AI & Machine Learning News. 14, December 2020
The Artificial Intelligence and Machine Learning News clippings for Quants are provided algorithmically with CloudQuant’s NLP engine which seeks out articles relevant to our community and ranks them by our proprietary interest score. After all, shouldn’t you expect to see the news generated using AI?
Hyundai Buys Boston Dynamics In $1.1B Deal
The South Korean automaker Hyundai Motors has announced that it’s reached a deal to buy an 80% stake in the robotics company Boston Dynamics from its owner SoftBank. The acquisition is reported as a $1.1 billion deal, implying that Hyundai is paying in the ballpark of $880 million for its 80% stake, while the remaining 20% will be held by SoftBank and its affiliates.
With an 80% stake, Hyundai has control of Boston Dynamics, which has drawn fame for its autonomous legged robots; the two-legged humanoid-like Atlas and the four-legged dog-like Spot.
In a press statement, Hyundai notes that it’ll make use of Boston Dynamics to improve its in-house manufacturing capabilities while providing more funding for the company to grow and sell its products to other customers. Hyundai itself says it’s looking to expand into the humanoid robot market over time, an area where Boston Dynamics’ expertise proves very useful.
CloudQuant Thoughts : In 2013 Google bought Boston Dynamics, selling it to SoftBank three years ago. BD had always said they were not interested in military applications, and the Netflix Black Mirror MetalHead episode served to make most people grateful for that statement. However, defense department contractor Ghost Robotics offers a very similar model!
Sizing The AI Software Market: Not As Big As Investors Expect But Still $37 Billion By 2025
How big is the market for artificial intelligence software? In our new report, “The AI Software Market Will Grow To $37 Billion Globally By 2025,” Forrester forecasts that the size of the AI software market will approach $37 billion by 2025. That’s a large number but still smaller than many investors and other analysts have projected. We believe our projection is more realistic, however, for two reasons. First, most business applications are adding AI functions without monetizing them. Second, the custom-built AI applications that businesses create for their own use don’t generate market revenues. Investors, vendors, and buyers who wish to understand and/or invest in the AI software market must understand Forrester’s four segments: AI maker platforms, AI facilitator platforms, AI-centric applications, and AI-infused applications.
2020-12-10 14:49:43-05:00 Read the full story…
Weighted Interest Score: 7.3077, Raw Interest Score: 2.4268,
Positive Sentiment: 0.0000, Negative Sentiment 0.1776
CloudQuant Thoughts : The market for AI will be huge and it will take away a lot of jobs. For an example of its impact on an industry you may not have considered, watch this excellent Blender Conference presentation.
Survey Shows Increased Need for Data Skills in Finance
- 90% of respondents are going to increase their data consumption over the next twelve months
- Over half (52%) stated generating meaningful insights from data is a strategic priority for their firm, with an extra 33% stating their firm is seeking to enhance their data intelligence
- Nearly half of respondents (41%) are anticipating increased demand for data science skills from their business over the next 12 months
There is an industry-wide need for specialist data science skills to match the growing appetite for meaningful data insights and greater data consumption, according to a survey by SIX among 113 representatives from buy-side and sell-side firms, exchanges, regulatory bodies and other organisations. The survey was conducted from 28thSeptember to 02nd November 2020 to check the pulse of the industry with regards to their views on data consumption, management, and analytics.
2020-12-09 09:59:10-05:00 Read the full story…
Weighted Interest Score: 4.0329, Raw Interest Score: 1.8630,
Positive Sentiment: 0.1644, Negative Sentiment 0.0822
CloudQuant Thoughts : “Respondents ranked data management and data analytics as the first and second most important initiatives at their firm.”
Yet Another Library for Deep Learning You Should Know About – SciKit-Learn
Yet Another Library for Deep Learning You Should Know About
PyTorch and TensorFlow aren’t the only Deep Learning frameworks in Python. There’s another library similar to scikit-learn.
SciKit-Learn is my first choice when it comes to classic Machine Learning algorithms in Python. It has many algorithms, supports sparse datasets, is fast and has many utility functions, like cross-validation, grid search, etc.
2020-12-14 13:58:50.280000+00:00 Read the full story…
Weighted Interest Score: 3.2632, Raw Interest Score: 1.7962,
Positive Sentiment: 0.1239, Negative Sentiment 0.0619
CloudQuant Thoughts : A number of our researchers use Scikit-Learn.
