Roundup As much of the Western world winds down for the Christmas period, here's a summary of this week's news from those machine-learning boffins who havent broken into the eggnog too early.

Finland, Finland, Finland: The Nordic country everyone thinks is part of Scandinavia but isnt has long punched above its weight on the technology front as the home of Nokia, the Linux kernel, and so on. Now the Suomi state is making a crash course in artificial intelligence free to all.

The Elements of AI series was originally meant to be just for Finns to get up to speed on the basics of AI theory and practice. Many Finns have already done so, but as a Christmas present, the Finnish government is now making it available for everyone to try.

The course takes about six weeks to complete, with six individual modules and is available in English, Swedish, Estonian, Finnish, and German. If you complete 90 per cent of the course and get 50 per cent of the answers right then the course managers will send you a nice certificate.

Meanwhile, don't forget there are many cool and useful free online courses on neural networks and the like, such as Fast.ai's excellent series and Stanford's top-tier lectures and notes.

Yep, AL still racist and sexist: A major study by the US National Institute of Standards and Technology, better known as NIST, has revealed major failings in today's facial-recognition systems.

The study examined 189 software algorithms from 99 developers, although interestingly Amazons Rekognition engine didnt take part, and the results arent pretty. When it came to recognizing Asian and African American faces, the algorithms were wildly inaccurate compared to matching Caucasian faces, especially with systems from US developers.

While it is usually incorrect to make statements across algorithms, we found empirical evidence for the existence of demographic differentials in the majority of the face recognition algorithms we studied, said Patrick Grother, a NIST computer scientist and the reports primary author.

While we do not explore what might cause these differentials, this data will be valuable to policymakers, developers and end users in thinking about the limitations and appropriate use of these algorithms.

For sale: baby shoes, never worn: As Hemmingway put it, the death of a child is one of the greatest tragedies that can occur, and Microsoft wants to do something about that using machine learning.

Redmond boffins worked with Tatiana Anderson and Jan-Marino Ramirez at Seattle Childrens Research Institute, in America, and Edwin Mitchell at the University of Auckland, New Zealand, to analyse Sudden Unexpected Infant Death (SUID) cases. Using a decades worth of data from the US Center for Disease Control (CDC), covering over 41 million births and 37,000 SUID deaths, the team sought to use specially prepared logistic-regression models to turn up some insights.

The results, published in the journal Pediatrics, were surprising: there was a clear difference between deaths that occurred in the first week after birth, dubbed SUEND, which stands for Sudden Unexpected Early Neonatal Death, and those that occurred between the first week and the end of a childs first year.

In the case of SUID, they found that rates were higher for unmarried, young mothers (between 15 and 24 years old), while this was not the case for SUEND cases. Instead, maternal smoking was highlighted as a major causative factor in SUEND situations, as were the length of pregnancy and birth weight.

The team are now using the model to look down other causative factors, be they genetic, environmental or something else. Hopefully such research will save many more lives in the future.

AI cracking calculus: Calculus, the bane of many schoolchildrens lives, appears to be right up AIs street.

A team of Facebook eggheads built a natural-language processing engine to understand and solve calculus problems, and compared the output with Wolfram Mathematica's output. The results were pretty stark: for basic equations, the AI solved them with 98 per cent accuracy, compared to 85 per cent for Mathematica.

With more complex calculations, however, the AIs accuracy drops off. It scored 81 per cent for a harder differential equation and just 40 per cent for more complex calculations.

These results are surprising given the difficulty of neural models to perform simpler tasks like integer addition or multiplication, the team said in a paper [PDF] on Arxiv. These results suggest that in the future, standard mathematical frameworks may benefit from integrating neural components in their solvers.

Deep-fake crackdown: Speaking of Facebook: today, the antisocial network put out an announcement that it had shut down two sets of fake accounts pushing propaganda. One campaign, originating in the country of Georgia, had 39 Facebook accounts, 344 Pages, 13 Groups, and 22 Instagram accounts, now all shut down. The network was linked to the nation's Panda advertising agency, and was pushing pro-Georgian-government material.

What's the AI angle? Here it is: the other campaign was based in Vietnam, and was devoted to influencing US voters using Western-looking avatars generated by deep-fake software a la thispersondoesnotexist.com.

Some 610 accounts, 89 Pages, 156 Groups and 72 Instagram accounts were shut down. The effort was traced to a group calling itself Beauty of Life (BL), which Facebook linked to the Epoch Media Group, a stateside biz that's very fond of President Trump and spent $9.5m in Facebook advertising to push its messages.

"The BL-focused network repeatedly violated a number of our policies, including our policies against coordinated inauthentic behavior, spam and misrepresentation, to name just a few," said Nathaniel Gleicher, Head of Security Policy at Facebook.

"The BL is now banned from Facebook. We are continuing to investigate all linked networks, and will take action as appropriate if we determine they are engaged in deceptive behavior."

Facebook acknowledged that it took the action as a result of its own investigation and "benefited from open source reporting." This almost certainly refers to bullshit-busting website Snopes, which uncovered the BL network last month.

