Artificial intelligence is transforming industries around the world and health care is no exception. A recent Mayo Clinic study found that AI-enhanced electrocardiograms (ECGs) have the potential to save lives by speeding diagnosis and treatment in patients with heart failure who are seen in the emergency room.

The lead author of the study is Demilade Demi Adedinsewo, a noninvasive cardiologist at the Mayo Clinic who is actively integrating the latest AI advancements into cardiac care and drawing largely on her learning experience with MIT Professional Education.

Identifying AI opportunities in health care

A dedicated practitioner, Adedinsewo is a Mayo Clinic Florida Women's Health Scholar and director of research for the Cardiovascular Disease Fellowship program. Her clinical research interests include cardiovascular disease prevention, women's heart health, cardiovascular health disparities, and the use of digital tools in cardiovascular disease management.

Adedinsewos interest in AI emerged toward the end of her cardiology fellowship, when she began learning about its potential to transform the field of health care. I started to wonder how we could leverage AI tools in my field to enhance health equity and alleviate cardiovascular care disparities, she says.

During her fellowship at the Mayo Clinic, Adedinsewo began looking at how AI could be used with ECGs to improve clinical care. To determine the effectiveness of the approach, the team retroactively used deep learning to analyze ECG results from patients with shortness of breath. They then compared the results with the current standard of care a blood test analysis to determine if the AI enhancement improved the diagnosis of cardiomyopathy, a condition where the heart is unable to adequately pump blood to the rest of the body. While she understood the clinical implications of the research, she found the AI components challenging.

Even though I have a medical degree and a masters degree in public health, those credentials arent really sufficient to work in this space, Adedinsewo says. I began looking for an opportunity to learn more about AI so that I could speak the language, bridge the gap, and bring those game-changing tools to my field.

Bridging the gap at MIT

Adedinsewos desire to bring together advanced data science and clinical care led her to MIT Professional Education, where she recently completed the Professional Certificate Program in Machine Learning & AI. To date, she has completed nine courses, including AI Strategies and Roadmap.

All of the courses were great, Adedinsewo says. I especially appreciated how the faculty, like professors Regina Barzilay, Tommi Jaakkola, and Stefanie Jegelka, provided practical examples from health care and nonhealth care fields to illustrate what we were learning.

Adedinsewos goals align closely with those of Barzilay, the AI lead for the MIT Jameel Clinic for Machine Learning in Health. There are so many areas of health care that canbenefit from AI, Barzilay says. Its exciting to see practitioners like Demijoin the conversation and help identify new ideas for high-impact AIsolutions.

Adedinsewo also valued the opportunity to work and learn within the greater MIT community alongside accomplished peers from around the world, explaining that she learned different things from each person. It was great to get different perspectives from course participants who deploy AI in other industries, she says.

Putting knowledge into action

Armed with her updated AI toolkit, Adedinsewo was able to make meaningful contributions to Mayo Clinics research. The team successfully completed and published their ECG project in August 2020, with promising results. In analyzing the ECGs of about 1,600 patients, the AI-enhanced method was both faster and more effective outperforming the standard blood tests with a performance measure (AUC) of 0.89 versus 0.80. This improvement could enhance health outcomes by improving diagnostic accuracy and increasing the speed with which patients receive appropriate care.

But the benefits of Adedinsewos MIT experience go beyond a single project. Adedinsewo says that the tools and strategies she acquired have helped her communicate the complexities of her work more effectively, extending its reach and impact. I feel more equipped to explain the research and AI strategies in general to my clinical colleagues. Now, people reach out to me to ask, I want to work on this project. Can I use AI to answer this question? she said.

Looking to the AI-powered future

Whats next for Adedinsewos research? Taking AI mainstream within the field of cardiology. While AI tools are not currently widely used in evaluating Mayo Clinic patients, she believes they hold the potential to have a significant positive impact on clinical care.

These tools are still in the research phase, Adedinsewo says. But Im hoping that within the next several months or years we can start to do more implementation research to see how well they improve care and outcomes for cardiac patients over time.

Bhaskar Pant, executive director of MIT Professional Education, says We at MIT Professional Education feel particularly gratified that we are able to provide practitioner-oriented insights and tools in machine learning and AI from expert MIT faculty to frontline health researchers such as Dr. Demi Adedinsewo, who are working on ways to enhance markedly clinical care and health outcomes in cardiac and other patient populations. This is also very much in keeping with MITs mission of 'working with others for the betterment of humankind!'