Forbes Insights: Medical Imaging’s Next Frontier: AI And The Edge
Medical imaging is facing a problem:
“There’s a worldwide shortage of radiologists,” says Prashant Shah, global head of AI for Intel’s Health and Life Sciences group. “At the same time, the number of [radiology] studies is increasing at an unprecedented rate.”
To keep pace, the average radiologist interpreting computed tomography (CT) and magnetic resonance imaging (MRI) examinations would need to read an image every three-to-four seconds of an eight-hour workday, according to one study.
What’s more, says Shah, “if you burden the radiologist with more scans and ask them to read them faster, you can introduce life-critical errors.”
The solution? Many clinicians are finding it in artificial intelligence (AI) and edge computing. Thanks to these technologies, healthcare providers can process, store and analyze complex imaging data on-premises, speeding diagnosis, improving clinician workflows and saving time—and potentially lives.
2020-12-09 00:00:00 Read the full story…
Weighted Interest Score: 2.6073, Raw Interest Score: 1.1238,
Positive Sentiment: 0.2593, Negative Sentiment 0.2737
CloudQuant Thoughts : And there you go, worldwide shortage of Radiologists.. AI can help.. in steps SalesForce.
Salesforce claims its AI can spot signs of breast cancer with 92% accuracy
Salesforce today peeled back the curtains on ReceptorNet, a machine learning system researchers at the company developed in partnership with clinicians at the University of Southern California’s Lawrence J. Ellison Institute for Transformative Medicine of USC. The system, which can determine a critical biomarker for oncologists when deciding on the appropriate treatment for breast cancer patients, achieved 92% accuracy in a study published in the journal Nature Communications.
Breast cancer affects more than 2 million women each year, with around one in eight women in the U.S. developing the disease over the course of their lifetime. In 2018 in the U.S. alone, there were also 2,550 new cases of breast cancer in men. And rates of breast cancer are increasing in nearly every region around the world.
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.6111, Raw Interest Score: 1.1304,
Positive Sentiment: 0.1758, Negative Sentiment 0.1256
CloudQuant Thoughts : SalesForce/Breast Cancer Research, that is not a combination I thought I would see, but all power to them if they are helping to identify Breast Cancer.
EU AI Watchdog
EU watchdog warns of using AI in predictive policing, medical diagnoses, and targeted advertising
The European Union’s rights watchdog has warned of the risks of using artificial intelligence in predictive policing, medical diagnoses, and targeted advertising as the bloc mulls rules next year to address challenges posed by the technology. While AI is widely used by law enforcement agencies, rights groups say it is also abused by authoritarian regimes for mass and discriminatory surveillance. Critics also worry about the violation of people’s fundamental rights and data privacy rules.
In a report issued on Monday, the Vienna-based EU Agency for Fundamental Rights (FRA) urged policymakers to provide more guidance on how existing rules apply to AI and ensure that future AI laws protect fundamental rights. “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,” FRA director Michael O’Flaherty said in a statement.
2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 4.3327, Raw Interest Score: 1.4963,
Positive Sentiment: 0.0499, Negative Sentiment 0.3491
EU rights watchdog warns of pitfalls in use of AI
The European Union’s rights watchdog has warned of the risks of using artificial intelligence in predictive policing, medical diagnoses and targeted advertising as the bloc mulls rules next year to address the challenges posed by the technology.
While AI is widely used by law enforcement agencies, rights groups say it is also abused by authoritarian regimes for mass and discriminatory surveillance. Critics also worry about the violation of people’s fundamental rights and data privacy rules.
The Vienna-based EU Agency for Fundamental Rights (FRA) urged policymakers in a report issued on Monday to provide more guidance on how existing rules apply to AI and ensure that future AI laws protect fundamental rights.
2020-12-14 05:08:44+00:00 Read the full story…
Weighted Interest Score: 3.9430, Raw Interest Score: 1.3783,
Positive Sentiment: 0.0475, Negative Sentiment 0.3802
VMware exec: AI’s two Achilles’ heels keep me up at night
The sudden switch to remote working during the COVID-19 pandemic left a huge gap of visibility for cybersecurity attacks. In some cases, on-premise security tools couldn’t immediately extend to the cloud or into home-working environments. This meant between March and May, teams scrambled to render their technology into a risk-free format. Microsoft’s CEO Satya Nadella said his company has seen two years’ worth of digital transformation in just two months as a result of the pandemic.
