Category Archives: Machine Learning

Twitter has appointed Stanford professor and former Google vice president Fei-Fei Li to its board as an independent director.

The social media platform said that Li's expertise in artificial intelligence (AI) will bring relevant perspectives to the board. Li's appointment may also help Twitter to attract top AI talent from other companies in Silicon Valley.

Li left her role as chief scientist of AI/ML (artificial intelligence/machine learning) at Google Cloud in October 2018 after being criticized for comments she made in relation to the controversial Project Maven initiative with the Pentagon, which saw Google AI used to identify drone targets from blurry drone video footage.

When details of the project emerged, Google employees objected, saying that they didn't want their AI technology used in military drones. Some quit in protest and around 4,000 staff signed a petition that called for "a clear policy stating that neither Google nor its contractors will ever build warfare technology."

While Li wasn't directly involved in the project, a leaked email suggested she was more concerned about what the public would make of Google's involvement in the project as opposed to the ethics of the project itself.

"This is red meat to the media to find all ways to damage Google," she wrote, according to a copy of the emailobtained by the Intercept. "You probably heardElon Muskand his comment about AI causing WW3."

"I don't know what would happen if the media starts picking up a theme that Google is secretly building AI weapons or AI technologies to enable weapons for the Defense industry. Google Cloud has been building our theme on Democratizing AI in 2017, and Diane (Greene, head of Google Cloud) and I have been talking about Humanistic AI for enterprise. I'd be super careful to protect these very positive images."

Up until that point, Li was seen very much as a rising star at Google. In the one year and 10 months she was there, she oversaw basic science AI research, all of Google Cloud's AI/ML products and engineering efforts, and a newGoogle AI lab in China.

While at Google she maintained strong links to Stanford and in March 2019 she launched the Stanford University Human-Centered AI Institute (HAI), which aims to advance AI research, education, policy and practice to benefit humanity.

"With unparalleled expertise in engineering, computer science and AI, Fei-Fei brings relevant perspectives to the board as Twitter continues to utilize technology to improve our service and achieve our long-term objectives," said Omid Kordestani, executive chairman of Twitter.

Twitter has been relatively slow off the mark in the AI race. Itacquired British start-up Magic Pony Technologies in 2016 for up to $150 million as part of an effort to beef up its AI credentials, but its AI efforts remain fairly small compared to other firms. It doesn't have the same reputation as companies like Google and Facebook when it comes to AI and machine-learning breakthroughs.

Today the company uses an AI technique called deep learning to recommend tweets to its users and it also uses AI to identify racist content and hate speech, or content from extremist groups.

Competition for AI talent is fierce in Silicon Valley and Twitter will no doubt be hoping that Li can bring in some big names in the AI world given she is one of the most respected AI leaders in the industry.

"Twitter is an incredible example of how technology can connect the world in powerful ways and I am honored to join the board at such an important time in the company's history," said Li.

"AI and machine learning can have an enormous impact on technology and the people who use it. I look forward to leveraging my experience for Twitter as it harnesses this technology to benefit everyone who uses the service."

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Twitter adds former Google VP and A.I. guru Fei-Fei Li to board as it seeks to play catch up with Google and Facebook - CNBC

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TORONTO (PRWEB) May 11, 2020

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Genomics and Machine Learning for In Vitro Sensitization Testing of Challenging Chemicals, Upcoming Webinar Hosted by Xtalks - PR Web

Matt Newton, Senior Portfolio Marketing Manager at AVEVA, on how to overcome adoption barriers for AI and machine learning in the manufacturing industry

There has been a considerable amount of hype around Artificial Intelligence (AI) and Machine Learning (ML) technologies in the last five or so years.

So much so that AI has become somewhat of a buzzword full of ideas and promise, but something that is quite tricky to execute in practice.

At present, this means that the challenge we run into with AI and ML is a healthy dose of scepticism.

For example, weve seen several large companies adopt these capabilities, often announcing they intend to revolutionize operations and output with such technologies but then failing to deliver.

In turn, the ongoing evolution and adoption of these technologies is consequently knocked back. With so many potential applications for AI and ML it can be daunting to identify opportunities for technology adoption that can demonstrate real and quantifiable return on investment.

Many industries have effectively reached a sticking point in their adoption of AI and ML technologies.

