“The future is in disorder. A door like this has cracked open five or six times since we got up on our hind legs. It is the best possible time to be alive, when almost everything you thought you knew is wrong.” — Tom Stoppard
The media environment is like a crystal ball. By observing it, we can predict the future. Commerce will become quirkier, education will be overhauled, and politicians will increasingly look like anti-establishment celebrities.
In part one of this two-part series, we reviewed HBO’s present day dominance, as well as the reasons why the company will need to change and grow if it’s to extend its reign through the next decade. Here, we detail exactly which changes the company needs to make, why they won’t require the network to alter its identity, and how the company was addressing these changes long before it was acquired by AT&T.
By Matthew Ball at Redef
The creation of a successful status game is so mysterious that it often smacks of alchemy. For that reason, entrepreneurs who succeed in this space are thought of us a sort of shaman, perhaps because most investors are middle-aged white men who are already so high status they haven't the first idea why people would seek virtual status (more on that later).
Almost every social network of note had an early signature proof of work hurdle. For Facebook it was posting some witty text-based status update. For Instagram, it was posting an interesting square photo. For Vine, an entertaining 6-second video. For Twitter, it was writing an amusing bit of text of 140 characters or fewer. Pinterest? Pinning a compelling photo. You can likely derive the proof of work for other networks like Quora and Reddit and Twitch and so on. Successful social networks don't pose trick questions at the start, it’s usually clear what they want from you.
So, to answer an earlier question about how a new social network takes hold, let’s add this: a new Status as a Service business must devise some proof of work that depends on some actual skill to differentiate among users. If it does, then it creates, like an ICO, some new form of social capital currency of value to those users.
Because I teach a course on product management at Harvard Business School, I am routinely asked “What is the role of a product manager?” The role of product manager (PM) is often referred to as the “CEO of the product.” I disagree because, as Martin Eriksson points out, “Product managers simply don’t have any direct authority over most of the things needed to make their products successful — from user and data research through design and development to marketing, sales, and support.” PMs are not the CEO of product, and their roles vary widely depending on a number of factors. So, what should you consider if you’re thinking of pursuing a PM role?
Aspiring PMs should consider three primary factors when evaluating a role: core competencies, emotional intelligence (EQ), and company fit. The best PMs I have worked with have mastered the core competencies, have a high EQ, and work for the right company for them. Beyond shipping new features on a regular cadence and keeping the peace between engineering and the design team, the best PMs create products with strong user adoption that have exponential revenue growth and perhaps even disrupt an industry.
Prof Galloway goes into his reasons why Snap and Tesla are reaching the end of the road as independent companies.
The End of Snap and Tesla
Snapchat and Tesla were sold this week. They just don’t know it yet.
When I ask new entrepreneurs what their distribution model will be, I often get answers like: “I don’t want to hire any of those Rolex-wearing, BMW-driving, overly aggressive enterprise sales slimeballs, so we are going to distribute our product like Dropbox did.” In addition to taking stereotyping to a whole new level, this answer demonstrates a deep misunderstanding of how sales channels should be designed.
Two of the more famous military sayings are “Generals are always preparing to fight the last war”, and “Never interrupt your enemy while he is making a mistake.” I thought of the latter at the conclusion of last Sunday’s 60 Minutes report on Google:
For me, in strategic planning, the question in building my forecast was to flush out what I call the invisible asymptote: a ceiling that our growth curve would bump its head against if we continued down our current path. It's an important concept to understand for many people in a company, whether a CEO, a product person, or, as I was back then, a planner in finance.
Read More: http://www.eugenewei.com/blog/2018/5/21/invisible-asymptotes
You have probably noticed a flood of emails and alerts from companies in the last few weeks informing you about changes to their privacy policies.
Don’t ignore them.
Read More: https://www.nytimes.com/2018/05/23/technology/personaltech/what-you-should-look-for-europe-data-law.html
How artificial intelligence is being used to capture, interpret and respond to human emotions and moods.
This article has been updated from the original, published on June 17, 2017, to reflect new events and conditions and/or updated research.