AI – a force for good and evil : Against this backdrop, Tom Kellermann, cybersecurity strategy head at major US software firm VMware, points out the particular threat of artificial intelligence (AI). “AI has two Achilles heels,” he explains at a roundtable attended by FinTech Futures. One is that timestamps and data can be manipulated, he says. The other is that the technology can be “turned against its mission”.
2020-12-09 07:30:09+00:00 Read the full story…
Weighted Interest Score: 3.9175, Raw Interest Score: 1.3625,
Positive Sentiment: 0.2890, Negative Sentiment 0.5367
2021 Predictions: Focus on People; See Nuances with Emotion AI; Turn to RPA Bots
AI experts are predicting 2021 will bring: more focus on people, reinforcement of analytic core, turn to RPA bots, coping with ‘Zoom fatigue’
By AI Trends Staff
We have heard from a range of AI practitioners for their predictions on AI Trends in 2021. Here are predictions from a selection of those writing.
2020-12-10 22:28:57+00:00 Read the full story…
Weighted Interest Score: 3.5265, Raw Interest Score: 1.3664,
Positive Sentiment: 0.1314, Negative Sentiment 0.3022
Accern launches AI Marketplace with over 400 ready-made use cases
Accern, a no-code AI platform, has launched the Accern AI Marketplace, which dramatically increases the speed enterprises can deploy and begin reaping the benefits of AI across their businesses.
Accern’s AI Marketplace allows data scientists and business analysts to empower their business functions with over 400 ready-made AI use cases to automate manual workflows and enhance decisions. Accern says the result is a 24x gain in productivity for financial teams.
Use cases include but are not limited to insights on Credit Risk, ESG Behaviours, Covid-19, Anti-Money Laundering Analytics, Mergers and Acquisitions, and more. These ready-made use cases are backed by AI and adaptive NLP (natural language processing) to allow financial services teams to quickly research, summarise, and extract data, and gain investment insights and manage risk.
2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 6.5084, Raw Interest Score: 2.9401,
Positive Sentiment: 0.3769, Negative Sentiment 0.1131
TD Securities Makes Strategic Investment in Data Services and Analytics to Accelerate its Digital Transformation Journey
Bloomberg selected to help enable data strategy
High-quality, comprehensive and integrated data that is accessible, shareable and utilized effectively across our TD Securities organization is critical to supporting the evolving needs of our clients.
TD Securities today announced an investment in data services and analytics using Bloomberg Enterprise Data content and services. Bloomberg is a global leader in providing business and financial data, news and insights. Access to its extensive catalog of comprehensive, market-leading datasets and robust data management tools will help strengthen TD Securities’ advanced analytics, AI and machine learning platforms.
“The importance of data quality and its management is revolutionizing capital markets and has become increasingly more critical in all aspects of how we operate and serve our clients,” says Rajesh Tolani, Head of Business Innovation and Chief Data Office for TD Securities.
2020-12-10 08:41:30-05:00 Read the full story…
Weighted Interest Score: 6.2765, Raw Interest Score: 2.8196,
Positive Sentiment: 0.4240, Negative Sentiment 0.1272
Workshop: From Privacy to Fairness in AI – 9th January 2021
“Responsible AI and AI governance also become a priority for AI on an industrial scale” according to Gartner 2020 hypecycle.
Machine learning(ML) and Artificial intelligence(AI) solutions are getting deeper in our day to day life. Currently, AI is empowering our convenience at the cost of our privacy. In the last few years, we have heard the news about big techs and startups facing lawsuits because of not compiling with new data governance laws. AI implementation in business has been facing several issues and challenges to solve them.
The aim of this workshop to empower ML/AI researches and industry leaders in understanding and mitigating the risk of AI. The workshop also provides a platform to exchange ideas and discuss some of the open problems in privacy, ethics and fairness surrounding AI.