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Putting artificial intelligence at the heart of health care with help from MIT - MIT News

Teresa Carey: This is Scientific Americans 60-Second Science. I'm Teresa Carey.

Every morning at five oclock, composer Walter Werzowa would sit down at his computer to anticipate a particular daily e-mail. It came from six time zones away, where a team had been working all night (or day, rather) to draft Beethovens unfinished 10th Symphonyalmost two centuries after his death.

The e-mail contained hundreds of variations, and Werzowa listened to them all.

Werzowa: So by nine, 10 oclock in the morning, its likeIm already in heaven.

Carey: Werzowa was listening for the perfect tunea sound that was unmistakably Beethoven.

But the phrases he was listening to werent composed by Beethoven. They were created by artificial intelligencea computer simulation of Beethovens creative process.

Werzowa: There werehundreds of options, and some are better than others. But then there is that one which grabs you, and that was just a beautiful process.

Carey: Ludwig van Beethoven was one of the most renowned composers in Western music history. When he died in 1827, he left behind musical sketches and notes that hinted at a masterpiece. There was barely enough to make out a phrase, let alone a whole symphony. But that didnt stop people from trying.

In 1988 musicologist Barry Cooper attempted. But he didnt get beyond the first movement. Beethovens handwritten notes on the second and third movements are meagernot enough to compose a symphony.

Werzowa: A movement of a symphony can have up to 40,000 notes. And some of his themes were three bars, like 20 notes. Its very little information.

Carey: Werzowa and a group of music experts and computer scientists teamed up to use machine learning to create the symphony. AhmedElgammal, the director of the Art and Artificial Intelligence Laboratory at Rutgers University, led the AI side of the team.

Elgammal: When you listen to music read by AIto continue a theme of music, usually its a very short few seconds, and then they start diverging and becoming boring and not interesting. They cannot really take that and compose a full movement of a symphony.

Carey: The teams first task was to teach the AI to think like Beethoven. To do that, they gave it Beethovens complete works, his sketchesand notes. They taught it Beethoven's processlike how he went from those iconic four notes to his entire Fifth Symphony.

[CLIP: Notes from Symphony no. 5]

Carey: Then they taught it to harmonize with a melody, compose a bridge between two sectionsand assign instrumentation. With all that knowledge, the AI came as close to thinking like Beethoven as possible. But it still wasnt enough.

Elgammal: The way music generation using AI works is very similar to the way, when you write an e-mail, you find that the e-mail thread predicts whats the next word for you or what the rest of the sentence is for you.

Carey: Butlet the computer predict your words long enough, and eventually, the text will sound like gibberish.

Elgammal: It doesnt really generate something that can continue for a long time and be consistent. So that was the main challenge in dealing with this project: How can you take a motif or a short phrase of music that Beethoven wrote in his sketchand continue it into a segment of music?

Carey: Thats where Werzowas daily e-mails came in. On those early mornings, he was selecting what he thought was Beethovens best. And, piece by piece, the team built a symphony.

Matthew Guzdial researches creativity and machine learning at the University of Alberta. He didnt work on the Beethoven project, but he says that AI is overhyped.

Guzdial: Modern AI, modern machine learning, is all about just taking small local patterns and replicating them. And its up to a human to then take what the AI outputs and find the genius. The genius wasnt there. The genius wasnt in the AI. The genius was in the human who was doing the selection.

Carey: Elgammal wants to make the AI tool available to help other artists overcome writers block or boost their performance. But both Elgammal and Werzowa say that the AI shouldnt replace the role of an artist. Insteadit should enhance their work and process.

Werzowa: Like every tool, you can use a knife to kill somebody or to save somebodys life, like with a scalpel in a surgery. So it can go any way. If you look at the kids, like kids are born creative.Its like everything is about being creative, creative and having fun. And somehow were losing this. I think if we could sit back on a Saturday afternoon in our kitchen, and because maybe were a little bit scared to make mistakes, ask the AI to help us to write us a sonata, song or whateverin teamwork, life will be so much more beautiful.

