Apple introduced CoreML in WWDC 2017, and it is a great deal. CoreML is a machine learning framework used in many Apple products, like Siri, Camera, Keyboard Dictation, etc. The cool stuff about CoreML is that it can use a pre-trained model to work offline. Apple has provided lots of pre-trained models like MobileNet, SqueezeNet, Inception v3, VGG16 to help us with image recognition tasks, especially detecting dominant objects in a scene. The job of CoreML is simply predicting data based on the models.

Microsoft has explained before that it uses PC "telemetry" information and machine learning to assess the readiness of PCs for Windows 10 feature upgrade releases, which arrive twice per year. In a blog post this week, Microsoft data scientists Archana Ramesh and Michael Stephenson expanded on how the company uses machine learning algorithms to ensure those feature upgrades are rolled out successfully at organizations. Background A Windows 10 feature upgrade essentially replaces the underlying operating system's bits with a new OS version. The process is called an "in-place upgrade." However, there are lots of potential issues that can arise with each OS upgrade, given the variations in hardware, drivers and software that exist, as well as flaws that may be present in the upgrade itself. Some Windows 10 upgrade releases have been minor disasters.

Fluid Dynamics is around us every day. Any flow moving fast enough or with a small kinematic viscosity generates turbulence. Turbulent flows, like atmospheric and oceanic circulations or flows around vehicles, develop extremely complex dynamics coupling structures over a large range of scales, and with chaotic behavior, which makes them unpredictable. For engineers this creates significant design challenges as they have to find aerodynamic data that best replicates the conditions in the outside world. Today, scientists rely on experiments such as wind tunnels or numerical simulations performed on the largest supercomputers in the world, an approach known as computational fluid dynamics (CFD).

Data science is a required practice for organizations accelerating their journeys to AI. Businesses are keen on hiring the right talent, acquiring the right tools and evolving the discipline. Solving the lack of data scientists' problems requires investment in our employees in terms of time and training. We can't expect these people to just keep on learning for a year before they can be productive. We need to reach a stage where people know enough to start contributing immediately while continuing to improve their skills. As far as the second problem is concerned, taking too much time getting to a usable and tuned model, we need tools to help us optimize our data scientists' productivity.

The exponential growth in data has proven to be a gamechanger in marketing, especially with the introduction of cognitive computing and AI. IBM Watson is breaking new ground in this area and speaking more on this is Marta McMichael, global director of performance marketing at IBM Watson IoT. With an extensive background in the high-tech industry, Marta has worked in varied roles, including working as a programmer, a consultant and managing large account sales at IBM. It is here at IBM that she discovered her passion for marketing and transitioned into it. In the interview, Marta shares her big career epiphany that helped her refocus on creating value for her clients.

David Ferrucci led the team that built Watson, the IBM question-answering system that beat the top humans in the world at the game of Jeopardy. He is also the Founder, CEO, and Chief Scientist of Elemental Cognition, a company working engineer AI systems that understand the world the way people do. This conversation is part of the Artificial Intelligence podcast.

Machine Learning in Pharmaceutical Market Research Report has been studied and presents an actionable idea to key contributors working in it. A thorough study of the competitive landscape of the global Machine Learning in Pharmaceutical Market has been given, presenting insights into the company profiles, financial status, recent developments, mergers and acquisitions, and the SWOT analysis. This report has published stating that the Global Machine Learning in Pharmaceutical Market is anticipated to expand significantly at Million US$ in 2019 and is projected to reach Million US$ by 2026, at a CAGR of during the forecast period. The global Machine Learning in Pharmaceutical market can be segmented based on product type, application, end-user, and region. This report gives an in depth and broad understanding of market with accurate data covering all key features of the prevailing market, this report offers prevailing data of leading companies.

Australian IBM cybersecurity engineers have developed an artificial intelligence (AI) system to analyse network connections and employee communications at an enterprise scale. The model detects changes in users' behaviour and can automatically triggers investigations even if the changes occur across multiple platforms. IBM research found the root cause for 52 per cent of data breaches in Australia was malicious or criminal attacks which often use methods like phishing and social engineering. The new IBM solution, developed in the company's Gold Coast cybersecurity lab as part of a hackathon, uses AI to monitor changes in employee behaviour and flags indicators of compromise. It was debuted to the industry at last week's Australian Cyber Conference in Melbourne as a way of showing what can be done but the solution is not something that can be bought directly from IBM. Currently known as "QRadar Insider Threat Detector with Watson" it uses IBM's AI model, Watson, to analyse user generated content – like emails, Word documents, and Slack messages – to detect both the tone of content and employees' typical behaviour or "personalities".

I look forward to the next two posts. I hope you cover the challenges with public AI apps. Being able to ensure data privacy is maintained by being able to use this technology in private clouds or on premise would be helpful. Application portability and data security is also key.

AI has gone through many cycles since we first coined the term "machine learning" in 1959. Our latest resurgence began in 2011 when we put Watson on national television to play Jeopardy! This became a cornerstone event, demonstrating that we had something unique. And we saw early success, putting Watson to work on projects with clients. This created even more excitement. That excitement led to more opportunity.