2020-12-11 09:59:15+00:00 Read the full story…
2020-12-11 12:30:00+00:00 Read the full story…
Weighted Interest Score: 6.2171, Raw Interest Score: 2.3231,
Positive Sentiment: 0.2112, Negative Sentiment 0.2112
20 Core Data Science Concepts for Beginners
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
2020-12-20 00:00:00 Read the full story…
Weighted Interest Score: 5.5455, Raw Interest Score: 2.3596,
Positive Sentiment: 0.1265, Negative Sentiment 0.1925
IBM Commercialises Its AI FactSheets. Could It Become An Industry Standard?
IBM has recently announced the commercialisation of its AI FactSheets, which were first introduced in 2018. In an official release, the company wrote that it plans to “commercialise key automated documentation capabilities from IBM Research’s AI FactSheets methodology into Watson Studio in Cloud Pak for Data throughout 2021”. This fact sheet will provide businesses with a framework to define how AI is to be used, to measure a model’s performance, and to generate reports on internal and external transparency. Can such an AI fact sheet from IBM become an industry standard?
What Is IBM FactSheet & Why Is It Required? Some of the most important factors to consider while achieving trust in AI are — fairness, safety, explainability, reliability, and accountability. Apart from these factors, it must be accompanied by having a parameter against which the models are measured. A lot of this could be attributed to the increasing usage of AI models and services, even in high-stakes situations such as financial risk assessments, medical diagnosis, talent acquisition, policing, and governance.
2020-12-14 12:30:00+00:00 Read the full story…
Weighted Interest Score: 4.9550, Raw Interest Score: 1.7350,
Positive Sentiment: 0.1803, Negative Sentiment 0.1577
DataRobot, Snowflake Expand AI Collaboration
DataRobot announced more late-round investments this week along with an expanded partnership with big data leader Snowflake Inc. that would extend the reach of its enterprise AI platform.
A Series F funding round announced last month and led by Altimeter Capital raised $270 million. Boston-based DataRobot said Salesforce Ventures and Hewlett Packard Enterprises (NYSE: HPE) have since added strategic investments, raising that total to $320 million.
The enterprise AI and MLOps specialist has so far raised about $750 million in venture funding, and claims a market valuation of $2.8 billion.
Along with the new funding, DataRobot said Wednesday (Dec. 9) it is expanding collaboration with Snowflake “through deep product integration” and product marketing to their joint customers. The collaboration includes integration of Snowflake’s cloud data warehouse and its emerging data marketplace with DataRobot’s AI platform.
2020-12-09 00:00:00 Read the full story…
Weighted Interest Score: 4.2495, Raw Interest Score: 1.7478,
Positive Sentiment: 0.2399, Negative Sentiment 0.1028
Deutsche Bank expands Google cloud partnership
Deutsche Bank and Google have revealed further enhancements to a cloud partnership signed in July this year.
The bank will utilise Google Cloud’s AI and data analytics to “deliver new capabilities quicker and cheaper”.
The lender also plans to “co-innovate” by making its products available on the Google Cloud for the first time.
Co-innovation use cases include new lending products, one retail bank interface and enhancements to the Autobahn pla…
2020-12-09 08:30:27+00:00 Read the full story…
Weighted Interest Score: 4.1294, Raw Interest Score: 2.2727,
Positive Sentiment: 0.5510, Negative Sentiment 0.2755
AWS Announces Amazon Redshift ML, A Cloud-based Service For Data Scientists To Use ML Technologies
Recently at the AWS re:Invent event, the e-commerce giant announced the launch of Amazon Redshift Machine Learning (Amazon Redshift ML). According to its developers, with Amazon Redshift ML data scientists can now create, train as well as deploy machine learning models in Amazon Redshift using SQL.
Amazon Redshift is one of the most widely used cloud data warehouses, where one can query and combine exabytes of structured and semi-structured data across a data warehouse, operational database, and data lake us…
2020-12-14 06:30:00+00:00 Read the full story…
Weighted Interest Score: 4.0794, Raw Interest Score: 2.3022,
Positive Sentiment: 0.1788, Negative Sentiment 0.0447
AI for Credit Risk Management: Banking and Finance
A strong credit risk management system in combination with AI and ML technologies can not only mitigate financial risks but also level up the effectiveness of decision-making processes, increasing a company’s profit.
