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.

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.

PHOENIX--(BUSINESS WIRE)--Aspect Software, a global provider of fully-integrated consumer engagement, workforce optimization and self-service solutions, announced today that it is making its Aspect Mila personal assistant chatbot available on leading workforce collaboration and messaging platforms such as Slack, Microsoft Teams and Facebook Workplace. Announced last year, Mila provides contact center and enterprise employees with automated self-service functionality to view schedules, request time off or trade shifts with other employees. Mila simplifies day-to-day administrative challenges faced by thousands of employees in call centers and other large labor force employers while also reducing the oftentimes labor-intensive workload of schedule management. With plain, conversational language, Mila allows employees to easily ask for a day off or pick up a shift right from within Slack. Supervisors and managers no longer have to field the administrative requests from employees, leaving that instead to Aspect Mila and Aspect's Workforce Management solution.

Peruse the headlines, and you'll find hundreds of articles predicting the "next Silicon Valley." Pundits claim that the growing cost of living in the Bay Area is driving businesses away to more affordable regions that are rich in tech talent such as Austin, Phoenix, Boulder, and Miami. But try as they might, these "hubs" won't ever beat Northern California at its own game – the area will continue to dominate information technology because of its unique arbitrage of thought, culture and research. But that's okay, because striving to replicate the success of the Bay Area limits us by imposing an arbitrary constraint on our imagination. After all, does Silicon Valley represent the pinnacle of success in human innovation, or are there regions that have the potential to evolve into something even greater?