In this article, I cover the 12 types of AI problems i.e. I address the question: in which scenarios should you use Artificial Intelligence (AI)? Recently, I conducted a strategy workshop for a group of senior executives running a large multi national. In the workshop, one person asked the question: How many cats does it need to identify a Cat? This question is in reference to Andrew Ng's famous paper on Deep Learning where he was correctly able to identify images of Cats from YouTube videos.

Last week, at an event on AI, I asked the panel about how investors evaluate the Data readiness of AI start-ups. This subject is close to my work and my teaching. I teach a course on Implementing Enterprise AI and also teach Data Science for IoT at the University of Oxford. Professor Neil Laurence has proposed a concept of Data readiness levels. The highest level of Data readiness represents Data which is most useful to make predictions i.e. "Can we use this data to prove the efficacy of a drug?"

During a keynote at his company's big annual conference in Silicon Valley last week, NVIDIA CEO Jensen Huang took several hours to announce the chipmaker's latest products and innovations, but also to drive home the inevitability of the force that is Artificial Intelligence. NVIDIA is the top maker of GPUs used in computing systems for Machine Learning, currently the part of the AI field where most action is happening. GPUs work in tandem with CPUs, accelerating the processing necessary to both train machines to do certain tasks and to execute them. "Machine Learning is one of the most important computer revolutions ever," Huang said. "The number of [research] papers in Deep Learning is just absolutely explosive."

Computers are advancing so fast and what they're being used for no in the medical field has changed dramatically over the past decade. Artificial intelligence and deep learning techniques are being used to change the way in which various processes are carried out. With thanks to neural networks, computers are beginning to learn o their own how to best solve a problem and come up with a solution far more efficient or accurate than a human could ever deliver. Just last year Cancer Moonshot began. The purpose of this program is to progress cancer prevention, diagnosis, and treatment, largely through the use of AI and other algorithms.

An alternative job description was created by replacing the job title "infrastructure engineer" with "person" and removing the two other references. Now we run the job title prediction model on both job descriptions and compare the resulting embeddings with the learned job title embeddings from the model using the cosine similarity. Given the fact that "Isuzu technician" was not part of the job titles in our training data set, the prediction "auto technician" makes sense. The following tables show the top 5 input patterns from all job descriptions of the test data set for several filters.

Open source deep learning neural networks are coming of age. There are several frameworks that are providing advanced machine learning and artificial intelligence (A.I.) capabilities over proprietary solutions. How do you determine which open source framework is best for you? In "Big data – a road map for smarter data," I describe a set of machine learning architectures that will provide advanced capabilities to include image, handwriting, video, and speech recognition, natural language processing and object recognition. There is no perfect deep learning network that will solve all your business problems.

Thanks to great experimental work by several research groups studying the behavior of Stochastic Gradient Descent (SGD), we are collectively gaining a much clearer understanding as to what happens in the neighborhood of training convergence. This paper I first discussed several months ago in a blog post "Rethinking Generalization in Deep Learning". Leslie Smith and Nicholay Topin, recently submitted a workshop paper to the ICLR 2017 workshop: "Exploring Loss Function Topology with Cyclic Learning Rate" where they discover some peculiar convergence behavior: Here, as you monotonically increase and decrease the learning rate, there is a transition near at the convergence regime that a large enough learning rate perturbs the system right off is basin into a space of much higher loss. There is however one pragmatic take away from this paper "Averaging two models within a basin tend to give a error that is the average of the two models (or less).Averaging two models between basins tend to give an error that is higher than both models".

Applying the blockchain concept to the world of [Internet of Things] offers fascinating possibilities. Right from the time a product completes final assembly, it can be registered by the manufacturer into a universal blockchain representing its beginning of life. Once sold, a dealer or end customer can register it to a regional blockchain (a community, city or state). We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions.

Learning to learn has gone from a fringe research area to a Google I/O keynote in just 1 year. The pace of progress in ML is insane. Here is one with an implementation, woohoo! DeepArchitect: Automatically Designing and Training Deep Architectures by Renato Negrinho, Geoff Gordon In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored.

Deep Learning is growing in power, scale, availability and frameworks. Open Source tools for Neural networks are everywhere, you have so much choice. Interesting new developments like PaddlePaddle, Keras and Deep Water are showing up and updating frequently. Unfortunately a lot of them take some serious power in GPUs, number of nodes, RAM, disk space, network bandwidth and CPU cores like IM2TXT for TensorFlow. These are very good uses of your 100 node Hadoop clusters especially ones running on AWS with GPUs.