In the last post, we talked about how the open sourcing of machine learning algorithms and hardware architecture gives rise to the latest phenomenon of "Data is king". As companies compete to get developers to use their libraries, we continue to see this ongoing arms race like trend in open sourcing. In addition, we are also seeing how complicated the big data landscape is becoming. This is the link to the most recent 2016 Big Data Landscape compiled by Matt Turck. There are several new entrants into niche areas in the landscape.

It used to be that computer vision - getting a computer to not only'see', but also to'understand', the objects around it - was one of the hardest problem for engineers and researchers to solve. However, with modern advances in neural networks and machine learning, computer vision has become common place. Now, Google has shared the latest big advancements in this space, which spell doom for humanity. A cross between computer vision and machine learning, the system analyzes images and then describes what it sees in captions, which are supposed to be easily understandable and as human-like as possible. We've seen previous incarnation of such systems from both Google and Microsoft, not to mention other companies in the IT space.

Increased investor interest in AI startups – from around 10 deals in Q1'11 to over 120 in Q2'16 – can be attributed to recent advances in machine learning algorithms, particularly "deep learning" technology, a souped up version of AI. Just this week, Google integrated deep learning into its Google Translate tool; Baidu announced the launch of DeepBench, an "open source benchmarking tool for evaluating deep learning performance across different hardware platforms"; and NVIDIA introduced Xavier, a deep learning-based supercomputer for driverless cars. In the private market, Google put deep learning in the spotlight back in 2014 when it acquired 4 startups focused on this AI tech in quick succession: DeepMind, Vision Factory, Dark Blue Labs, and DNNresearch. Apple, which joined the race in 2015, most recently acquired Turi, which has developed a deep learning toolkit, among other AI-based solutions. Not to be outdone, Intel has acquired more than 5 AI startups this year alone, including deep learning startup Nervana Systems and, more recently, Movidius.

In this video, we look at how simple AI systems work, and how we can create "smarter" AI that create and execute plans. We'll learn how to code a modified form of the GOAP AI in a future programming tutorial. This month's dev challenge theme is artificial intelligence! They are used under fair use.

The ads will use a genetic algorithm to determine which genes in the ads including elements like copy, layout, fonts and image are successful. Elements that fail to trigger an engagement will be killed off where as those that do will be reproduced in future executions.

The biggest names in tech are teaming up to form a new organization dedicated to advancing the public's understanding of machine learning. The Partnership on Artificial Intelligence to Benefit People and Society (aka the Partnership on AI) is a non-profit whose members include Amazon, Google, Facebook, IBM, and Microsoft. As its lengthier title suggests, the organization's mission statement is to "address opportunities and challenges with AI technologies." It aims to achieve this goal through research, which will be made available to the public, on topics related to AI such as ethics, privacy, transparency, the exchange of information between machine learning systems, and collaboration between people and AI. Additionally, the partnership plans to host discussions, conferences, and outreach with the public.

With the ability to make intelligent decisions based on data and behavior triggers, Oracle states that it will help users streamline business tasks and provide them with individualized recommendations for engaging with prospective customers. The announcement, named Adaptive Intelligent Applications, was made at the company's annual OpenWorld Conference and came shortly after Salesforce unveiled its Einstein project, which will incorporate AI into its core cloud offerings. The moves may also be connected to buyer needs. In a study conducted by Marketing ID (a sister publication to Demand Gen Report), one-third (33%) of marketers surveyed say their top budget priority will be predictive tools, slightly more than CRM integration (31%) and campaign reporting and metrics (31%). Industry experts noted that AI will provide users with insights into customer activities they haven't had access to previously.

Artificial Intelligence (AI), is a form of scientific advancement where robots and computer programmes have an ability to make decision independently and get closer to the way we, humans, think. Development of AI has a huge role to play in future. Today we can see driveless cars being tested, self-operating space shuttles being sent to Jupiter and even a robotic waiters waiting to welcome you at certain restaurants. However, when it comes to the future of AI, the scene is bigger and better than ever. AI enthusiasts like us grew up hoping that someday'Back to the Future' will become a reality.

A positive label means that an utterance was an actual response to a context, and a negative label means that the utterance wasn't – it was picked randomly from somewhere in the corpus. Each record in the test/validation set consists of a context, a ground truth utterance (the real response) and 9 incorrect utterances called distractors. Before starting with fancy Neural Network models let's build some simple baseline models to help us understand what kind of performance we can expect. The Deep Learning model we will build in this post is called a Dual Encoder LSTM network.