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Another free web course to gain machine-learning skills (thanks, Finland), NIST probes 'racist' face-recog and more - The Register

One of Amazons most recent announcements was the release of their new tool called Amazon Rekognition Custom Labels. This advanced tool has the capability to improve machine learning on a whole new scale, allowing for better data analysis and object recognition.

Amazon Rekognition will help users train their machine learning models more easily and allow them to understand a set of objects out of limited data. In other words, this capability will make machines more intelligent and capable of recognizing items with far less data sets than ever before.

Employees stand near an The Amazon Inc. logo is displayed above the reception counter at the ... [+] company's campus in Hyderabad, India, on Friday, Sept. 6, 2019. Amazon's only company-owned campus outside the U.S. opened at the end of August on the other side of the globe, thousands of miles from their Seattle headquarters. The 15-storey building towers over the landscape in Hyderabad's technology and financial district, signaling the giant online retailer's ambitions to expand in one of the world's fastest-growing retail markets. Photographer: Dhiraj Singh/Bloomberg

The Benefits of Machine Learning with Amazon Rekognition

Machine learning includes a scientific study and adoption of algorithms that allow computers to learn new information and functionalities without needing direct instructions. In other words, machine learning can be understood as the capability of computers to learn on their own.

Thus far, machine learning models required large data sets in order to learn something new. For instance, if you wanted a device to recognize a chair as a chair, you would have to provide hundreds, if not thousands of pieces of visual evidence of what a chair looks like.

However, with Amazons new recognition tool, machine learning models will be able to work with very limited data sets and still effectively learn the difference between new objects and items.

Computers will now be able to recognize a group of object based on as little as ten images, which is a significant improvement compared to previous requirements. Amazon is slowly but surely stepping on a fresh and untrodden path of machine learning development.

Why Amazon Rekognition Matters

Having limited data to work with used to be a challenge in machine learning. Today, new models will be able to learn efficiently without large sets of data all thanks to Amazons recently announced tool.

Instead of having to train a model from scratch, which requires specialized machine learning expertise and millions of high-quality labeled images, customers can now use Amazon Rekognition Custom Labels to achieve state-of-the-art performance for their unique image analysis needs, announced Amazon in their blog post.

The new Amazon Rekognition featured on December 3rd and it is expected to bring significant changes to machine learning all throughout 2020. The release of the new tool also took place in the AWS re:Invent conference that was held in Las Vegas.

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Amazon Releases A New Tool To Improve Machine Learning Processes - Forbes

As we draw closer to 2020, Rick Rider, Senior Director, Product Management, Infor shares predictions on AI.

Moving to Intellectual Digital Assistants. To meet growing enterprise user expectations, AI Digital Assistants will evolve into Intellectual Digital Assistants. Users no longer are satisfied with just telling Digital Assistants what to do and having them automatically execute certain tasks or basic configurations. 2020 will be the year when these digital assistants, using AI and machine learning (ML), start to understand the context of what users are doing, recommend potential next steps (based on completed actions), identify mistakes and auto-correct inputs, and start to engage with users in dynamic, on-the-fly conversations.

AI helps define a new normal. In 2020, AI and machine learning platforms will start to challenge conventional thinking, when it comes to enterprise business processes and expected outcomes. In other words, these systems will re-define our default assumptions about what is normal. This will make business process re-engineering and resource training more efficient. When examining supply chain processes, for example, AI platforms have observed that default values related to expected delivery dates and payment dates typically are used only 4 percent of the time. Users almost always plug in their own values. Therefore, AI and machine learning systems will start enabling us to disregard default values, as we understand them today, and act more quickly through trust in our data. We no longer will be beholden to predefined rules, defaults, or assumptions.

Operationalizing AI. Industry-specific templates will make AI easier to use and deploy in 2020. In manufacturing, AI and machine learning systems, will take advantage of templated processes to help enterprises better manage their parts inventories, improve demand forecasting and supply chain efficiency, and improve quality control and time-to-delivery. In healthcare, organizations will leverage AI and machine learning to better integrate data thats segregated in application silos, exchange information with partners across the care continuum, and better use that data to respond to regulatory and compliance requirements. And, in retail, companies will use AI and ML to better predict demand patterns and shipment dates, based on defined rules, and improve their short- and long-term planning processes.

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AI and machine learning platforms will start to challenge conventional thinking - CRN.in

Jen-Hsun Huang, president and chief executive officer of Nvidia Corp., gestures as he speaks during ... [+] the company's event at the 2019 Consumer Electronics Show (CES) in Las Vegas, Nevada, U.S., on Sunday, Jan. 6, 2019. CES showcases more than 4,500 exhibiting companies, including manufacturers, developers and suppliers of consumer technology hardware, content, technology delivery systems and more. Photographer: David Paul Morris/Bloomberg

We found that if Nvidia Stock drops 10% or more in a week (5 trading days), there is a solid 36% chance itll recover 10% or more, over the next month (about 20 trading days)

Nvidia stock has seen significant volatility this year. While the company has been impacted by the broader correction in the semiconductor space and the trade war between the U.S. and China, the stock is being supported by a strong long-term outlook for GPU demand amid growing applications in Deep Learning and Artificial Intelligence.

