Innovative technologies appear every day. Today the center of development is machine learning based on artificial intelligence. ML applications and programs will become an integral part of the optimization and success of companies. Already, these tools are helping to proactively detect equipment malfunctions, create personalized recommendations for customers, and find rational approaches to problem solving. Such programs cope with some tasks perfectly, while others still require the attention of people.

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We do not know exactly what is going on inside the "brain" of artificial intelligence (AI), and therefore we are not able to accurately predict its actions. We can run tests and experiments, but we cannot always predict and understand why AI does what it does. Just like humans the development of artificial intelligence is based on experiences (in the form of data when it comes to AI). That is why the way artificial intelligence acts sometimes catch us by surprise, and there are countless examples of artificial intelligence behaving sexist, racist, or just inappropriate. "Just because we can develop an algorithm that lets artificial intelligence find patterns in data to best solve a task, it does not mean that we understand what patterns it finds. So even though we have created it, it does not mean that we know it, "says Professor Søren Hauberg, DTU Compute.

I plan to discuss interesting upcoming features primarily from TorchVision and secondary from the PyTorch ecosystem. My target is to highlight new and in-development features and provide clarity of what's happening in between the releases. Though the format is likely to change over time, I initially plan to keep it bite-sized and offer references for those who want to dig deeper. Finally, instead of publishing articles on fixed intervals, I'll be posting when I have enough interesting topics to cover. Disclaimer: The features covered will be biased towards topics I'm personally interested.

Greece's decision to deploy machine learning in pandemic surveillance will be much-studied around the world.Credit: Konstantinos Tsakalidis/Bloomberg/Getty A few months into the COVID-19 pandemic, operations researcher Kimon Drakopoulos e-mailed both the Greek prime minister and the head of the country's COVID-19 scientific task force to ask if they needed any extra advice. Drakopoulos works in data science at the University of Southern California in Los Angeles, and is originally from Greece. To his surprise, he received a reply from Prime Minister Kyriakos Mitsotakis within hours. The European Union was asking member states, many of which had implemented widespread lockdowns in March, to allow non-essential travel to recommence from July 2020, and the Greek government needed help in deciding when and how to reopen borders. Greece, like many other countries, lacked the capacity to test all travellers, particularly those not displaying symptoms.

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Qualcomm emphasized its top efficiency score on select AI tasks in the MLPerf benchmark test. One of the greatest challenges facing artificial intelligence is the vast amount of energy consumed by the computers that perform AI. Scholars of the discipline have for some time now sounded the alarm about the rising cost of energy given the ever-increasing size of AI programs, especially those of the deep learning variety, and the spiraling compute resources they consume. As pointed out in a new five-year study, the AI100 report, published last week by Stanford University, "many within the field are becoming aware of the carbon footprint of building such large models," referring to deep learning programs. "There are significant environmental costs," the study asserts.

Tesla is leading the electric vehicle race by relying on big data, artificial intelligence and the internet of things to outsmart its competitors. It also manufactures and sells advanced battery, software and solar panel technology. The company is also investing big to become the number one self driving company in the world. However it's important to state that a fully autonomous vehicle won't happen anytime soon. Self-driving cars rely on advanced artificial intelligence, machine learning and deep learning technology.

As a leading mind in the field of computational biology and a pioneer of CMU's program on the topic, Murphy himself has played a strong role in this. In 2011, he penned a commentary noting that machine learning would play a role of growing importance in the drug discovery process. But his argument went a step further, advocating for the use of active machine learning, or a subset of ML in which the user offers the machine feedback on desired outcomes, improving its efficiency and accuracy over time. In the drug discovery process, the number of experiments required to screen a specific compound on a specific target while monitoring impact on other targets can quickly become unwieldy. Active ML offers researchers the opportunity to direct the experiment, supervising the computer as it iteratively chooses experiments that are most likely to improve the model.

Intuit, the business and financial tools company best known for its TurboTax software, made news last week for its acquisition of Mailchimp, and is now announcing a corporate venture capital arm, Intuit Ventures, to identify growth opportunities and trends beneficial for its key customers -- small businesses and consumers. The company is the latest to get into corporate venture, joining a group that includes WorkDay, Salesforce and Zoom. CEO Sasan Goodarzi spoke exclusively with TechCrunch about the new venture, which will focus its initial investments in the areas of fintech, e-commerce infrastructure, platforms and enablement, virtual experts/digital advice and AI/ML. The initial idea for the venture arm came about a year ago, when Goodarzi and Intuit's chief corporate strategy and development officer Anton Hanebrink were discussing the acceleration of internal and external company pipelines and how to align those with the company's mission and identify big opportunities. The VC arm is one of the ways they would do this, which would enable the company to accelerate innovation while also learning from companies, Goodarzi said.