Carey: The team released the 10th Symphony over the weekend. When asked who gets credit for writing it Beethoven, the AIor the team behind itWerzowa insists it is a collaborative effort. But, suspending disbelief for a moment, it isnt hard to imagine that were listening to Beethoven once again.

Werzowa: I dare to say that nobody knows Beethovenas well as the AI, didas well as the algorithm. I think music, when you hear it, when you feel it, when you close your eyes, it does something to your body. Close your eyes, sit back and be open for it, and I would love to hear what you felt after.

Carey: Thanks for listening. For Scientific Americans60-Second Science, Im Teresa Carey.

[The above text is a transcript of this podcast.]

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Beethoven's Unfinished 10th Symphony Brought to Life by Artificial Intelligence - Scientific American

The government is undertaking research to explore how AI R&D is successfully commercialised and brought to market.

The Department for Digital, Culture, Media and Sport (DCMS), along with the Office for Artificial Intelligence and Digital Standards and Internet Governance (DSIG), are leading the research project.

Research consultants Oxford Insights and Cambridge Econometrics have been commissioned with exploring the ways technology transfer happens for AI, and are seeking to conduct interviews with those with knowledge of the industry.

The research aims to increase understanding of the following topics:

Oxford Insights and Cambridge Econometrics would like to speak individuals with experience and knowledge of the AI development ecosystem, Innovate UK and other funding programmes, Standards Developing Organisations (SDOs), AI patents, AI R&D in the public and private sectors, AI funding and Venture Capital, and AI policy.

Our interviews will take approximately 45 mins -1 hour; however, we are happy to accommodate if time doesnt permit this length of interview. We may request your approval to follow up on specific points and themes identified across all our interactions.

Please get in touch with either aisha.naz@dcms.gov.uk or sam.hainsworth@dcms.gov.uk if you have any clarifications or questions. We look forward to working with you.

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Understanding the UK Artificial Intelligence commercialisation - GOV.UK

Software as a Service, or SaaS, is a concept that is familiar to many. Long-time Photoshop users will recall when Adobe stopped selling its product and instead shifted to a subscriber model. Netflix and Disney+ are essentially Movies as a Service, particularly at a time when ownership of physical media is losing ground to media streaming. Artificial Intelligence as a Service (AIaaS) has been growing in market adoption in recent years, but the uninitiated might be asking: what exactly is it?

In a nutshell, AIaaS is what happens when a company develops and licenses use of an AI to another company, most often to solve a very specific problem. For example, Bill owns a company that sells hotdogs through his e-commerce site. While Bill offers a free returns policy for dissatisfied customers, he lacks the time to provide decent customer support, and rarely replies to emails. Separately, a software developer has created a chatbot that can handle most customer inquiries using natural language processing, and often solve the issue or answer a question before human intervention is even required. For a monthly fee, the chatbot is licensed to the hotdog vendor, and implemented on his website. Now, the bot is solving 80% of customer issues, leaving Bill with the time to respond to the remaining 20%. But Bill is still too preoccupied making hotdogs, so he subscribes to a service like Flowrite, that uses AI to intelligently write his emails on the fly.

AI is also being put in service to analyze large sets of data and make predictions, streamline information storage, or even detect fraudulent activity. Amazons personal recommendation engine, an AI powered by machine learning, is now available as a licensed service to other retailers, video stream platforms, and even the finance industry. Googles suite of AI services range from natural language processing, handwriting recognition, to real-time captioning and translation. IBMs groundbreaking AI, Watson, is now being deployed to fight financial crimes, target advertisements based on real-time weather analysis, and analyze data to help hospitals make treatment judgements.

Also read: AI-Enabled Payments: A Q&A with Tradeshift

Also read: How Quantum Computing Will Transform AI

Machine learning AIs improve with time, usage, and development. Some, like YouTubes recommendation engine, have become so sophisticated that it sometimes feels like we have entire television stations tailored perfectly to our interests. Others, like language model AI GPT-3, produce entire volumes of text that are nearly indistinguishable from an authentic human source.

Microsoft has even put GPT-3 to use to translate conversational language into a working computer code, potentially opening up a new frontier in how software can be written in the future, and giving coding novices a fighting chance. Microsoft has also partnered with NVIDIA to create a new natural language generation model, three times as powerful as GPT-3. Improvements in language recognition and generation have obvious carryover benefits for the future development of chatbots, home assistants, and document generation as well.

