As the Head Coach of The Data School, I need to be on top of all things Tableau so that I can pass that knowledge onto our trainees. What I really hope they pick up is my method for learning and my constant to desire to get better at my craft. While that's fantastic, unless they have an approach to problem solving, a thirst for learning, and a passion for sharing, I have failed as their coach, teacher, and mentor. A poster and web site to assist designers and journalists to select the optimal symbology for data visualisations, by the Financial Times Visual Journalism Team. The FT Visual Vocabulary is at the core of a newsroom-wide training session aimed at improving chart literacy.

As a machine learning expert, I predict there will soon be a new balance between human and machine intelligence, a shift that humanity hasn't …

Beethoven's unfinished symphony is set to be completed by artificial intelligence, in the run-up to celebrations around the 250th anniversary of the composer's birth. A computer is set to complete Beethoven's unfinished tenth symphony, in the most ambitious project of its kind. Artificial intelligence has recently been used to complete Schubert's'Unfinished' Symphony No. 8, as well as to attempt to match the playing of revered 20th-century pianist, Glenn Gould. Beethoven famously wrote nine symphonies (you can read more here about the Curse of the Ninth). But alongside his Symphony No. 9, which contains the'Ode to Joy', there is evidence that he began writing a tenth.

While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL. In particular, it remains elusive how to design a provably efficient policy optimization algorithm that incorporates exploration. To bridge such a gap, this paper proposes an Optimistic variant of the Proximal Policy Optimization algorithm (OPPO), which follows an "optimistic version" of the policy gradient direction. This paper proves that, in the problem of episodic Markov decision process with linear function approximation, unknown transition, and adversarial reward with full-information feedback, OPPO achieves O (\sqrt{d 3 H 3 T}) regret. Here d is the feature dimension, H is the episode horizon, and T is the total number of steps.

SANTA CLARA Calif., Dec. 16, 2019 – Intel Corporation today announced that it has acquired Habana Labs, an Israel-based developer of programmable deep learning accelerators for the data center for approximately $2 billion. The combination strengthens Intel's artificial intelligence (AI) portfolio and accelerates its efforts in the nascent, fast-growing AI silicon market, which Intel expects to be greater than $25 billion by 20241. "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need – from the intelligent edge to the data center," said Navin Shenoy, executive vice president and general manager of the Data Platforms Group at Intel. "More specifically, Habana turbo-charges our AI offerings for the data center with a high-performance training processor family and a standards-based programming environment to address evolving AI workloads." Intel's AI strategy is grounded in the belief that harnessing the power of AI to improve business outcomes requires a broad mix of technology – hardware and software – and full ecosystem support.

American technology giant Intel Corp. has acquired Israeli AI processor developer Habana Labs in a deal worth approximately $2 billion, the companies announced on Monday.Founded by David Dahan and Ran Halutz in 2016, Tel Aviv-headquartered Habana Labs is the developer of powerful artificial intelligence processors, optimized for training deep neural networks and artificial intelligence systems, and for deployment in production environments.Intel said the acquisition, the company's second largest in Israel to date, will strengthen its artificial intelligence portfolio and accelerate its efforts in the fast-growing AI silicon market, expected to be worth more than $25b. Chairman Avigdor Willenz will serve as a senior advisor to both the business unit and to Intel.The company currently employs 150 people worldwide at its offices in Tel Aviv, Caesarea, California, Beijing and Gdańsk."We We are excited to partner with Intel to accelerate and scale our business. Together, we will deliver our customers more AI innovation, faster."Prior to the acquisition, in November 2018, Intel Capital - the strategic investment arm of the Santa Clara-headquartered chipmaker - led a $75 million Series B round of fundraising for Habana.The purchase of Habana represents Intel's second largest in Israel to date and the latest in a long line of investments in the country. In August 2017, Intel purchased Jerusalem-based vision technology company Mobileye for a record $15.3b., the largest sale or "exit" of an Israeli company yet.Intel, which has been active in Israel since 1974, employs nearly 13,000 workers in the country, exporting products worth $3.9b. in 2018 and procuring local materials and services worth $1.7b., primarily from peripheral areas.In January, Intel announced plans to invest approximately $10.9b. in the company's Israel-based operations to construct a vast production facility in Kiryat Gat.

Artificial intelligence is now commonly used in journalism for anything from combing through large datasets to writing stories. To help you prepare for the future, the Journalism AI team at Polis, London School of Economics and Political Science (LSE), put together a training module seven things to consider before adopting AI in your news organisation. "Keep in mind that this is not a manual for implementation," writes professor Charlie Beckett who leads Journalism AI. "The recommendations will help you reflect on your newsroom AI-readiness but they won't tell you how to do design a strategy. We link to more resources that might help you with that and we hope to produce more training resources ourselves in the near future." For more insights into the Journalism AI report, you can watch this three-minute video, as well as Charlie Beckett's presentation of the report at its launch event.

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. A supervised machine learning algorithm (as opposed to an unsupervised machine learning algorithm) is one that relies on labeled input data to learn a function that produces an appropriate output when given new unlabeled data. Imagine a computer is a child, we are its supervisor (e.g. We will show the child several different pictures, some of which are pigs and the rest could be pictures of anything (cats, dogs, etc). When we see a pig, we shout "pig!"

Iowa State engineers are developing machine learning tools that can quickly and cheaply design better solar cells.

An international machine learning conference is set to land in Australia in 2021 following a successful bid by CSIRO's Data61 and tendering partner …