Social media app Instagram announced this week it would start rolling out longer videos -- up to 60 seconds as opposed to just 15, the current limit. "This is one step of many you'll see this year," the tech giant promised in a Tuesday blog post. "In the last six months, the time people spent watching video increased by more than 40 percent. And longer videos mean more diverse stories from the accounts you love, whether it's Selena Gomez (@selenagomez) hanging out with friends or beauty star Bretman Rock's (@bretmanrock) latest makeup tutorial." So what are you going to do with all that freedom?

This post explores the idea that if we can successfully learn multiple levels of representation then we can generalize well. The below flow charts illustrate how the different parts of an AI system relate to each other within different AI disciplines. The shaded boxes indicate components that are able to learn from data. Rule-based systems are hand-designed AI programs. The knowledge required by these programs are provided by experts in the concerned field.

I'm looking for help/direction for the use of a text classification engine powered by universal taxonomy in making certain ML processes more efficient through providing context to entities extracted from a corpus in real time. My company, eContext, has curated a universal taxonomy over the past nine years that encompasses everything commercially and socially relevant on the web. It is made up of 650M real user search queries bucketed into 25 vertical categories (Auto, Health, Finance, etc.) containing roughly 450K sub-categories. It's a rule-based system, and we use NLP and nGram chunking to parse long and short form text and map search queries, social posts, web content, blogs, forums, reviews, etc. to the category hierarchy providing structured, topical intelligence to data streams at scale. It is extremely accurate because we've built 55M controlled vocabularies (Ex.

Yesterday, the 46-year-old Google veteran who oversees the company's search engine, Amit Singhal, announced his retirement. And in short order, Google revealed that Singhal's rather enormous shoes would be filled by a man named John Giannandrea. On one level, these are just two guys doing something new with their lives. But you can also view the pair as the ideal metaphor for a momentous shift in the way things work inside Google--and across the tech world as a whole. Giannandrea, you see, oversees Google's work in artificial intelligence.

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization's business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don't expect to see machines displacing knowledge workers anytime soon -- they expect computing technology to augment rather than replace the work of humans.

Ray Bradbury and others envisioned a world in which human needs are anticipated by the surrounding environment. In the late 1990s, this vision was termed "ambient intelligence" (AmI). Since that time, innovators have continued to imagine scenarios in which technology is ubiquitous, more transparent, and more valuable to humans than it ever has been. What the user interface will ultimately look like is a matter of debate. Some foresee a Minority Report scenario in which the surrounding environment itself adapts to individuals in context.

I'm looking for expertise in ML (mkting/adv applications a plus) to build and test a hypothesis around the use of text classification to a taxonomy. My employer eContext, has curated a general taxonomy that encompasses everything commercially and socially relevant on the web. It consists of 650M real user search queries bucketed into 25 vertical categories (Auto, Health, Finance, etc.) containing roughly 450K sub-categories. It's a rule-based system, and we use NLP and nGram chunking to parse long and short form text and map search queries, social posts, web content, blogs, forums, etc. to the category hierarchy providing structured, topical intelligence to data streams at scale. I understand that supervised training models require a corpus of text from which a model can determine entities, ontological connections, and apply statistical models to understand what people, places, things, concepts are and how they may be connected. That said, we've already built out the taxonomy to understand those connections and can provide greater context to "what" something truly is.

The risk of cyberattacks is one of the most dangerous threats facing businesses today. And while new versions of attacks are constantly being born, teams of analysts are rushing to keep up with the latest risks. While many detection systems rely primarily on machine learning for catching attackers, a new AI system at PatternEx depends on human analysts as a vital part of their system of supervised machine learning. Humans 2.0: How the robot revolution is going to change how we see, feel, and talk Robots aren't going to replace us, but by working hand in hand with us they will redefine what it means to be human. PatternEx's AI system is the first "virtual" security analyst team, and can predict, detect, and stop attackers in real time.

It is made up of 650M real user search queries bucketed into 25 vertical categories (Auto, Health, Finance, etc.) containing roughly 450K sub-categories. It's a rule-based system, and we use NLP and nGram chunking to parse long and short form text and map search queries, social posts, web content, blogs, forums, reviews, etc. to the category hierarchy providing structured, topical intelligence to data streams at scale. It is extremely accurate because we've built 55M controlled vocabularies (Ex. Being the noob I am, I am trying to understand how our real time classification capabilities can improve the efficiency of machine learned processes. I understand that supervised training models require a corpus of text from which a model can determine entities, ontological connections, and apply statistical models to understand what people, places, things, concepts are and how they may be connected, but we've already built out the taxonomy to understand connections between things, and can provide greater context to "what" something truly is.

See machine learning in action during "A Glimpse into the Future of Email Marketing – Reaping the Benefits of Machine Learning," featuring Kath Pay, Dela Quist, Skip Fidura and Jeremy Swift, May 19 at the Email Innovations Summit in Las Vegas. "Machine learning" has moved out of science fiction and into real-life applications, like powering Tesla cars that run on autopilot and robots that can beat humans at the Japanese game of Go. For marketers, it gets them closer to their email nirvana: true 1:1 personalization on a mass scale. Machine learning, at its simplest, is a method of data analysis that allows computers to learn – to analyze, predict and act – without explicit instructions or programming. That last phrase – "without explicit instructions or programming" – highlights the difference between today's rule-based marketing automation and systems that use machine learning.