Industrial giant Siemens has announced they are integrating Googles AIaaS solutions to streamline and analyze data, and predict, for instance, the rate of wear-and-tear of machinery on their factory floor. This could reduce maintenance costs, improve the scheduling of routine inspections, and prevent unexpected equipment failures.

AIaaS is a rapidly growing field, and there will be many more niches discovered that it can fill for years to come.

Read next: Top 5 Benefits of AI in Banking and Finance

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What is Artificial Intelligence as a Service (AIaaS)? | ITBE - IT Business Edge

Artificial Intelligence (AI) has become a major focus of, and the most valuable asset in, many technology transactions and the competition for top AI companies has never been hotter. According to CB Insights, there have been over 1,000 AI acquisitions since 2010. The COVID pandemic interrupted this trajectory, causing acquisitions to fall from 242 in 2019 to 159 in 2020. However, there are signs of a return, with over 90 acquisitions in the AI space as of June 2021 according to the latest CB Insights data. With tech giants helping drive the demand for AI, smaller AI startups are becoming increasingly attractive targets for acquisition.

AI companies have their own set of specialized risks that may not be addressed if buyers approach the transaction with their standard process. AIs reliance on data and the dynamic nature of its insights highlight the shortcomings of standard agreement language and the risks in not tailoring agreements to address AI specific issues. Sophisticated parties should consider crafting agreements specifically tailored to AI and its unique attributes and risks, which lend the parties a more accurate picture of an AI systems output and predictive capabilities, and can assist the parties in assessing and addressing the risks associated with the transaction. These risks include:

Freedom to use training data may be curtailed by contracts with third parties or other limitations regarding open source or scraped data.

Clarity around training data ownership can be complex and uncertain. Training data may be subject to ownership claims by third parties, be subject to third-party infringement claims, have been improperly obtained, or be subject to privacy issues.

To the extent that training data is subject to use limitations, a company may be restricted in a variety of ways including (i) how it commercializes and licenses the training data, (ii) the types of technology and algorithms it is permitted to develop with the training data and (iii) the purposes to which its technology and algorithms may be applied.

Standard representations on ownership of IP and IP improvements may be insufficient when applied to AI transactions. Output data generated by algorithms and the algorithms themselves trained from supplied training data may be vulnerable to ownership claims by data providers and vendors. Further, a third-party data provider may contract that, as between the parties, it owns IP improvements, resulting in companies struggling to distinguish ownership of their algorithms prior to using such third-party data from their improved algorithms after such use, as well as their ownership and ability to use model generated output data to continue to train and improve their algorithms.

Inadequate confidentiality or exclusivity provisions may leave an AI systems training data inputs and material technologies exposed to third parties, enabling competitors to use the same data and technologies to build similar or identical models. This is particularly the case when algorithms are developed using open sourced or publicly available machine learning processes.

Additional maintenance covenants may be warranted because an algorithms competitive value may atrophy if the algorithm is not designed to permit dynamic retraining, or the user of the algorithm fails to maintain and retrain the algorithm with updated data feeds.

In addition to the above, legislative protection in the AI space has yet to fully mature, and until such time, companies should protect their IP, data, algorithms, and models, by ensuring that their transactions and agreements are specifically designed to address the unique risks presented by the use and ownership of training data, AI-based technology and any output data generated by such technology.

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Transactions in the Age of Artificial Intelligence: Risks and Considerations - JD Supra

The devil went down to Silicon Valley; he was looking for a soul to steal. But he ended up taking a consulting gig with Palantir instead.

In the meantime, the algorithms in charge of punishing the wicked now. And these days the sign above hells gates reads Abandon Open Source, with an Amazon smile beneath the print.

Those condemned to an eternity of pain and suffering in the modern era are now forced to read the same five AI articles over and over.

Which kind of sounds like what its like to read tech news back here on Earth anyway. Dont believe me? Lets dive in.

No it wasnt. These articles usually involve a text generator such as OpenAIs GPT-3. The big idea is that the journalist will either pay for access or collaborate with OpenAI to get GPT-3 to generate text from various prompts.

The journalist will ask something silly like can AI ever truly think like a human? and then GPT-3 will use that prompt to generate a specific number of outputs.

