AI News, Google Artificial Intelligence 'Alpha Go Zero' Just Pressed Reset On ... artificial intelligence

Humans Are Still Better Than AI at StarCraft—for Now

That was clear on Tuesday after professional StarCraft player Song Byung-gu defeated four different bots in the first contest to pit AI systems against pros in live bouts of the game.

Though it has not attracted as much global scrutiny as the March 2016 tournament between Alphabet’s AlphaGo bot and a human Go champion, the recent Sejong competition is significant because the AI research community considers StarCraft a particularly difficult game for bots to master.

Following AlphaGo’s lopsided victory over Lee Sedol last year, and other AI achievements in chess and Atari video games, attention shifted to whether bots could also defeat humans in real-time games such as StarCraft.

Unlike Go, which allows bots and human players to see the main board and devote time to formulating a strategy, StarCraft requires players to use their memory, devise their strategy, and plan ahead simultaneously, all inside a constrained, simulated world.

(In StarCraft, players have to destroy all of their competitors’ resources by scouting and patrolling opponents’ territory and implementing battle strategies.) Song did find the bots impressive on some level.

Kim Kyung-joong, the Sejong University computer engineering professor who organized the competition, said the bots were constrained, in part, by the lack of widely available training data related to StarCraft.

AI Computer Wins First Match against Master Go Champion

Tanguy Chouard, an editor withNature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go.

As I’m sure you’ve heard, one of humanity’s best Go players, Lee Sedol, justlost his first gamein a best-of-five series against AlphaGo, the artificial intelligence system created by Google DeepMind.

From their smiles, you knew straight away that they were pretty sure they were winning—although the experts providing live public commentary on the match weren’t clear on the matter, and remained confused up to the end of the game just before Lee resigned.

Towards the end of the match, Michael Redmond, an American commentator who is the only westerner to reach the top rank of 9 dan pro, said the game was still “very close”.

We’d certainly seen Lee looking tense early on: Redmond had already told us—and the hundreds of journalists watching in two overflow rooms—that AlphaGo was playing more aggressively, in contrast to its relatively peaceful games last year against Fan Hui.

Unpacking Artificial Intelligence

From the future of work to the subversion of democracy, 2017 has seen artificial intelligence subject to a wide variety of associations.

But marketers should be careful not to conflate stories about experiments in deep neural network technology (such as headlines about AI’s ability to master our most challenging strategy games on their own, predict your sexual preference, and invent its own language) with game-changing technologies poised to dissolve our professions (much less our progeny).

With data comprising the backbone of AI and neural networks showing so much promise, an arms race is underway among researchers in government, academia, and the private sector seeking to master AI.

By training their neural networks with enormous amounts of data, these researchers use a process known as deep learning to discover nuanced patterns that human cognition is incapable of reaching alone.

When reckoning with speculations about AI and the future of work, it’s helpful to think about human capabilities in four main skill categories: manual routine, cognitive routine, manual nonroutine, and cognitive nonroutine.

In theory, manual routine tasks found in places like factory working and assembly lines will be the quickest to automate, whereas cognitive nonroutine tasks such as being creative (or any job entailing a high degree of interpersonal interaction, such as nurses or social workers) will grow in demand.

Anyone working in analytics, media, or traditional digital marketing such as search engine optimization (SEO) or search engine marketing (SEM) at a large agency knows the countless hours of routine number crunching and other cognitive chores are endemic to the attention economy – thankfully, this is where AI stands to most significantly benefit marketers in 2018.

Most existing solutions derive their value from customer relationship management solutions, like Salesforce’s Einstein and Marketo that increasingly bake in machine learning functionality that allows for predictive and anticipatory lead-generation tactics.

Adobe Smart Tags and Google’s Cloud Vision API also allow for smart digital asset management services, affording the automation of display ad trafficking by using computer vision to generate naming conventions on the fly.

Companies such as Affectiva and GumGum offer a glimpse at the emergent computer vision space, using facial recognition to track expressions and offer so-called “emotion as a service” for novel testing methodologies, as well as application programming interfaces (APIs) for creative experimentation.

Vendors such as Clarifai and LogoGrab use similar tech to provide services along the lines of “visual listening,” scraping the visual Web for instances of your brand or desirable user-generated content (UGC), as do existing social listening and sentiment analysis platforms such as Crimson Hexagon and Synthesio.

More interestingly, brands are charged to experiment using this data to better inform planning about participating customers, working toward building a robust micro-influencer ecosystem for future campaigns.

Individual consumer data can be analyzed to better track marketing dollars, with technologies such as visual listening, expression tracking, and sentiment analysis allowing marketers to better calculate impressions and engagement (provided the experience is shareable, of course).

For instance, executions such as Sephora’s Virtual Artist chatbot used a computer vision API to detect faces in user-submitted photos – paired with smart technology on the back-end, this activation essentially allows people to try on makeup before they buy it.

As business leaders, marketers have a responsibility to retrain, reinvest, and even consider readjusting revenue models and talent schemes to leverage AI in creative new ways, as opposed to simply reaping marginal efficiency gains to stay competitive in the short term.

The Brute Force Of IBM Deep Blue And Google DeepMind

There are interesting parallels between one of this week’s milestones in the history of technology and the current excitement and anxiety about artificial intelligence (AI).

Deep Blue was a specialized, purpose-built computer, the fastest to face a chess world champion, capable of examining 200 million moves per second, or 50 billion positions, in the three minutes allocated for a single move in a chess game.

To many observers, this was another milestone in man’s quest to build a machine in his own image and another indicator that it’s just a matter of time before we create a self-conscious machine complex enough to mimic the brain and display human-like intelligence or even super-intelligence.

But one specific move in Game 2 of the 1997 match, a game that Kasparov based not on tactics, but on strategy (where human players have a great advantage over machines), was “the lightning flash that shows us the terrors to come.” Krauthammer continues: What was new about Game Two… was that the machine played like a human.

AlphaGo used 1,920 Central Processing Units (CPU) and 280 Graphics Processing Units (GPU), according to The Economist, and possibly additional proprietary google Tensor Processing Units, for a lot of hardware power, plus brute force statistical analysis software (processing and analyzing lots and lots of data) known as Deep Neural Networks, or more popularly as Deep Learning.

But that explanation escaped observers, then and now, preferring to believe that humans can create intelligent machines (“giant brains” as they were called in the early days of very fast calculators) because the only difference between humans and machines is the degree of complexity, the sheer number of human or artificial neurons firing.

You build a device with enough number-crunching algorithmic power and speed—and, lo, quantity becomes quality, tactics becomes strategy, calculation becomes intuition… After all, how do humans get intuition and thought and feel?

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