According to Statista, the number of only FDIC-insured commercial banks in the U.S. over the last 20 years reduced by half. It suggests that there is strong competition in the market. Banks and lending organizations need to check ev…
2020-12-14 12:41:52 Read the full story…
Weighted Interest Score: 4.0668, Raw Interest Score: 2.2567,
Positive Sentiment: 0.3056, Negative Sentiment 0.5407
C3.ai Soars 138% From IPO Price to Start Trading at $100
The IPO for C3.ai, the artificial-intelligence-software provider, was priced at $42 a share.
C3.ai (AI) – Get Report, an artificial intelligence software provider, began trading Wednesday on the New York Stock Exchange at $100 a share, up 138% from its initial public offering price of $42.
At last check, the stock was at $96.48, up 129%.
With the company issuing 15.5 million Class A shares, C3.ai raised $651 million. The $42 share price put the company’s value at $4.05 billion.
2020-12-09 13:48:20+00:00 Read the full story…
Weighted Interest Score: 3.9054, Raw Interest Score: 1.9412,
Positive Sentiment: 0.1109, Negative Sentiment 0.2219
How digital labour helps financial firms cope with market volatility
intelligence (AI), helps financial firms effectively manage increases in operational workload due to market volatility. It covers trends in digital labour, as well as governance and the need to put appropriate solutions in place to manage exceptions and risk, while considering the interplay between human and digital labour.
Recent market volatility caused by the COVID-19 outbreak resulted in an unprecedented surge in trading v…
2020-12-14 00:01:32+00:00 Read the full story…
Weighted Interest Score: 3.7593, Raw Interest Score: 1.7550,
Positive Sentiment: 0.1856, Negative Sentiment 0.3037
How artificial intelligence can improve software development process?
How Artificial Intelligence can improve Software Development Process?
Today, Artificial intelligence dominates technology trends. It has impacted retail, finance, healthcare, and many industries around the world. In fact, by 2025, the global AI market is expected to reach an impressive $60 million.
2020-12-08 14:55:07+00:00 Read the full story…
Weighted Interest Score: 3.6506, Raw Interest Score: 2.0975,
Positive Sentiment: 0.3056, Negative Sentiment 0.1945
Microsoft initiative will use AI to sniff out bribes, theft and other government corruption
Microsoft unveiled an initiative to use artificial intelligence to detect government corruption, calling it “an urgent global issue that can and must be solved.”
The new Microsoft Anti-Corruption Technology and Solutions initiative “will leverage the company’s investments in cloud computing, data visualization, AI, machine learning, and other emerging technologies to enhance transparency and to detect and deter corruption” over the next decade, said Dev Stahlkopf, Microsoft general counsel, in a post announcing the plan.
Microsoft made the announcement in conjunction with the United Nations’ International Anti-Corruption Day. Stahlkopf described the initiative as increasingly important given the events of the past year.
2020-12-09 18:44:00+00:00 Read the full story…
Weighted Interest Score: 3.6039, Raw Interest Score: 1.4634,
Positive Sentiment: 0.0976, Negative Sentiment 0.7805
New venture capital firm looks to invest at intersection of 5G, edge networks and AI
A new venture capital firm is taking shape in Seattle with a name suitable for this rain-soaked city.
Cloud City Venture Capital was formed earlier this year by tech veterans Jim Brisimitzis and Kevin Ober, and is in the process of raising a new fund, GeekWire has learned.
Ober is well known in venture capital circles. He co-founded Seattle venture capital firm Divergent Ventures, and before that spent seven years with Vulcan Ventures, the vent…
2020-12-11 18:09:00+00:00 Read the full story…
Weighted Interest Score: 3.5891, Raw Interest Score: 1.6134,
Positive Sentiment: 0.2305, Negative Sentiment 0.0659
5 Major Benefits of Big Data in Financial Trading Industry
Big data is making a significant impact on the financial world. The market for big data in the banking industry alone is projected to reach over $14.8 million by 2023.
The impact it’s making is much more of a grandiose splash rather than a few ripples. This is primarily due to the fact the technology in the space is scaling to unprecedented levels at such a fast rate. The exponentially increasing complexity and generation of data are dynamically…
2020-12-12 15:06:36+00:00 Read the full story…
Weighted Interest Score: 3.5888, Raw Interest Score: 1.8178,
Positive Sentiment: 0.4016, Negative Sentiment 0.1057
2/10 Recruiters are prepared for the deployment of AI in hiring
Artificial intelligence (AI) has numerous applications in today’s world – from website chatbots to healthcare and supply chain management, it is revolutionising the way that we work in numerous industries. But with AI being used increasingly in HR and recruitment, what do the professionals think about the technology?
