Facebook CEO and cofounder Mark Zuckerberg at an event at Facebook's Menlo Park, Calif., headquarters to celebrate Facebook Friends Day with users from around the world. SAN FRANCISCO -- Facebook has been translated into three new languages -- and it has its users to thank. Be it "what's on your mind?" or the "like" or "share" buttons, a dedicated community of Facebook users want to make sure all the words and phrases on the social networking service are accurately translated into their native tongues. In all, Facebook is now available in 101 languages with the addition on Friday of Maltese (the official language of Malta that has more than 400,000 native speakers), Pulaar (a dialect of Fula spoken by more than 7 million across West and Central Africa), and Corsican (spoken by some 200,000 people and listed on UNESCO's Atlas of the World's Languages in Danger.) Human-powered translation is critical to Facebook's growth.

Sometimes you develop a small predictive model that you want to put in your software. Actually, there is a part that is missing in my knowledge about machine learning. All tutorials give you the steps up until you build your machine learning model. How could you use this model? In this post, we look at some best practices to ease the transition of your model into production and ensure that you get the most out of it.

Skymind, a company developing an open-source deep-learning library for Java, along with tools for implementation, today closed 3 million in financing from Tencent, SV Angel, GreatPoint Ventures, Mandra Capital and Y Combinator. Skymind was previously part of Y Combinator's Winter 2016 batch and has taken money from Joe Montana's Liquid 2 Ventures and a number of other prominent angels. Chris Nicholson, the company's co-founder and CEO, decided to start the company after he noticed the steady stream of deep-learning researchers leaving the halls of academia for the six- and seven-figure salaries of large tech companies. With human capital becoming a finite resource, the challenge quickly became about helping companies leverage existing resources to play in the world of deep learning. Eighty percent of the world's programmers are versed in Java programming.

Lots of prior work with hierarchy (hierarchical RNN / stacked RNN) and multi-scale (LSTM, clockwork RNN) but they all rely on pre-defined boundaries, pre-defined scales, or soft non-hierarchical boundaries. Avoids "soft" gating which leads to "curse of updating every timestep". Discrete (binary) decisions are difficult to optimize due to non-smooth gradients. Uses straight-through estimator (as an alternative to REINFORCE) to learn discrete variables. The simplest variant uses a step function on the forward pass and a hard sigmoid on backward pass for gradient estimation.

Data science sits at the core of any analytical exercise conducted on a big data or Internet of Things (IoT) environment. Data science involves a wide array of technologies, business, and machine-learning algorithms. The purpose of data science is not only to do machine learning or statistical analysis, but also to derive insights out of the data that a user with no statistics knowledge can understand. In a fast-paced environment such as big data and IoT, where the type of data might vary over the course of time, it becomes difficult to maintain and re-create the models each and every time. This gap calls for an automated way to manage the data-science algorithms in those environments.

As we inch closer to a time when we may rely on truly autonomous devices to move us or do things on our behalf, the need for software that's able to think on its feet (or mid-air) will be essential. Now, an artificial intelligence startup working on this emerging area of machine learning has raised a seed round of funding to try to do just that. Cambridge, UK-based Prowler.io, which is building a platform that can be used by makers of autonomous systems to help those machines think and learn to make better decisions, has raised 1.5 million ( 2 million). The company is still largely in stealth with little information available online. But CEO Vishal Chatrath tells me that the funding -- which comes from Passion Capital, Amadeus Capital and Singapore's Infocomm Investments -- will be used to continue research and development of its platform, as well as hiring more talent to build it.

Introduction In the previous article, we described combat performance in the Artificial Intelligence PK Gold Medal Stenography Competition and told the story behind the annual Alibaba Cloud meeting's Man VS Machine competition. Are there any curious technology geeks out there? What was the on-site real-time transcription system? What on earth is the core of a speech recognition system? How come the Alibaba Cloud iDST speech recognition system is so accurate?

One of the examples of these areas in action is work Accenture is doing using a a combination of drones, computer vision, Bayesian learning and geospatial analytics to survey palm fields in Indonesia. Through the application of Artificial Intelligence, we've been able to help a leading forestry company in Indonesia analyze over 1 million records and 6000 variables covering 15 years across 0.5 million hectares of land. This involves combinations of disparate data such as GIS, Video, Water table, Soil information, historical inventory, work orders and more. Through this work we've helped identify deforestation and growth patterns, and identify areas and species where planting seedlings to reforest is not effective. We've gone from what used to take 36 human hours of analysis down to minutes.

Microsoft said on Thursday it created a new artificial intelligence unit, as the company pushes deeper into the fast-growing field. Silicon Valley is diving into artificial intelligence (AI) and machine learning research, an industry estimated to zoom to 70 billion by 2020 from just 8.2 billion in 2013, according to a Bank of America report that cited IDC research. On Wednesday, Microsoft teamed up with four other big technology companies--Amazon.com, Alphabet unit Google, Facebook and IBM--to create a non-profit organization to advance public understanding of AI technologies. The new unit--Microsoft AI and Research Group--will be headed by Harry Shum, a company veteran who has held senior roles at the Microsoft Research and Bing engineering divisions.

The tech behemoths want consumers to feel comfortable with real-life versions of Ava, the robot from Ex Machina. The Partnership on Artificial Intelligence to Benefit People and Society was unveiled today by Google, Facebook, Amazon, IBM and Microsoft. Their chief mission is to promote public understanding of AI while developing standards and best practices for researchers to abide by. A press release late on Wednesday stated: "The objective of the partnership on AI is to address opportunities and challenges with AI technologies to benefit people and society. Together, the organization's members will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology. It does not intend to lobby government or other policymaking bodies."