Typically, this has been driven by unproven start-up companies delivering some type of open source technology and placing a flashy exterior around it, and then relying on a customer to act as a development partner for it.

However, this is the primary problem customers are not looking for prototype and unproven software to run their industrial operations.

Instead of offering a revolutionary digital experience, many companies are continuing to fuel their initial scepticism of AI and ML by providing poorly planned pilot projects that often land the company in a stalled position of pilot purgatory, continuous feature creep and a regular rollout of new beta versions of software.

This practice of the never ending pilot project is driving a reluctance for customers to then engage further with innovative companies who are truly driving digital transformation in their sector with proven AI and ML technology.

A way to overcome these challenges is to demonstrate proof points to the customer. This means showing how AI and ML technologies are real and are exactly like wed imagine them to be.

Naturally, some companies have better adopted AI and ML than others, but since much of this technology is so new, many are still struggling to identify when and where to apply it.

For example, many are keen to use AI to track customer interests and needs.

In fact, even greater value can be discovered when applying AI in the form of predictive asset analytics on pieces of industrial process control and manufacturing equipment.

AI and ML can provide detailed, real-time insights on machinery operations, exposing new insights that humans cannot necessarily spot. Insights that can drive huge impact on businesses bottom line.

AI and ML is becoming incredibly popular in manufacturing industries, with advanced operations analysis often being driven by AI. Many are taking these technologies and applying it to their operating experiences to see where economic savings can be made.

All organisations want to save money where they can and with AI making this possible.

These same organisations are usually keen to invest in further digital technologies. Successfully implementing an AI or ML technology can significantly reduce OPEX and further fuel the digital transformation of an overall enterprise.

Understandably, we are seeing the value of AI and ML best demonstrated in the manufacturing sector in both process and batch automation.

For example, using AI to figure out how to optimize the process to achieve higher production yields and improve production quality. In the food and beverage sectors, AI is being used to monitor production line oven temperatures, flagging anomalies - including moisture, stack height and color - in a continually optimised process to reach the coveted golden batch.

The other side of this is to use predictive maintenance to monitor the behaviour of equipment and improve operational safety and asset reliability.

A combination of both AI and ML is fused together to create predictive and prescriptive maintenance. Where AI is used to spot anomalies in the behavior of assets and recommended solution is prescribed to remediate potential equipment failure.

Predictive and Prescriptive maintenance assist with reducing pressure on O&M costs, improving safety, and reducing unplanned shutdowns.

Both AI, machine learning and predictive maintenance technologies are enabling new connections to be made within the production line, offering new insights and suggestions for future operations.

Now is the time for organisations to realise that this adoption and innovation is offering new clarity on the relationship between different elements of the production cycle - paving the way for new methods to create better products at both faster speeds and lower costs.

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How to overcome AI and machine learning adoption barriers - Gigabit Magazine - Technology News, Magazine and Website

Image and object recognition

Of the seven patterns of AI that represent the ways in which AI is being implemented, one of the most common is the recognition pattern. The main idea of the recognition pattern of AI is that were using machine learning and cognitive technology to help identify and categorize unstructured data into specific classifications. This unstructured data could be images, video, text, or even quantitative data. The power of this pattern is that were enabling machines to do the thing that our brains seem to do so easily: identify what were perceiving in the real world around us.

The recognition pattern is notable in that it was primarily the attempts to solve image recognition challenges that brought about heightened interest in deep learning approaches to AI, and helped to kick off this latest wave of AI investment and interest. The recognition pattern however is broader than just image recognition In fact, we can use machine learning to recognize and understand images, sound, handwriting, items, face, and gestures. The objective of this pattern is to have machines recognize and understand unstructured data. This pattern of AI is such a huge component of AI solutions because of its wide variety of applications.

The difference between structured and unstructured data is that structured data is already labelled and easy to interpret. However unstructured data is where most entities struggle. Up to 90% of an organization's data is unstructured data. It becomes necessary for businesses to be able to understand and interpret this data and that's where AI steps in. Whereas we can use existing query technology and informatics systems to gather analytic value from structured data, it is almost impossible to use those approaches with unstructured data. This is what makes machine learning such a potent tool when applied to these classes of problems.