RS Components has surveyed recruiters and HR professionals from the UK, to get an industry perspective on how AI could impact the way we hire and are hired ourselves, for jobs in the future. You can see the full piece here.
2020-12-08 16:06:59+00:00 Read the full story…
Weighted Interest Score: 3.5256, Raw Interest Score: 1.3743,
Positive Sentiment: 0.0916, Negative Sentiment 0.0000
Designing systems for real-time risk management
Financial organisations rely on risk management systems to assess strategic, compliance and operational risks. However, according to a pre-COVID-19 survey of more than 800 audit committee and board members conducted by KPMG, the top challenge for companies is maintaining a highly effective risk management program, due to fast changing regulations and volatility in the business environment. Almost half of the survey respondents reported that their…
2020-12-08 01:01:23+00:00 Read the full story…
Weighted Interest Score: 3.5145, Raw Interest Score: 2.1433,
Positive Sentiment: 0.1715, Negative Sentiment 0.3772
Top 10 AI Collaborations Between Indian Govt. & Tech Giants In 2020
The pandemic has forced organisations, businesses and academia around the globe to make a technological shift. Joining the efforts, the Government of India has been doing a lot of advancement in the case of emerging technologies. As a matter of fact, the increasing cases of COVID pandemic leading to a massive economic downfall has pushed the government of the country to take more such AI initiatives in order to grow the economy and businesses.
Below here, we have curated a list of top 10 AI collaborations, in no particular order, between the Government of India and tech giants in 2020.
2020-12-09 Read the Full Story…
AI Weekly: NeurIPS 2020 and the hope for change
Following a keynote address Wednesday, Microsoft Research Lab director Chris Bishop was asked if Big Tech companies’ monopoly on infrastructure and machine learning talent is stifling innovation. He responded by arguing that cloud computing allows developers to rent compute resources instead of undertaking the more expensive task of buying the hardware that powers machine learning.
The tension between corporate interests, human rights, ethics, and power could be seen at workshops throughout the week. At the Muslim in AI workshop on Tuesday, participants exp…
2020-12-11 00:00:00 Read the full story…
Weighted Interest Score: 3.3810, Raw Interest Score: 1.4538,
Positive Sentiment: 0.1869, Negative Sentiment 0.2492
An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku
Predicting sport scores, from data wrangling to model deployment
At the time of the Rugby World Cup in 2019 I did a small data science project to try and predict rugby match results, which I wrote about here. I’ve expanded this into an example end-to-end machine learning project to demonstrate how to deploy a machine learning model as an interactive web app.
Goal : To provide a high-level overview of the key steps needed in going from raw data to a live deployed machine learning app.
Once you’ve gone through this — pick a topic that you’re interested in, find some data, get your hands dirty and aim to build your own machine learning app, from data preparation to deployment.
2020-12-09 13:01:07.764000+00:00 Read the full story…
Weighted Interest Score: 3.3411, Raw Interest Score: 2.5098,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000
FPGA chips are coming on fast in the race to accelerate AI
AI is hungry, hyperscale AI ravenous. Both can devour processing, electricity, algorithms, and programming schedules. As AI models rapidly get larger and more complex (an estimated 10x a year), a recent MIT study warns that computational challenges, especially in deep learning, will continue to grow.
But there’s more. Service providers, large enterprises and others also face unrelenting pressures to speed up innovation, performance, and rollouts of neural networks and other low-latency, data-intensive applications, often involving exascale cloud and High-Performance Computing (HPC). These dueling demands are driving technology advances and adoption of a growing universe of Field Programmable Gate Arrays (FPGAs).
Early leader gains a new edge : In the early days of exascale computing and AI, these customer-configurable integrated circuits played a key role. Organizations could program and reprogram FPGAs onsite to handle a range of changing demands. As time went on, however, their performance and market growth got outpaced by faster GPUs and specialized ASICs.