Then, the journalists and editors go to work. Theyll pick the best responses, mix and match sentences that make the most sense, and then discard the rest.

This is the editorial equivalent of taking the collected works of Stephen King, copy/pasting a single sentence from each book into a word doc, and then claiming youve published an entirely new book from the master of horror.

In hell, you stand in a long line to read hyperbolic, made-up stories about AIs capabilities. And, as your ultimate punishment, you have to rewrite them for the next person in line.

I remember reading about an early funding round for an AI company called PredPol. It had raised several million dollars to develop an AI system capable of predicting crime before it happens.

Im sorry. Perhaps you didnt read that right. It says: predicting crime before it happens.

This is something thats impossible. And I dont mean technologically impossible, I mean not possible within the realms of classical or quantum physics.

You see crime isnt generated from hotspots like mobs spawning in an MMO every 5 minutes. A first year statistics or physics student understands that no amount of historical data can predict where new crimes will occur. Mostly because the past isnt literally prescient. But, also, its impossible to know how many crimes have actually been committed.Most crimes go unreported.

PredPol cant predict crime. It predicts arrests based on historical data. In other words: PredPol tells you where youve already arrested people and then says try there again. Simply put: it doesnt work because it cant work.

But it raised money and raised money until one day it grew into a full-grown company worth billions all for doing nothing.

In hell, you have to read funding stories about billion-dollar AI startups that dont actually do anything or solve any problems. And youre not allowed to skim.

Theres variations on this one Googles AI demonstrates a 72% reduction in racial bias, Amazons new algorithm is 87% better at spotting and removing Naziproducts from its store front and theyre all bunk.

Big techs favorite PR company is the mainstream media.

Facebook will, as a hypothetical example, say something like our new algorithms are 80% more efficient at finding and removing toxic content in real time, and thats when the telephone game starts.

Youll see half a dozen reputable news outlets printing headlines that basically say Facebooks new algorithms make it 80% less toxic. And thats simply not true.

If a chef were to tell you theyve adopted a new cooking technique that results in 80% less fecal matter being detected in the soup theyre about to serve, you probably wouldnt think that was a good thing.

Increasing the efficiency of an algorithm doesnt result in a unilateral increase in overall system efficiency. And, because statistical correlations are incredibly difficult to make when you dont have access to the actual data being discussed, the people writing up these stories are simply taking the big tech marketing teams word for it.

In hell, you have to read articles about big tech companies that only have quotes from people who work at those companies and statistics that cant possibly be verified.

Weve all read these stories. They cover the biggest issues in the world of AI as if theyre writing about the weather.

The story will be something like Clearview AI gets new government contracts, and the coverage will quote a politician, the CEO of Clearview, and someone representing law enforcement.

The gist of the piece will be Ethics aside, law enforcement agencies say these products are invaluable.

And then, way down towards the end of the article, youll see the obligatory studies have shown that facial recognition struggles to identify some faces. Experts warn against the use of such technologies until this bias can be solved.

In hell, every AI article you read starts with the sentence this doesnt work as well for Black people or women, but were just going to move past that like it isnt important.

My least favorite AI article is the ones that profess to tell me what non-experts think.

These are the articles with headlines like Study: 80% of people believe AI will be sentient within a decade and 75% of moms think Alexa is a danger to children.

These studies are typically conducted by consultancy companies that specialize in this sort of thing. And usually theyre not out conducting studies on the speculation that some journalist will find their workappealing. They get paid to do their research.

And by research, I mean: sourcing answers on Amazons Mechanical Turk or giving campus students a gift card to fill out a survey.

These studies are often bought and paid for ahead of time by an AI company as a marketing tool.

These pitches, in my inbox, usually look something like Hey Tristan, did you hear that 92% of CEOs dont know what Kubernetes is? Are you interested in this exclusive study and a conversation with Dr Knows Itall, founder of the Online School For Learning AI Good? They can speak to the challenges of hiring quality IT talent.

Can you spot the rubbish?

In hell, the algorithm tells you that you can read articles covering actual computer science research as soon as you finish reading all the vapid survey pieces on AI published in mainstream outlets.

But youre never done are you? Theres always another. What do soccer dads think about gendered voice assistants? What percentage of people think data is a character on Star Trek? Will driverless cars be a reality in 2022? Heres what Tesla owners think.