Machine learning has a potent ability to recognize or match patterns that are seen in data. Specifically, we use supervised machine learning approaches for this pattern. With supervised learning, we use clean well-labeled training data to teach a computer to categorize inputs into a set number of identified classes. The algorithm is shown many data points, and uses that labeled data to train a neural network to classify data into those categories. The system is making neural connections between these images and it is repeatedly shown images and the goal is to eventually get the computer to recognize what is in the image based on training. Of course, these recognition systems are highly dependent on having good quality, well-labeled data that is representative of the sort of data that the resultant model will be exposed to in the real world. Garbage in is garbage out with these sorts of systems.

The many applications of the recognition pattern

The recognition pattern allows a machine learning system to be able to essentially look at unstructured data, categorize it, classify it, and make sense of what otherwise would just be a blob of untapped value. Applications of this pattern can be seen across a broad array of applications from medical imaging to autonomous vehicles, handwriting recognition to facial recognition, voice and speech recognition, or identifying even the most detailed things in videos and data of all types. Machine-learning enabled recognition has added significant power to security and surveillance systems, with the power to observe multiple simultaneous video streams in real time and recognize things such as delivery trucks or even people who are in a place they ought not be at a certain time of day.

The business applications of the recognition pattern are also plentiful. For example, in online retail and ecommerce industries, there is a need to identify and tag pictures for products that will be sold online. Previously humans would have to laboriously catalog each individual image according to all its attributes, tags, and categories. Nowadays, machine learning-based recognition systems are able to quickly identify products that are not already in the catalog and apply the full range of data and metadata necessary to sell those products online without any human interaction. This is a great place for AI to step in and be able to do the task much faster and much more efficiently than a human worker who is going to get tired out or bored. Not to mention these systems can avoid human error and allow for workers to be doing things of more value.

Not only is this recognition pattern being used with images, it's also used to identify sound in speech. There are lots of apps that exist that can tell you what song is playing or even recognize the voice of somebody speaking. Another application of this recognition pattern is recognizing animal sounds. The use of automatic sound recognition is proving to be valuable in the world of conservation and wildlife study. Using machines that can recognize different animal sounds and calls can be a great way to track populations and habits and get a better all-around understanding of different species. There could even be the potential to use this in areas such as vehicle repair where the machine can listen to different sounds being made by an engine and tell the operator of the vehicle what is wrong and what needs to be fixed and how soon.

One of the most widely adopted applications of the recognition pattern of artificial intelligence is the recognition of handwriting and text. While weve had optical character recognition (OCR) technology that can map printed characters to text for decades, traditional OCR has been limited in its ability to handle arbitrary fonts and handwriting. Machine learning-enabled handwriting and text recognition is significantly better at this job, in which it can not only recognize text in a wide range of printed or handwritten mode, but it can also recognize the type of data that is being recorded. For example, if there is text formatted into columns or a tabular format, the system can identify the columns or tables and appropriately translate to the right data format for machine consumption. Likewise, the systems can identify patterns of the data, such as Social Security numbers or credit card numbers. One of the applications of this type of technology are automatic check deposits at ATMs. Customers insert their hand written checks into the machine and it can then be used to create a deposit without having to go to a real person to deposit your checks.

The recognition pattern of AI is also applied to human gestures. This is something already heavily in use by the video game industry. Players can make certain gestures or moves that then become in-game commands to move characters or perform a task. Another major application is allowing customers to virtually try on various articles of clothing and accessories. It's even being applied in the medical field by surgeons to help them perform tasks and even to train people on how to perform certain tasks before they have to perform them on a real person. Through the use of the recognition pattern, machines can even understand sign language and translate and interpret gestures as needed without human intervention.

In the medical industry, AI is being used to recognize patterns in various radiology imaging. For example, these systems are being used to recognize fractures, blockages, aneurysms, potentially cancerous formations, and even being used to help diagnose potential cases of tuberculosis or coronavirus infections. Analyst firm Cognilytica is predicting that within just a few years, machines will perform the first analysis of most radiology images with instant identification of anomalies or patterns before they go to a human radiologist for further evaluation.

The recognition pattern is also being applied to identify counterfeit products. Machine-learning based recognition systems are looking at everything from counterfeit products such as purses or sunglasses to counterfeit drugs.

The use of this pattern of AI is impacting every industry from using images to get insurance quotes to analyzing satellite images after natural disasters to assess damage.Given the strength of machine learning in identifying patterns and applying that to recognition, it should come as little surprise that this pattern of AI will continue to see widespread adoption. In fact, in just a few years we might come to take the recognition pattern of AI for granted and not even consider it to be AI. That just goes to the potency of this pattern of AI. .

