The Lenny Letter This is a project from Lena Dunham and her partner, Jenni Konner, and is essentially just an email newsletter, but it has become very popular. It offers perspective on politics and society for a mostly female audience and it is attracted over 400,000 subscribers in six months. What's most important about the Lenny Letter is that it has a 65% open rate and advertisers are taking notice. In tech and growth hacking, engagement is still what matters most in marketing. You can check out the Lenny Letter at lennyletter.com.

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods. In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological "labels" to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don't use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

A patient walks into the emergency department at Rush University Medical Center with a fever and bloodshot eyes. In days of yore, doctors would have to exhaust the obvious possibilities -- like a flu or allergic reaction -- before finally figuring a patient was suffering from Zika virus. But Rush says its predictive software, a system called Guardian, helps alert doctors to a possible diagnosis hours faster than physicians normally would. When dealing with more rare diagnoses, doctors typically think, "it's going to be everything else besides smallpox," said Dr. Dino Rumoro, who's been working on the technology for more than 20 years and is the chairman of Rush's Department of Emergency Medicine. "This gives the clinician a crutch."

In fact, some of Weizenbaum's colleagues and students exhibited very strong emotional connections to the program. As the preceding passage suggests, Weizenbaum discovered something disturbing: people approached the bot as if it were a real, intelligent entity who was genuinely interested in their condition -- even when they knew that it was a computer program. This condition is now referred to as the "Eliza Effect", and although the behaviour of its sufferers isn't always as extreme as the case above (alone time with robots is not consistently requested), it propelled Weizenbaum to become a staunch critic of AI. The Eliza Effect is a prime example of how an object, in this case a typewriter, can form a visceral connection with just a few rudimentary human elements. All it took to elevate a keyboard and screen to a human level was a basic conversational program.

When sailors speak of "blue water," they anticipate taking a boat offshore, out of sight of land, and usually for an extended cruise - an adventure underwritten by a frisson of risk. Much of the chatter about artificial intelligence has the patina of blue-water thinking about it. Its promise is expansive - as broad as an ocean. There are unplumbed depths, certainly treasure and possibly danger. At the risk of overworking the metaphor, today Artificial Intelligence (AI) sits in the matrix of a perfect storm of coincidence.

It's almost a given that you'll ride in an autonomous car at some point in your life, and when you do, the AI controlling it just might have honed its skills playing Minecraft. It sounds crazy, but open-world games like Minecraft are a fantastic tool for teaching learning algorithms--which power the next generation of advanced artificial intelligence--how to understand and navigate three-dimensional spaces. Achieving that is a major stepping stone toward creating AI that can interact with the real world in complex ways. It's easy to consider videogames mindless escapism, but because they generate such vast amounts of information--think of the expansive world players create in Minecraft--they are exceptionally well suited to teaching an AI how to perceive the world and interact with it. "It's hard for a human to teach AI," says Xerox researcher Adrian Gaidon, because they are "worse than the worst toddlers in the world--you have to explain everything."

Alex Gianturco was a successful corporate lawyer based in Washington, D.C. Then, in 2011, he gave up his day job at Zuckerman Spaeder LLP, moved to Madison, Wisconsin, and focused all his efforts on his other passion: being a space pirate. Known as The Mittani within the virtual world of "Eve Online," Gianturco commands an army of 40,000 space pilots loyal to his Imperium coalition. He has a trusted band of lieutenants and uses propaganda, espionage and deception to retain his position as the game's most powerful player, describing himself as the Vladimir Putin of the "Eve" universe. He has even leveraged his position to earn a living from "Eve Online," setting up his own website and even renting out his army of mercenaries to other video games.

During the Deep Learning in Healthcare Summit in London last week we hosted the'Shaping Tomorrow' startup session to showcase innovative startups applying cutting-edge deep learning algorithms and tools to advance healthcare and medicine. Daria Danilina, an MBA student from London Business School, attended the event and kindly summarised the startup presentations. Key take-away: Humans are trained to identify certain patterns. However, we tend to overlook things which we do not expect to see or are not trained to detect. In addition to this, anomalies exist that are impossible to identify for human eyes, such as tumours composed of soft tissue.

Reinforcement learning is all about making robots adapt and learn about their environment on their own given only a simple reward function. It allows computers to learn how to excel at Atari and Pacman games and how to walk like we humans do. This article provides a well written implementation of Reinforcement Learning through Q learning, one of the most popular reinforcement learning methods in Python.

Recently I wrote about interface visibility -- the presence and degree of friction in interface design and human computer interaction -- and the idea that it exists distinctly from specific interface paradigms. Since then I've been itching to revisit and dig a little deeper into the nuts and bolts of conversational interaction design, specifically the way context plays a key role in the construction of meaning in user experiences that leverage natural language (especially those of the invisible variety). The space is still super early, but in my (admittedly brief) time designing bot interactions, I've noticed a few key patterns that have shaped my own mental model for constructing seamless conversational interactions. In conversational interfaces, who initiates each new conversational interaction -- and their intent -- is key to establishing the tone and expectations of everything that follows. When the bot initiates, it's significantly easier to establish the direction and flow of conversation, thereby increasing the likelihood of producing a better, and more focused experience.