Machine learning is a technology that has recently matured enough to show business benefits, and those benefits are incredible. By training machines to understand and perform certain tasks, businesses can reap the rewards of human ability combined with machine speed. One place where machine learning can be a gamechanger is the medical field. To shed some light on these developments, Jeff Frick (@JeffFrick), cohost of theCUBE, from the SiliconANGLE Media team, joined Scarlett Spring, president and chief commercial officer at VisionGate, Inc., during the Girls in Tech Catalyst Conference 2016 event. The conversation opened up as Spring explained the technology her company was developing.

In particular, I checked out the k-Nearest Neighbors (k-NN) and logistic regression algorithms and saw how scaling numerical data strongly influenced the performance of the former but not that of the latter, as measured, for example, by accuracy (see Glossary below or previous articles for definitions of scaling, k-NN and other relevant terms). The real take home message here was that preprocessing doesn't occur in a vacuum, that is, you can prepocess the heck out of your data but the proof is in the pudding: how well does your model then perform? Scaling numerical data (that is, multiplying all instances of a variable by a constant in order to change that variable's range) has two related purposes: i) if your measurements are in meters and mine are in miles, then, if we both scale our data, they end up being the same & ii) if two variables have vastly different ranges, the one with the larger range may dominate your predictive model, even though it may be less important to your target variable than the variable with the smaller range. What we saw is that this problem identified in ii) occurs with k-NN, which explicitly looks at how close data are to one another but not in logistic regression which, when being trained, will shrink the relevant coefficient to account for the lack of scaling. As the data we used in the previous articles was real-world data, all we could see was how the models performed before and after scaling.

Machine types that accept non-regular languages 20.

Dag Kittlaus and Adam Cheyer created the artificial intelligence behind Siri, Apple's iconic digital assistant, and one of the first modern apps to capably handle natural language queries on a smartphone. Today the pair showed off their newest creation, Viv, a next generation AI assistant that they have been developing in stealth mode for the last four years. The goal was to create a better version of Siri, one that connected to a multitude of services, instead of routinely shuffling queries off to a basic web search. During a 20-minute demo onstage at Disrupt NYC, Viv flawlessly handled a number of complex requests, not just in terms of comprehension, but by connecting with third-party merchants to purchase goods and book reservations. Viv's approach is much closer to Amazon's Alexa or Facebook's Messenger bots, offering the ability to connect with third-party merchants and vendors so that it can execute on requests to purchase goods or book reservations.

In the previous blog post, we discussed the nature of sequential data and why we need a robust separate modeling technique to analyze that data. Traditionally, people have been using Hidden Markov Models (HMMs) to analyze sequential data, so we will center the discussion around HMMs in this blog post. HMMs have been implemented for many tasks such as speech recognition, gesture recognition, part-of-speech tagging, and so on. But HMMs place a lot of restrictions as to how we can model our data. HMMs are definitely better than using classical machine learning techniques, but they don't fully cover the needs of all the modern data analysis.

For newbies this is the best place to start; introductions, FAQs and a glossary of terms. Information on the different types of learning algorithms used in AI and ML systems and applications. A list of different software tools, used to simulate AI techniques, both free open source and commercial. A list of free data sets that can be used for research and testing of AI learning algorithms. Find out how different hardware can be used to host and accelerate the performance of AI applications.

For the IT sector, the concept of digital transformation represents a time for evolution, revolution and opportunity, according to Information Technology Association of Canada (ITAC) president Robert Watson. The new president for the technology association made the statements at last week's IDC Directions and CanadianCIO Symposium in Toronto. The tech trends event was co-hosted by ITWC and IDC with support from ITAC. Notable sessions included the ITWC-moderated Digital Transformation panel -- which featured veteran CIOs discussing the digital transformation opportunities and challenges-- and IDC Canada's Nigel Wallis outlining why Canadian business models should shift to reap IoT rewards. Digital transformation refers to the changes associated with the application of digital technology in all aspects of human society; the overarching event theme focused on digital transformation as more than mere buzzword, but as process that tech leaders and organizations should already be adopting.

If you're interested in working with machine learning and artificial-intelligence algorithms--but unsure of how to start--check out the OpenAI Gym, now in beta. The premise behind OpenAI Gym is simple: it's a toolkit for building reinforcement learning (RL) algorithms, which govern bots' decision-making and motor-control capabilities. Reinforcement learning is a key element in A.I. development, as it allows software to deal with random, unpredictable environments; one "classic" problem involves balancing an untethered pole on a rolling cart: OpenAI is a non-profit "artificial intelligence research company" funded by some heavy hitters in the tech world, including Tesla CEO Elon Musk and venture capitalist Peter Thiel. Its altruistic goal is to develop open-source A.I. software that's "friendly" to humanity. According to a blog posting accompanying the launch of OpenAI Gym, RL research is slowed by two factors: a need for better benchmarks, and a lack of standardization of environments used in publications.

Dag Kittlaus wants you to imagine buying a consumer electronic device in the near future. You take it out the box, plug it into the wall, unlock it with a biometric thumbprint, and then the device comes to life. "Hi, nice to meet you," it says, before walking you through its setup via natural conversation. That scenario isn't too far away, according to Kittlaus, who used today's TechCrunch Disrupt in Brooklyn as part of a coming-out-party for Viv, a new voice-activated digital assistant. After three round of venture capital (VC) funding and more than a year in development, Viv is ready for primetime.

Google, Amazon, Apple, IBM, Yahoo, Facebook, Intel, and Salesforce are all hustling to either invest in new artificial intelligence technology or acquire it outright both in New York and in other hubs across the USA. Lumped into this category are a variety of innovations, including image processing, natural language, machine learning and predictive APIs (application program interface). To find out how much activity has been going on within the AI sector in Silicon Alley, the New York Business Journal tapped PitchBook for help. PitchBook expects that as more AI technology gets integrated into different categories -- business intelligence, e-commerce, and healthcare -- the number of deals this year will surpass 2015 totals, both in terms of volume and capital invested (see the infographic above). According to previous coverage, a wide majority of deals across the U.S. (between 2010 to 2015) were seed stage, accounting for 50 percent of overall activity in 2015.