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Understanding The Recognition Pattern Of AI - Forbes

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The market is segregated into different segments to provide a granular analysis of the Machine Learning as a Service market. The market is segmented on the basis of application, end-user, region, and more.

The market share, size, and forecasted CAGR growth of each Machine Learning as a Service market segment and sub-segment are included in the report.

competition landscape which include competition matrix, market share analysis of major players in the global machine learning as a service market based on their 2016 revenues and profiles of major players. Competition matrix benchmarks leading players on the basis of their capabilities and potential to grow. Factors including market position, offerings and R&D focus are attributed to companys capabilities. Factors including top line growth, market share, segment growth, infrastructure facilities and future outlook are attributed to companys potential to grow. This section also identifies and includes various recent developments carried out by the leading players.

Company profiling includes company overview, major business strategies adopted, SWOT analysis and market revenues for year 2014 to 2016. The key players profiled in the global machine learning as a service market include IBM Corporation, Google Inc., Amazon Web Services, Microsoft Corporation, BigMl Inc., FICO, Yottamine Analytics, Ersatz Labs Inc, Predictron Labs Ltd and H2O.ai. Other players include ForecastThis Inc., Hewlett Packard Enterprise, Datoin, Fuzzy.ai, and Sift Science Inc. among others.

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Major Companies in Machine Learning as a Service Market Struggle to Fulfil the Extraordinary Demand Intensified by COVID-81 - Jewish Life News

Four promising research projects received funding from the University of Alabama CyberSeed program, part of the UA Office for Research and Economic Development.

The pilot seed-funding program promotes research across disciplines on campus while ensuring a stimulating and well-managed environment for high-quality research.

The funded projects come from four major thrusts of the UA Cyber Initiative that include cybersecurity, critical infrastructure protection, applied machine learning and artificial intelligence, and cyberinfrastructure.

These projects are innovative in their approach to using cutting-edge solutions to tackle critical challenges, said Dr. Jeffrey Carver, professor of computer science and chair of the UA Cyber Initiative.

One project will study cybersecurity of drones and develop strategies to mitigate potential attacks. Led by Dr. Mithat Kisacikoglu, assistant professor of electrical and computer engineering, and Dr. Travis Atkison, assistant professor of computer science, the research will produce a plan for the secure design of the power electronics in drones with potential for other applications.

Another project will use machine learning to probe the nature of dark matter using existing data from NASA. The work should position the research team, led by Dr. Sergei Gleyzer, assistant professor of physics and astronomy, and Dr. Brendan Ames, assistant professor of mathematics, to analyze images expected later this year from the Vera Rubin Observatory, the worlds largest digital camera.

The CyberSeed program is also funding work planning to use machine learning to accelerate discovery of candidates within a new class of alloys that can be used in real-world experiments. These new alloys, called high-entropy alloys or multi-principal component alloys, are thought to enhance mechanical performance. This project involves Drs. Lin Li and Feng Yan, assistant professors of metallurgical and materials engineering, and Dr. Jiaqi Gong, who begins as associate professor of computer science this month.

A team of researchers is involved in a project to use state-of-the-art cyberinfrastructure technology and hardware to collect, visualize, analyze and disseminate hydrological information. The research aims to produce a proof-of-concept system. The team includes Dr. Sagy Cohen, associate professor of geography; Dr. Brad Peter, a postdoctoral researcher of geography; Dr. Hamid Moradkhani, director of the UA Center for Complex Hydrosystems; Dr. Zhe Jiang, assistant professor of computer science; Dr. D. Jay Cervino, executive director of the UA Office of Information Technology; and Dr. Andrew Molthan with NASA.

The CyberSeed program came from a process that began in April 2019 with the first internal UA cybersummit to meet and define future opportunities. In July, ORED led an internal search for the chair of the Cyber Initiative,announcing Carver in August. In October, Carver led the second internal cybersummit, at which it was agreed the Cyber Initiative would define major thrusts and develop the CyberSeed program.

While concentrating in these areas specifically, the Cyber Initiative will continue to interact with other researchers across campus to identify other promising cyber-related research areas to grow the portfolio, Carver said.

This story originally appeared on the University of Alabamas website.

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Four projects receive funding from University of Alabama CyberSeed program - Alabama NewsCenter