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 3.2571, Raw Interest Score: 1.4770,
Positive Sentiment: 0.3296, Negative Sentiment 0.1099
How Quantum Computing Works at Goldman Sachs and JPMorgan
As we’ve reported before, banks have been busy building teams of quantum computing researchers. The heads of those teams at Goldman Sachs and JPMorgan recently presented their work at the Q2B Practical Quantum Computing conference.
Goldman Sachs has assembled a “full team dedicated to quantum computing,” William Zeng, head of quantum research at Goldman Sachs, told the virtual audience. In a subsequent session, Marco Pistoia, managing director and head of the FLARE (Future Lab for Applied Research and Engineering) at JPMorgan, said quantum computing is what FLARE is “particularly interested in.”
Zheng said Goldman sees three broad use cases for quantum computing in finance: simulation (e.g., for the statistical simulations of stochastic processes used in derivative pricing); optimization (e.g., portfolio optimization in the context of regulatory and tax constraints); and machine learning (which remains a “nascent” field in finance).
2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 3.2407, Raw Interest Score: 1.3607,
Positive Sentiment: 0.2027, Negative Sentiment 0.2316
A New Trend Of Training GANs With Less Data: NVIDIA Joins The Gang
Following MIT, researchers at NVIDIA have recently developed a new augmented method for training Generative Adversarial Networks (GANs) with a limited amount of data. The approach is an adaptive discriminator augmentation mechanism that significantly stabilised training in limited data regimes.
Machine learning models are data-hungry. As a matter of fact, in the past few years, we have seen that models that are fed with silos of data produce outstanding predictive outcomes.
Alongside, with significant growth, Generative Adversarial Networks have been successfully used for various applications including high-fidelity natural image synthesis, data augmentation tasks, improving image compressions, etc. From emoting realistic expressions to traversing the deep space, and from bridging the gap between humans and machines to introduce new and unique art forms, GANs have it all covered.
2020-12-13 05:30:00+00:00 Read the full story…
Weighted Interest Score: 3.2104, Raw Interest Score: 1.3238,
Positive Sentiment: 0.1953, Negative Sentiment 0.2170
Teachable AI will help Alexa users set up preferences
Alexa called Teachable AI that will enable the assistant to ask questions to fill gaps in its understanding. First announced during the company’s September virtual press event, Teachable AI leverages machine learning to determine whether a request can be a trigger for a teachable moment. If Alexa makes this determination, it will ask a customer for information to help it learn.
Amazon says Teachable AI will become available in the next few months for smart home devices before expanding to other areas.
Scientists at Amazon’s Alexa AI research division have long pursued semi-supervised and unsupervised learning techniques, in which AI systems learn to make predictions without ingesting gobs of annotated data. Semi-supervised and unsupervised learning have their limitations, but both promise to supercharge Alexa and other voice assistants’ capabilities by imbuing them with a humanlike capacity for inference.
2020-12-11 00:00:00 Read the full story…
Weighted Interest Score: 3.1390, Raw Interest Score: 1.1967,
Positive Sentiment: 0.2244, Negative Sentiment 0.1122
Google Wades Into Controversy with Dismissal of AI Ethicist Timnit Gebru
By John P. Desmond, Editor, AI Trends
Google ignited a firestorm around its ethics program last week when it let go a prominent AI ethicist, Timnit Gebru, apparently over contents of an email where she expressed her feelings, following a request by Google that a paper on large language models she had submitted to an industry conference be withdrawn.
Gebru had sent an email saying she felt “constantly dehumanized” at the company, according to an account in The Washington Post. She had been the co-leader of Google’s Ethical AI Team, where she was researching the fairness and risks associated with Google’s technology.
Of Ethiopian descent, Gebru was a rarity in the Silicon Valley culture known for its racial homogeneity. She became known in a senior role at Google for critically examining bias in the technology and its repercussions. She co-founded the Blacks in AI advocacy group that has pushed for more Black roles in AI development and research.
2020-12-10 23:07:24+00:00 Read the full story…
Weighted Interest Score: 3.0487, Raw Interest Score: 1.7498,
Positive Sentiment: 0.0866, Negative Sentiment 0.3292
Data Sourcebook 2020 – Downloadable PDF
Now, more than ever, the ability to pivot and adapt is a key characteristic of modern companies striving to position themselves strongly for the future. Download this year’s Data Sourcebook to dive into the key issues impact enterprise data management today and gain insights from leaders in cloud, data architecture, machine learning, data science and analytics.
