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Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

Keras models follow Scikit-learn's interface of fit/predict with some notable extensions.

The Future of AI: Blockchain and Deep Learning First point: considering blockchain and deep learning together suggests the emergence of a new class of global network computing system. These systems are self-operating computation graphs that make probabilistic guesses about reality states of the world. Second point: blockchain and deep learning are facilitating each other's development. This includes using deep learning algorithms for setting fees and detecting fraudulent activity, and using blockchains for secure registry, tracking, and remuneration of deep learning nets as they go onto the open Internet (in autonomous driving applications for example). Blockchain peer-to-peer nodes might provide deep learning services as they already provide transaction hosting and confirmation, news hosting, and banking (payment, credit flow-through) services.

Deep learning tech is making itself at home in hospitals by helping radiologists examine medical scans for just a buck per image. Now, GE Healthcare is bringing that AI tech directly to the scanners, thanks to partnerships with NVIDIA and Intel. It announced that it will update 500,000 of its medical devices around the world with NVIDIA AI tech, most notably its Revolution Frontier CT scanner (below). The tech "is expected to deliver better clinical outcomes in liver lesion detection and kidney lesion characterization because of its speed," GE wrote in a press release. The tech will also be used in GE's advanced ultrasound imaging devices to provide visualization and quantification of data. "NVIDIA's GPUs accelerate reconstruction and visualization of blood flow and improve 2D and 4D imaging for ... interventional deployments," the company said.

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning are some of the buzzwords swirling around today. You'e more likely to hear about AI and ML when tech companies talk about voice assistants and smart home devices. Now, while Artificial Intelligence and Machine Learning are very much related, they are not the same thing. Let's dive in a little deeper to understand what these terms mean. While tech giants have started talking about AI more recently, it is something that existed decades ago, and you probably didn't even realize it back then.

Note: The top row (left to right) represent the artistic style which is transposed onto the original images which are displayed in the first column (Woman, Golden Gate Bridge and Meadow Environment). Using conditional instance normalisation a single style transfer network can capture 32 style simultaneously, five of which are displayed here. This work will feature in the International Conference on Learning Representations (ICLR) 2017. Source: Dumoulin et al. (2017, p. 2)[79] Style transfer as a topic is fairly intuitive once visualised; take an image and imagine it with the stylistic features of a different image. This year Facebook released Caffe2Go,[80] their deep learning system which integrates into mobile devices.

Too much hard work needed to make a model work, if at all it works:-) To much effort is required to change those trivial network settings and configurations. Knowledge of'black-art' to tune/initialize parameters. Lot of computational time and memory is needed, forget to run deep learning programs on a laptop or PC, if your data is large. Lot of book-keeping is needed to analyze the outcomes from multiple deep learning models you are training on. Filters produced by the deep network can be hard to interpret.

New Delhi: Artificial Intelligence (AI) companies' revenue projections are increasing at a fast pace and expected to touch around $3.06 billion by 2024, says an Avendus Capital report. According to the report, Deep Learning is expected to have the fastest growth within the Arificial Intelligence space and will become the largest portion of total AI companies revenue. "Artificial Intelligence revenue projections are on a fast growth axis as they are increasing at a rate of CAGR 40 percent and are expected to be at a value of $3,061 million in 2024," the report said. Moreover, increased demand for robots has led to a rise in investment and M&A in the Artificial Intelligence space. According to the report, AI industry has received more than $11.5 billion of investments in the last three years and going forward, over $6 billion of VC investments are expected in 2017.

I recently completed all available material (as of October 25, 2017) for Andrew Ng's new deep learning course on Coursera. I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner. For example, Ng makes it clear that supervised deep learning is nothing more than a multidimensional curve fitting procedure and that any other representational understandings, such as the common reference to the human biological nervous system, are loose at best. The specialization only requires basic linear algebra knowledge and basic programming knowledge in Python.