Considering the recent price swings, we started with a simple question that investors could be asking about Nvidia stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if Nvidia stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 40%. Quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves. Given the recent volatility in the market, the mix of macroeconomic events (including the trade war with China and interest rate easing by the U.S. Fed), we think investors can prepare better.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in Nvidia stock become more likely after a drop?

Answer:

Not really.

Specifically, chances of a 5% rise in Nvidia stock over the next month:

= 40%% after Nvidia stock drops by 5% in a week.

versus,

= 44.5% after Nvidia stock rises by 5% in a week.

Question 2: What about the other way around, does a drop in Nvidia stock become more likely after a rise?

Answer:

No.

Specifically, chances of a 5% decline in Nvidia stock over the next month:

= 40% after NVIDIA stock drops by 5% in a week

versus,

= 27% after NVIDIA stock rises by 5% in a week

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, largely yes!

Given a drop of 5% in Nvidia stock over a week (5 trading days), while there is only about 28% chance the Nvidia stock will gain 5% over the subsequent week, there is more than 58% chance this will happen in 6 months.

The table below shows the trend:

Trefis

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in Nvidia stock are about 30% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 29% when the waiting period is a year (250 trading days).

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

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Machine Learning Answers: If Nvidia Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes

A small number of companies have begun developing new tools to measure employee engagement without requiring workers to fill out surveys or sit through focus groups. HR professionals and engagement experts are watching to see if these tools gain traction and lead to more effective cultural and retention strategies.

Two of these companiesNetherlands-based KeenCorp and San Franciscos Cultivateglean data from day-to-day internal communications. KeenCorp analyzes patterns in an organizations (anonymized) email traffic to gauge changes in the level of tension experienced by a team, department or entire organization. Meanwhile, Cultivate analyzes manager email (and other digital communications) to provide leadership coaching.

These companies are likely to pitch to a ready audience of employers, especially in the technology space. With IT unemployment hovering around 2 percent, corporate and HR leaders cant help but be nervous about hiring and retention. When competition for talent is fierce, companies are likely to add more and more sweeteners to each offer until they reel in the candidates they want. Then theres the matter of retaining those employees in the face of equally sweet counteroffers.

Thats why businesses utilize a lot of effort and money on keeping their workers engaged. Companies spend more than $720 million annually on engagement, according to the Harvard Business Review. Yet their efforts have managed to engage just 13 percent of the workforce.

Given the competitive advantage tech organizations enjoy when their teams are happy and productivenot to mention the money they save by keeping employees in placeengagement and retention are critical. But HR cant create and maintain an engagement strategy if it doesnt know the workforces mindset. So companies have to measure, and they measure primarily through surveys.

Today, many experts believe surveys dont provide the information employers need to understand their workforces attitudes. Traditional surveys have their place, they say, but more effective methods are needed. They see the answer, of course, in artificial intelligence (A.I.) and machine learning (ML).

One issue with surveys is they only capture a part of the information, and thats the part that the employee is willing to release, said KeenCorp co-founder Viktor Mirovic. When surveyed, respondents often hold back information, he explained, leaving unsaid data that has an effect similar to unheard data.

I could try to raise an issue that you may not be open to because you have a prejudice, Mirovic added. If tools dont account for whats left unsaid and unheard, he argued, they provide an incomplete picture.

As an analogy, Mirovic described studies of combat aircraft damaged in World War II. By identifying where the most harm occurred, designers thought they could build safer planes. However, the study relied on the wrong data, Mirovic said. Why? Because they only looked at the planes that came back. The aircraft that presumably suffered the most grievous damagethose that were shot downwerent included in the research.

None of this means traditional surveys surveys dont provide value. I think the traditional methods are still useful, said Alex Kracov, head of marketing for Lattice, a San Francisco-based workforce management platform that focuses on small and mid-market employers. Sometimes just the idea of starting to track engagement in the first place, just to get a baseline, is really useful and can be powerful.

For example, Lattice itself recently surveyed its 60 employees for the first time. It was really interesting to see all of the data available and how people were feeling about specific themes and questions, he said. Similarly, Kracov believes that newer methods such as pulse surveyswhich are brief studies conducted at regular intervalscan prove useful in monitoring employee satisfaction, productivity and overall attitude.

Whereas surveys require an employees active participation, the up-and-coming tools dont ask them to do anything more than their work. When KeenCorps technology analyzes a companys email traffic, its looking for changes in the patterns of word use and compositional style. Fluctuations in the products index signify changes in collective levels of tension. When a change is flagged, HR can investigate to determine why attitudes are in flux and then proceed accordingly, either solving a problem or learning a lesson.

When I ask you a question, you have to think about the answer, Mirovic said. Once you think about the answer, you start to include all kinds of other attributes. You know, youre my boss or youve just given me a raise or youre married to my sister. Those could all affect my response. What we try to do is go in as objectively as possible, without disturbing people as we observe them in their natural habitats.

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Measuring Employee Engagement with A.I. and Machine Learning - Dice Insights