Focusing on what you know best often makes the most sense. In an interview with VentureBeat's editor in chief, Blaise Zerega, Line CEO Takeshi Idezawa explained his decision to focus on four Asian markets: Japan, Taiwan, Thailand, and Indonesia. A few years ago, Line sought to enter Western markets. The messaging app known for cute anime stickers encountered stiff competition from overlapping services in an overcrowded market -- from WhatsApp to Snapchat, from Twitter to Instagram, from iMessage and SMS, as well as China's WeChat, to say nothing of Facebook Messenger. Line scaled back its expansion, shelved a planned IPO, and narrowed its focus to four Asian markets.

This hunger for data, or "statistical inefficiency" is perhaps the most significant practical limitation of current deep learning technology. Many of our clients at Scyfer have problems that could be solved by deep learning, but don't have large annotated datasets. Scyfer Active Learning Platform: once integrated, our system will passively observe the work of a domain expert (whether that's a medical doctor diagnosing patients or a factory worker identifying defective products). As the system is starting to learn how to imitate the expert, it will identify its own weaknesses and ask for guidance from the expert, thereby greatly accelerating its learning without requiring so many examples. Data-efficient deep networks: by building in prior knowledge, like "a rotated teddy bear is still a teddy bear", we can drastically reduce the number of examples required to learn a new concept.

OpenNMT is a industrial-strength, open-source (MIT) neural machine translation system utilizing the Torch mathematical toolkit. OpenNMT is used as provided in production by major translation providers. The system is designed to be simple to use and easy to extend, while maintaining efficiency and state-of-the-art translation accuracy. Simple general-purpose interface, requires only source/target files. Speed and memory optimizations for high-performance GPU training.

Me: I'd like to order a pizza Chatbot: What kind would you like? Me: What toppings do you have? Chatbot: I have pepperoni, sausage, Canadian bacon, ham, pineapple, mushrooms, peppers. Would you like to hear more? Me: Yes Chatbot: I also have ground beef, onions, spinach, tomatoes, cherry tomatoes, parmesan, and extra cheese, Would you like to hear more? Chatbot: What size would you like?

Vicarious has the mission to "build the next generation of artificial intelligence algorithms." That said, its objectives are longer-term in nature. Vicarious has assembled a who's who of technology legends as investors, including Jeff Bezos, Elon Musk, Peter Thiel, and Mark Zuckerberg. Co-founder, Scott Phoenix is clear that the biggest value Vicarious can contribute will be in the long-term, in the form of artificial general intelligence (AGI), or human-like intelligence. There will be plenty of value created in the interim in the form of what Phoenix refers to as the "exhaust" of the process.

Is Cybersecurity the Next Frontier for AI? Nextgov: President-elect Donald Trump's plans for federal cybersecurity aren't clear, but some contractors are gearing up for a spike in business over the next few years. One company that builds an artificial intelligence-based forecasting product called Eureqa for federal customers, including the Air Force, is segueing into the cybersecurity market. Nextgov spoke with Michael Schmidt, founder of Nutonian, which is currently piloting Eureqa at a few federal agencies, and David Rubal, chief technology officer of analytics at re-seller DLT Solutions, about trends in federal demand for artificial intelligence. Analyst: Trump administration could enrich contractors, not customer experience Obama Confronts Complexity of Using a Mighty Cyberarsenal Against Russia Analyst: Trump administration could enrich contractors, not customer experience Senators call for probe of cyber attacks by Russia Here's some questions Congress should ask about the election-related hacks Last chance to give feedback on health IT offerings for GSA/DHA eTool Earnest: Intelligence Conclusions on Election Hacking Not Political Report: What Trump Needs to Do About Connected Devices Rick Perry, climate change skeptic, soon to oversee U.S. supercomputing The FCC could soon be paralyzed in a partisan stalemate Chief Innovation Officers: An Unclear Role in the Federal Government Government orgs plagued by botnet compromises, says security report Senate Republican leader backs investigation into Russian hacking US, China hold third dialogue on jointly battling cybercrime Here's some questions Congress should ask about the election-related hacks Rick Perry, climate change skeptic, soon to oversee U.S. supercomputing

Imagine pulling into a Starbucks drive-thru and seeing not just your drink order but your name on the screen -- along with the suggestions of what foods you might like with your drink, automatically generated by the weather, your buying history, and the choices that others with similar preferences have made. Coming soon to a Starbucks drive-through near you -- and to your smartwatch, and possibly to each store's cash register -- are serving suggestions generated by artificial intelligence. It's all part of the coffee giant's plan to use AI and the cloud to drive sales and growth, as laid out in a 45-minute presentation this month at Starbucks Investor Day in Manhattan. "I hope you're convinced we're continuing to set the bar for digital in brick-and-mortar," said Matt Ryan, Starbucks chief strategy officer, after laying out the company's technology plans. It gives us the advantage we have moving forward." Seattle-based Starbucks is going strong -- growing steadily and last month ...

Mark Zuckerberg says he's built an artificially intelligent, voice-controlled butler for his home – and named it Jarvis, after superhero Iron Man Tony Stark's home computer. The Facebook honcho's AI personal assistant can turn the house lights on and off, play songs, recognize who's at the front door and let people in, manage the temperature, activate a toaster – and has even been programmed with a sense of humor. "I've taught it fun little games like [wife] Priscilla or I can ask it who we should tickle and it will randomly tell our family to all go tickle one of us, [daughter] Max or [pet dog] Beast," Zuckerberg writes in a Facebook post Monday. "I've also had fun adding classic lines like'I'm sorry, Priscilla. Zuckerberg said the project was one of his 2016 challenges. "In some ways, this challenge was easier than I expected," he wrote, comparing it to his other goal of running 365 miles in 2016. He says building Jarvis, which "uses several artificial intelligence techniques, including natural language processing, speech recognition, face recognition, and reinforcement learning, written in Python, PHP and Objective C," took less time than the running. But he said one of the biggest takeaways from the project was figuring out the promise and limits of AI. "In a way, AI is both closer and farther off than we imagine," he wrote. "AI is closer to being able to do more powerful things than most people expect – driving cars, curing diseases, discovering planets, understanding media.

Eric is responsible for the development of strategic products, services and solutions. He joined Esker in 2002 as Director of Marketing Communications, and in 2005, extended his responsibilities to include product management. Many businesses have yet to understand the full potential of machine learning and cognitive computing for back office processes. The benefits range far beyond simply increased productivity and faster supply chains. Today's advanced algorithms have the computational power and speed to gather, analyze and manage vast repositories of data.

We've written a lot about the convergence of cloud infrastructure, Big Data, and artificial intelligence (AI) this year. Throughout the Software-as-a-Service (SaaS) space, we've seen an inextricable link between these three factors in business intelligence (BI) tools, social listening platforms, customer relationship management (CRM) solutions, or really any industry that's leveraging cloud-based data ingestion and analysis--which is pretty much all of them. Across use cases, we've observed a four-step process. Enterprise businesses gather massive amounts of data by using a portfolio of SaaS apps. They then store that data in the cloud by using a data warehouse or data lake, using data governance to keep data compliant and secure.