Machine Learning Data Scientist wanted in central London. You will report into the Data Science Lead and will be responsible for assisting with the development and refinement of the company's core algorithms using machine learning principles.

Machine Learning Research Engineer wanted to join a leading research firm in London. You will be Implementing Machine Learning algorithms in real-world environments and help create impactful, currently non-existent solutions, which will have the potential to transform industries covering computer vision, Deep Learning, Theano, Tensorflow, biometrics, facial recognition, voice recognition and/or novelty/outlier detection.

You will be working closely with data scientists to design and implement machine learning algorithms and be Implementing data pipelines in Spark. The Senior Data Engineer will be contributing to the design and overall architecture of the system, implementing APIs and mentoring and assisting more junior members of the team.

A single conversation can be interpreted in very different ways. For people with social anxiety or conditions like autism, this can make social situations extremely stressful. But a new device that can detect if a conversation is happy or sad based on speech patterns could make life easier for people who struggle in these situations. The Samsung Simband predicts if a conversation is happy or sad based on a person's speech patterns. Long pauses and monotonous vocal tones were associated with sadder stories.

No matter how debonair you are at your best, conversation can be awkward for anyone. That's especially true for those who struggle to pick up on social cues. To help navigate those rocky exchanges, MIT CSAIL researchers have created a wearable system that can tell whether the person you're talking to is happy or sad. The device takes an existing research-grade wearable--Samsung's Simband smartwatch, which can measure movement, heart rate, blood pressure, blood flow, and skin temperature--and pairs it with audio capture that can pick up signals like tone, pitch, energy, and word choice, and provide a transcript of the text. By weighing all of the incoming signals, algorithms can classify each five-second installment of conversation as either "positive" or "negative."

Melville, NY (PRWEB) January 17, 2017 – Nastel Technologies, a global provider of enterprise-grade operations analytics and application performance monitoring (APM) solutions, announced the next-generation version of its flagship software platform, AutoPilot Insight 2.0. According to Charley Rich, VP Product Management "The new release fuses predictive anomaly and machine learning capabilities, business transaction tracking that spans corporate firewalls, raw information handling and analytics speed, and the flexibility to operate across dynamic IT environments--from mobile to mainframe. It provides the broad array of capabilities needed by developers, IT admins, and business analysts for enterprise-grade operations intelligence and APM." Rich said building a solution that fully addresses today's client requirements demanded two years of ground-up product re-engineering. "Customers universally remarked that they needed to find data outliers faster and sense problem conditions before they actually affect users. They also wanted more powerful end-to-end transaction tracking capabilities, with the ability to tie transaction performance to business outcomes."

This is the second article in a larger series exploring the intersection of design and existing artificial intelligence technology through experiments, prototypes and concepts. We believe this is a critically important topic for the design community and beyond, so we're sharing what we learn along the way. Let's start by getting something out of the way: we're not machine learning experts -- we don't publish research about new algorithmic breakthroughs and we're not especially good at math. But we're curious about what to do with all the machine learning capability already floating around out in the world, and we're bullish about how far a'good enough' understanding can often take you. So how might non-experts begin to play with machine learning?

Reinforcement Learning provides a framework for training agents to solve problems in the world. One of the limitations of these agents however is their inflexibility once trained. They are able to learn a policy to solve a specific problem (formalized as an MDP), but that learned policy is often useless in new problems, even relatively similar ones. Imagine the simplest possible agent: one trained to solve a two-armed bandit task in which one arm always provides a positive reward, and the other arm always provides no reward. Using any RL algorithm such as Q-Learning or Policy Gradient, the agent can quickly learn to always choose the arm with the positive reward.

By now, you've probably heard that Google is in the process of moving to mobile-first indexing. That means Google will crawl your site with a mobile agent and index it accordingly. Unfortunately, that also means you'll likely need to make some changes in your digital marketing strategy if you want to stay competitive. Here are some inconvenient truths that all SEOs must realize about Google's new mobile-first index. If you have a desktop-only website, you're going to get left behind as your competitors develop websites that are more responsive. Of course, you hopefully realized that long before Google announced that it was moving to a mobile-first index.