Yes, AI hell is a place filled with horrors beyond comprehension. And, just in case you havent figured it out yet, were already here. This article has been your orientation.

Now if youll just sign in to Google News, well get started (Apple News is currently not available in hell due to legal issues concerning the App Store).

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The 5 articles you read in AI hell - The Next Web

Since launching in July this year, the highly anticipated IAIF has attracted 665 project submissions from over 300 schools in 8 countries and regions, with 121 projects from 98 schools selected for the final online presentation and verbal Q&A.During the final competition presentations, the project submissions were reviewed meticulously by 45 professional judges from top-tier universities, enterprises and research institutions, including University of Science and Technology of China, Tsinghua University, Fudan University, Shanghai Jiao Tong University, Nanyang Technological University, Peking University, Chinese University of Hong Kong and Shanghai Technology Art Center. The teaching and evaluation system of martial arts based on body posture recognition and machine learning by Li Lufei from Shanghai Nanyang Model High School and Wu Keyu from the High School Affiliated to Fudan University as well as the drone powered by OpenCV for flood fight and rescue by Huang Pucheng, Wang Bingyang and Lin Yinhang from Zhejiang Wenling High School became the winners of the grand prize.

Besides, the research of rehabilitation assessment and training system powered by 3D hand posture verification by Zhang Yihong from Shanghai World Foreign Language Academy stood out from many excellent projects and won the first prize. Leveraging the 3D hand posture verification, this project aims to design a low-cost and easy-to-operate product for the patient with hand movement disorders, realizing 89.9% accurate in assessment of hand rehabilitation and training.

Lin Junqiu, Deputy Director of Science and Education Department from Shanghai Science, Art and Education Center, said, "Artificial intelligence is critical to our future. As we continue to advance technology development, we must cultivate a larger pool of AI talent with even higher levels of expertise and innovation capability. The huge opportunities brought by the AI era will facilitate transformative applications across industry verticals and scenarios but also formulate optimal collaboration between human being and artificial intelligence."

Lynn Dai, General Manager of SenseTime's Education Product, said at the final competition, "AI has become an important driving force for technological innovation, we believe the IAIF can provide an innovative platform for young people to develop their interest in AI. Meanwhile, SenseTime Education is dedicated to nurturing young talent and broadening their horizons with advanced insights from an industry perspective, as well as preparing them for the AI-empowered future."

IAIF is also providing comprehensive services for participants, from scientific innovation training to project incubation, helping them solve practical industrial problems. The IAIF organizing committee hosted a four-week AI training course for students before the final competition. The students from the most outstanding project teams will have the chance to participate in other national or international competitions. In addition, the students from the most outstanding IAIF projects will participate in a roadshow training workshop for startups as part of incubator programmes organized by SenseTime; the company will provide technology for high-potential projects.

"IAIF provided me with a unique opportunity to exchange ideas on this exciting AI topic with participants from different schools around the world," said Wu Keyu, the winner of the grand prize. "Through this competition, I have gained a better understanding of the powerful impact from AI and humans working together to build novel solutions that will create a better tomorrow for human society."

The success of the 3rd International Artificial Intelligence Fair not only marks the formation of the foundations for the AI education ecosystem developed by the Shanghai Xuhui Education Bureau and SenseTime, but also boosts the collaboration among governments, academia, enterprises and industries in AI technology innovation. In the future, SenseTime Education will continue to act as a focal point and a platform for cultivating future AI talents.

About SenseTime

SenseTime is a leading AI software company focused on creating a better AI-empowered future through innovation. Upholding a vision of advancing the interconnection of the physical and digital worlds with AI, driving sustainable productivity growth and seamless interactive experiences, SenseTime is committed to advancing the state of the art in AI research, developing scalable and affordable AI software platforms that benefit businesses, people and society, and attracting and nurturing top talents, shaping the future together.

With our roots in the academic world, we invest in our original and cutting-edge research that allows us to offer and continuously improve industry-leading, full-stack AI capabilities, covering key fields across perception intelligence, decision intelligence, AI-enabled content generation and AI-enabled content enhancement, as well as key capabilities in AI chips, sensors and computing infrastructure. Our proprietary AI infrastructure, SenseCore, allows us to develop powerful and efficient AI software platforms that are scalable and adaptable for a wide range of applications.