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 3.0137, Raw Interest Score: 2.2039,
Positive Sentiment: 0.2755, Negative Sentiment 0.0000
Einblick Introduces Visual Data Computing Platform, Announces $6 Million in Seed Funding
Einblick, a visual data computing platform provider, based on years of research at MIT and Brown University, announced it has secured $6 million in Seed funding along with launching its visual data computing platform. The funding round was lead investor Amplify Partners with participation from top-tier investors Flybridge and Samsung Next.
Additionally, Sunil Dhaliwal, general partner at Amplify Partners, joins Einblick’s board of directors.
The funding will be used to continue investing in building a world-class engineering team, as well as expanding its sales and marketing capacity.
“Organizations are challenged by making data-driven decisions to achieve the best business outcome,” said Tim Kraska, founder and CEO at Einblick. “With Einblick’s visual data computing platform, business analysts and data scientists in organizations of all sizes can analyze past data, build predictive models, and simulate scenarios all in the same platform to quickly make data-driven decisions.”
2020-12-09 00:00:00 Read the full story…
Weighted Interest Score: 2.9345, Raw Interest Score: 1.6554,
Positive Sentiment: 0.3386, Negative Sentiment 0.0376
ML Deployment Woes Persist
Despite greater spending on staffing and use cases, investors in machine learning have so far reaped few returns as they struggle with life cycle issues related to data governance, security and auditing requirements.
An annual assessment released this week by Algorithmia on enterprise trends in machine learning found that machine learning investments are up, but adopters continue to struggle to reach production due to regulatory requirements. The survey released on Thursday (Dec. 10) found that 67 percent of the more than 400 executives polled said they must comply with multiple regulations covering data used in machine learning models.
Still, 83 percent of organizations said they are pressing on with ML development, increasing budget and hiring more data scientists. Staffing increased 76 percent over the previous year, Seattle-based Algorithmia reported.
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.8799, Raw Interest Score: 2.2185,
Positive Sentiment: 0.1664, Negative Sentiment 0.2219
Study: Expert Network Industry Tops $1.5 Billion Despite Muted Growth During Pandemic • Integrity Research
Expert networks were not immune from the effects of Covid-19 yet continued to grow double-digits in 2020, bringing the total industry size over $1.5 billion, according to analysis released by Integrity Research and Inex One, an expert network marketplace. The study, which includes detailed revenue estimates for the top 40 expert networks over the last five years, provides an in-depth perspective on the expert network industry, which continues to have opportunities for future growth.
Based on firm-level bottom-up analysis, 2020 Expert Network Market Sizing estimates that aggregate revenues for the industry slowed to low double digits in 2020 as Covid-19 dampened client activity in several key customer segments. Nevertheless, industry revenues exceeded $1.5 billion. Expert networks have had a strong trajectory of revenue growth even as the number of providers has increased.
2020-12-07 05:15:00+00:00 Read the full story…
Weighted Interest Score: 2.7397, Raw Interest Score: 1.7078,
Positive Sentiment: 0.3000, Negative Sentiment 0.0923
Web Data Scraping with AI is More Important than Ever
The latest innovation in the proxy service market makes every data gathering operation quicker and easier than ever before. Since the market for big data is expected to reach $243 billion by 2027, savvy business owners will need to find ways to invest in big data. Artificial intelligence is rapidly changing the process for collecting big data, especially via online media.
The Growth of AI in Web Data Collection: An entire generation of software engineers, data scientists, and even technical executives working in web data reliant industries are familiar with the pains of web data gathering, also known as web scraping. In brief, ineffective information retrieval, collection of incomplete or low-quality data, and complex data treatment operations are causing the most difficulties.
In this climate, the latest innovation in the industry – Next-Gen residential proxies are quickly gaining popularity among web-scraping professionals. The new web data gathering tool, powered by AI and machine learning (ML) algorithms, promises a staggering 100% success rate for scraping sessions, among many other advantages.
2020-12-07 18:49:00+00:00 Read the full story…
Weighted Interest Score: 2.7269, Raw Interest Score: 1.3983,
Positive Sentiment: 0.3122, Negative Sentiment 0.4073
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