Self-Organizing Maps (SOM), or Kohonen Networks ([1]), is an unsupervised learning method that can be applied to a wide range of problems such as: data visualization, dimensionality reduction or clustering. It was introduced in the 80' by computer scientist Teuvo Kohonen as a type of neural network ([Kohonen 82],[Kohonen 90]). In this post we are going to present the basics of the SOM model and build a minimal python implementation based on numpy. There is a huge litterature on SOMs (see [2]), theoretical and applied, this post only aims at having fun with this model over a tiny implementation. The approach is very much inspired by this post ([3]).

Today the team at Elementl is proud to announce an early release of Dagster, an open-source library for building systems like ETL processes and ML pipelines. We believe they are, in reality, a single class of software system. We call them data applications. Dagster is a library for building these data applications. We define a data application as a graph of functional computations that produce and consume data assets.

Significant advances are being made in artificial intelligence, but accessing and taking advantage of the machine learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn't regularly provided to retrain them. We envision a slightly different paradigm, one in which people will be able to easily and cost-effectively run machine learning models with technology they already have, such as browsers and apps on their phones and other devices. Through this new framework, participants can collaboratively and continually train and maintain models, as well as build datasets, on public blockchains, where models are generally free to use for evaluating predictions.

Kai-Fu Lee, a pioneer in artificial intelligence and venture capitalist based in China, tells "60 Minutes" it won't just be blue collar jobs that are displaced by AI. See the full report here: https://cbsn.ws/2FpBKEz Get more "60 Minutes" from "60 Minutes: Overtime" HERE: http://cbsn.ws/1KG3sdr Follow "60 Minutes" on Twitter HERE: http://bit.ly/1KxUsqX Follow "60 Minutes" on Google HERE: http://bit.ly/1KxUvmG

Artificial intelligence, or AI, is one of the most intriguing topics in software development today. It is also one of the most widely misunderstood.

Personalized Analytics is becoming essential in healthcare, stemming from the movement from fee-for-service to a value-based market. The need to preempt and prevent disease on a more personal level, rather than merely reacting to symptoms, has created a significant opportunity for machine learning-based applications. This "analytics of one" approach (using advanced mathematical models and artificial intelligence techniques) is already impacting several key areas: Prime examples include cardiac imaging analysis that aides physicians in assessing conditions, including heart attacks and coronary artery disease, and retinal image analysis to detect diabetic retinopathy. The anticipated goal for AI in healthcare is to enhance and expand the "four Ps" of care delivery – predictive, preventative, personalized and participatory. Predictive: Predictions have existed in healthcare for some decades now, as statistical models based on structured data sources.

So much for VW keeping a tight lid on the ID.3's finished design. Auto Conspiracy and others have obtained a promo video for the electric hatchback's interior, showing an appropriately very connected machine. The early look suggests the ID.3 will use a slightly more Teutonic variant of the cabin from Seat's el-Born concept, with an all-screen instrument cluster, a likely 10-inch center touchscreen and a twistable shifter placed on the dash. However, the biggest addition may be what you can't see: modern voice control. Much like Mercedes' newer cars, the ID.3 will apparently have natural-language voice commands to handle common tasks.

Writing requires a dash of uniquely human creativity. Artificial intelligence alone cannot do it for us, at least not very well. But AI can – and already is – helping us do things like make sure we spell words correctly and use correct grammar, through the myriad ways it is infused across the suite of Microsoft 365 products. Some of them were even used to craft this story. As the AI in these products is becoming more sophisticated, they are helping us do more than spot a misspelled word.

Technological development is only speeding up. Virtual reality and high-tech solutions are changing reality in its numerous aspects. Today, we have smart homes with internet-connected kettles and robotic vacuum cleaners, job interviews are conducted by AI, and countless gadgets are meant to make our lives more comfortable and cozy. Very soon, humans may even be able to communicate without words, converting thoughts into electric signals. And healthcare, of course, is not excluded from this rapid pace of advances, with psychiatric disorders now diagnosed by man-made machines.

Computer pioneer and codebreaker Alan Turing will feature on the new design of the Bank of England's £50 note. He is celebrated for his code-cracking work that proved vital to the Allies in World War Two. The £50 note will be the last of the Bank of England collection to switch from paper to polymer when it enters circulation by the end of 2021. The note was once described as the "currency of corrupt elites" and is the least used in daily transactions. However, there are still 344 million £50 notes in circulation, with a combined value of £17.2bn, according to the Bank of England's banknote circulation figures.