Today, our technologies are trusted by customers and partners in many industry verticals including Smart Business, Smart City, Smart Life and Smart Auto.

We have offices in markets including Hong Kong, Mainland China, Taiwan, Macau, Japan, Singapore, Saudi Arabia, the United Arab Emirates, Malaysia, and South Korea, etc., as well as presences in Thailand, Indonesia and the Philippines. For more information, please visit SenseTime's website as well as its LinkedIn, Twitter and Facebook pages.

SOURCE SenseTime

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SenseTime Co-hosts the 3rd International Artificial Intelligence Fair to Nurture AI Talent and Promote a Collaborative Education Ecosystem -...

REDWOOD CITY, Calif.--(BUSINESS WIRE)--Alation Inc., the leader in enterprise data intelligence solutions, today announced the acquisition of Lyngo Analytics, a Los Altos, Calif.-based data insights company. The acquisition will elevate the business user experience within the data catalog, scale data intelligence, and help organizations drive data culture. Lyngo Analytics CEO and co-founder Jennifer Wu and CTO and co-founder Joachim Rahmfeld will join the company.

Lyngo Analytics uses a natural language interface to empower users to discover data and insights by asking questions using simple, familiar business terms. Alation offers the most intelligent and user-friendly machine-learning data catalog on the market. And by integrating Lyngo Analytics artificial intelligence (AI) and machine-learning (ML) technology into its platform, Alation deepens its support for the non-technical user, converting natural language questions into SQL.

The integration lowers the barrier to entry for business users. Now, they can acquire and develop data-driven insights from across an enterprise's broad range of data sources. This means even data consumers without SQL expertise can ask questions in natural language and find data and insights without the support of data analysts. The acquisition will help organizations drive data culture by putting data and analytics into the hands of the masses.

Wu will join Alation as Senior Director of Product Management, where she will be responsible for product strategy and delivery for natural language data search, discovery, and exploration experiences. Rahmfeld, who is also a part-time, graduate-level deep learning and natural language processing lecturer at UC Berkeleys Master of Information and Data Science Program, will be Senior Director of AI/ML Research. He will be responsible for Alations AI and machine learning center of excellence, building both platform and application experiences that leverage AI and ML to enhance Alations value for business and technical users.

Alation created the first machine learning data catalog and were known for providing the most user-friendly interface on the market, said Raj Gossain, Chief Product Officer, Alation. With this acquisition, were building on the best. Were doubling down on key aspects of the platform that will help drive data culture and spur innovation and growth. Jennifer and Joachim developed a unique solution for a complex data and analytics issue, and Im excited to welcome them to the Alation team.

The acquisition is the latest milestone for Alation, which announced a $110 million Series D funding round and a $1.2 billion market valuation in June 2021. Alation is growing quickly, earning the trust of nearly 300 customers, including leading global brands such as Cisco, Exelon, GE Aviation, Munich Re, NASDAQ, and Pfizer. The company has more than 450 employees globally and is hiring. Recently, Alation was named a leader in The Forrester Wave: Data Governance Solutions, Q3 2021 report and Snowflakes Data Governance Partner of the Year.

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About Alation

Alation is the leader in enterprise data intelligence solutions including data search & discovery, data governance, data stewardship, analytics, and digital transformation. Alations initial offering dominates the data catalog market. Thanks to its powerful Behavioral Analysis Engine, inbuilt collaboration capabilities, and open interfaces, Alation combines machine learning with human insight to successfully tackle even the most demanding challenges in data and metadata management. Nearly 300 enterprises drive data culture, improve decision making, and realize business outcomes with Alation including AbbVie, American Family Insurance, Cisco, Exelon, Fifth Third Bank, Finnair, Munich Re, NASDAQ, New Balance, Parexel, Pfizer, US Foods and Vistaprint. Headquartered in Silicon Valley, Alation was named to Inc. Magazines Best Workplaces list and is backed by leading venture capitalists including Blackstone, Costanoa, Data Collective, Dell Technologies, Icon, ISAI Cap, Riverwood, Salesforce, Sanabil, Sapphire, and Snowflake Ventures. For more information, visit alation.com.

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Alation Acquires Artificial Intelligence Vendor Lyngo Analytics - Business Wire