In this episode I'm joined by Alexander Chukovski, Director of Data Services at Munich, Germany based career platform, Experteer. In this podcast we explore Alex's journey to implement machine learning at Experteer. Alex and I discuss the Experteer NLP pipeline and how it's evolved over time to address the company's need for greater automation in the way it processes jobs on its platform. We also discuss Alex's work with deep learning based ML models, including models like VDCNN and Facebook's FastText offering, which he's particularly excited about. Finally, we briefly discuss recent papers that look at transfer learning for NLP, how Alex keeps up with recent academic papers, and a few tips for people looking to inject ML/DL in their products or projects.

Elon Musk and many of the world's most respected artificial intelligence researchers have committed not to build autonomous killer robots. The public pledge not to make any "lethal autonomous weapons" comes amid increasing concern about how machine learning and AI will be used on the battlefields of the future. The signatories to the new pledge – which includes the founders of DeepMind, a founder of Skype, and leading academics from across the industry – promise that they will not allow the technology they create to be used to help create killing machines. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let's see how this thing actually works out in practice in the case of image classification. Before that let's introduce these two terms Keras and Tensorflow and help you build a powerful image classifier within 10 min! Tensorflow is the most used library to develop models in deep learning. It has been the best ever library which has been completely opted by many geeks in their daily experiments .

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Advanced Micro Devices is gearing up to join a race to accelerate deep-learning jobs in client and embedded systems. However, AMD is not yet ready to provide any specifics on the 7-nm x86 and GPU chips that it aims to deliver over the next year -- or its roadmap beyond 7 nm. "There is a need for high performance with what we call the edge [of the network] … closer to the source where data is coming in and [needing] to be analyzed -- often in real time," said Mark Papermaster, AMD's chief technology officer, in an interview. "AMD's machine-learning strategy is holistic and provides engines of AI for both the data center and the edge." In late 2016, AMD released its first GPU accelerators for deep learning in the data center.

With all the excitement over neural networks and deep-learning techniques, it's easy to imagine that the world of computer science consists of little else. Neural networks, after all, have begun to outperform humans in tasks such as object and face recognition and in games such as chess, Go, and various arcade video games. These networks are based on the way the human brain works. Nothing could have more potential than that, right? An entirely different type of computing has the potential to be significantly more powerful than neural networks and deep learning.

Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology -- or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. The result: software that ended up assembling bits of human-created programs, a feat Wired Magazine referred to as "looting other software." And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large.

Testing, testing: DeepMind sits AI down for an IQ test. While the AI performance results are not staggering in trumping or matching human reasoning, it is a start. AI scientists recognize that establishing their capacity to reason about abstract concepts has proven difficult. DeepMind wanted to see how AI could perform and the team proposed a dataset and challenge to probe abstract reasoning. Can AI match our abilities for abstract reasoning?

In Part 1 of this series, we have studied the Forward and Backward passes of a Feed Forward Fully-Connected network. In spite of the fact, that Feed Forward networks are widespread and find a lot of real-world applications, they have a main limitation. Feed Forward networks cannot handle sequential data. This means that they cannot work with inputs of different sizes and they do not store information about previous states (memory). Thus, in this article, we will talk about Recurrent Neural Networks (RNNs) allowing overcome named limitations.

Thousands of the top names in tech have come together to take a stand against the development of killer robots. Tesla and SpaceX CEO Elon Musk, who has long been outspoken about the dangers of AI, joined the founders of Google DeepMind, the XPrize Foundation, and over 2,500 individuals and companies in signing a pledge this week condemning lethal autonomous weapons. The industry experts vowed to'neither participate in nor support' the use of such weapons, arguing that'the decision to take a human life should never be delegated to a machine.' The move comes amid growing fears of AI systems that could target and kill a person without a human at the reins to make the final judgement call. 'There is an urgent opportunity and necessity for citizens, policymakers, and leaders to distinguish between acceptable and unacceptable uses of AI,' industry experts argue in the pledge, which was released on Wednesday at the 2018 International Joint Conference on Artificial Intelligence in Stockholm.