After months of anticipation, Elon Musk finally took the wraps off Tesla's first AI humanoid robot, 'Optimus' last week. Optimus, which was first announced in August last year, received a frenzied reception at the firm's AI Day event in California on Friday. The bot was filmed emerging from behind a wall with two robotic hands in a heart shape, before taking a few tentative steps to wild applause. Musk said Tesla is planning to sell the bot for'probably less than $20,000' (£17,700) in three to five years' – meaning another long wait for Tesla fans to get their hands on the firm's most anticipated technology. Following its unveiling, MailOnline has taken a look at the Tesla products that have been announced but are still yet to be released - including Cybertruck, Robotaxi and the second-generation Roadster.

Summary: In this talk, Raj will take you on a time machine ride to explore the technology used by leading Cybersecurity products.

The phrases machine learning, deep learning, and artificial intelligence (AI) have entered practically every field of medicine.

And, it was first published on Dataconomy on Dec 21, 2016; I'm reposting here for ease of access. In May 2017 I gave an updated talk; here's the slides & video.] In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB.

Candidates bring a deep understanding of geometric computer vision and machine learning algorithms and technologies that are necessary building blocks of interactive, real-time, and immersive systems, including Simultaneous localization and mapping (SLAM), and sensor fusion, also applying ML/DL techniques to enhance accuracy and robustness of traditional geometric computer vision algorithms, handle 3D data, modeling geometries of 3D objects, computational geometry, and mathematical optimization. They need to exhibit excellent analytical and algorithm development skills. Experience in designing solutions and implementations for real-time embedded and mobile platforms would be a plus.

有限会社アズリアルと株式会社HashLab（ハッシュラボ）は10月4日、日本語で指示を入力するとAIが自動でソースコードを生成する「AI Programmer」のベータ版を提供開始した。プログラマーのサポートだけでなく、プログラミング学習などでの活用が期待される。 対応言語 ・SQL ・Python ・Jav

Spark Machine Learning Project (Employee Attrition Prediction) for beginners using Databricks Notebook (Unofficial) (Community edition Server) In this Data science Machine Learning project, we will create Employee Attrition Prediction Project using Decision Tree Classification algorithm one of the predictive models.

Learn how your company can create applications to automate tasks and generate further efficiencies through low-code/no-code tools on November 9 at the virtual Low-Code/No-Code Summit. Can artificial intelligence (AI) create its own algorithms to speed up matrix multiplication, one of machine learning's most fundamental tasks? Today, in a paper published in Nature, DeepMind unveiled AlphaTensor, the "first artificial intelligence system for discovering novel, efficient and provably correct algorithms." The Google-owned lab said the research "sheds light" on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices. Ever since the Strassen algorithm was published in 1969, computer science has been on a quest to surpass its speed of multiplying two matrices.

… model (people and skills) and how to deploy artificial intelligence (AI) and machine learning (ML) techniques to identify and remove the noise.

Physicists prove that classical machine learning models can improve predictions about quantum materials.