A small Maryland company took first place in last week's AlphaDogfight Trials Final event, a three-day competition designed to demonstrate advanced algorithms capable of performing simulated, within-visual-range air combat maneuvering – commonly known as a dogfight. Heron Systems' F-16 AI agent defeated seven other companies' F-16 AI agents and then went on to dominate the main event – a series of simulated dogfights against an experienced Air Force F-16 pilot – winning 5-0 through aggressive and precise maneuvers the human pilot couldn't outmatch. "The AlphaDogfight Trials were a phenomenal success, accomplishing exactly what we'd set out to do," said Col. Dan "Animal" Javorsek, program manager in DARPA's Strategic Technology Office. "The goal was to earn the respect of a fighter pilot – and ultimately the broader fighter pilot community – by demonstrating that an AI agent can quickly and effectively learn basic fighter maneuvers and successfully employ them in a simulated dogfight." The trials were designed to energize and expand a base of AI developers for DARPA's Air Combat Evolution (ACE) program.

Tim O'Reilly is a kind of bard of technology, a lyrical poet of computing's past, present and future. Ask him a question and whole paragraphs of reflection bubble up. Is the present state of artificial intelligence, for example, bigger than the open-source software revolution, an epochal development chronicled in detail from the front lines by O'Reilly's publishing company? "That's an interesting question," says O'Reilly, before re-framing it, declaring that there is something bigger than AI itself. "I think in the long run, this transformation to machine autonomy, and to, basically, machines that are in a new kind of hybrid existence with humans -- they talk about AI as separate from us, but all interesting machines are hybrids of human and machine -- we have this machine that has been amplifying things we can do, and I think of the human-machine symbiosis as a trend that is probably bigger than the internet, and bigger than open source, and of which AI is one manifestation."

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. At 10:30pm on 29 October 1969, a graduate student at UCLA sent a two-letter message from an SDS Sigma 7 computer to another machine a few hundred miles away at the Stanford Research Institute in Menlo Park. The student had meant to send "LOGIN," but the packet switching network supporting the transmission of the message, the ARPANET, crashed before the whole message could be typed out.

That moment helped plant the seeds for the era of big data in which we live today, and it marked a critical cultural turning point that suggested a machine could outsmart a human after all. While Kasparov initially expressed cynicism regarding the computer's methods and its intelligence, he has more recently changed his tune, crediting the power of artificial intelligence (AI) and advocating for a symbiotic relationship between humans and machines. "At the end of the day," he argued, "it is for us to even explain when something is successful. It is still for us to define success and machines to perform their duty" – underscoring the significance of our human role in defining and creating the knowledge base, the logic, and the authority that we empower our AI systems to wield. What does this mean for those of us who create AI systems, in the era of big data, and in an era where consumers are (rightfully) expecting and demanding that we leverage that data responsibly and accurately?

At the same time, however, a new and unwelcome consequence of our increasingly hyper-connected world is also likely to grow exponentially: the continuous uptick in hacking and massive data breaches. Until we transition IT systems control structures from linear hierarchical architecture to a more robust network architecture, the expansion of hyper-connected networks will result in hackers having many more opportunities to breach systems. If we are too slow in making this necessary shift, the day will likely come when one madman will be able to shut down an electrical grid, use IoT to spread a deadly virus, or possibly unleash a weapon of mass destruction. And if this were to happen, perhaps the concerns about an independent-minded AI might materialize should it harness its formidable intelligence and exercise its power of judgment to stop the madness or even eliminate the threat. Just as Jennings and Rutter were easily defeated by IBM's Watson, if human intelligence and machine intelligence remain separate entities, then these fears may prove true.

The game of Go invites analysis. The rules seem few and simple, suggesting that the game may have helpful theorems. Tens of millions of people play and skill has developed over centuries to extraordinary levels. Thus, computer analysis can be tested against analysis by highly skilled human players. We study M × N boards, rather than the usual 19 × 19.