the day, Id like to see a 100% success rate, you know, Coventry said. Someday Im sure its possible.
Reach reporter Nuria Alina Chandra at news@dailyuw.com. Twitter: @AlinaChandra
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Proteins, artificial intelligence, and future of pandemic responses - Dailyuw