“By 2022, your personal device will know more about your emotional state than your own family,” says Annette Zimmermann, research vice president at Gartner. This assertion might seem far-fetched to some. But the products showcased at CES 2018 demonstrate that emotional artificial intelligence (emotion AI) can make this prediction a reality.
Backstage Capital, a venture fund that invests in underrepresented founders — “women, People of Color, and LGBT founders,” according to the firm’s website — announced a new $36 million fund on Saturday that will invest exclusively in black female founders.
“They’re calling it a ‘diversity fund,’” tweeted Arlan Hamilton, the firm’s founder and managing partner. “I’m calling it an IT’S ABOUT DAMN TIME fund.”
CB Insights has a new article that highlights the biggest product failures of all time. There are a few lessons from this post:
- Some of the failures are with companies that had a core product ie Google with ads that wanted to innovate and focus on building new products. It is not clear if these companies used the lessons from the launch of some these products as an opportunity to iterate for the future.
- Some of the product failures noted here are with established consumer goods companies who focused on a new product were created for marketing purposes. Those product failures should be differentiated from the products outlined in point one.
From the DeLorean and New Coke to the Newton and Google Glass, here's a list of the biggest product flops from corporate giants.
Computer scientists have created a deep-learning, software-coding application that can help human programmers navigate the growing multitude of often-undocumented application programming interfaces, or APIs.
Designing applications that can program computers is a long-sought grail of the branch of computer science called artificial intelligence (AI). The new application, called Bayou, came out of an initiative aimed at extracting knowledge from online source code repositories like GitHub. Users can try it out at askbayou.com.
We survey 885 institutional venture capitalists (VCs) at 681 firms to learn how they make decisions across eight areas: deal sourcing; investment selection; valuation; deal structure; postinvestment value-added; exits; internal firm organization; and relationships with limited partners. In selecting investments, VCs see the management team as more important than business related characteristics such as product or technology. They also attribute more of the likelihood of ultimate investment success or failure to the team than to the business. While deal sourcing, deal selection, and post-investment value-added all contribute to value creation, the VCs rate deal selection as the most important of the three. We also explore (and find) differences in practices across industry, stage, geography and past success. W
Matt Turck has a fascinating article on Artificial Intelligence with insights on the progress that has been made, and where we are right now. I found it a good read as it touched on various models being used to lead to "general" artificial intelligence. It seems in the last year a lot of progress has been made in the hardware front that has led to gains but we may still be far off from the robots taking over.
GANs, or “generative adversarial networks” is a much more recent method, directly related to unsupervised deep learning, pioneered by Ian Goodfellow in 2014, then a PhD student at University of Montreal. GANs work by creating a rivalry between two neural nets, trained on the same data. One network (the generator) creates outputs (like photos) that are as realistic as possible; the other network (the discriminator) compares the photos against the data set it was trained on and tries to determine whether whether each photo is real or fake; the first network then adjusts its parameters for creating new images, and so and so forth. GANs have had their own evolution, with multiple versions of GAN appearing just in 2017 (WGAN, BEGAN, CycleGan, Progressive GAN).
How close is AlphaZero from AGI? Demis Hassabis, the CEO of DeepMind, called AlphaZero’s playstyle “alien”, because it would sometime win with completely counterintuitive moves like sacrifices. Seeing a computer program teach itself the most complex human games to a world-class level in a mere few hours is an unnerving experience that would appear close to a form of intelligence. One key counter-argument in the AI community is that AlphaZero is an impressive exercise in brute force: AlphaZero was trained via self-play using 5,000 first generation TPUs and 64 second generation TPUs; once trained it ran on a single machine with 4 TPUs. In reinforcement learning, AI researchers point out that the AI has no idea what it is actually doing (like playing a game) and is limited to the specific constraints that it was given (the rules of the game). Here is an interesting blog post disputing whether AlphaZero is a true scientific breakthrough
Transfer learning has been mostly challenging to make work – it works well when the tasks are closely related, but becomes much more complex beyond that. But this is a key area of focus for AI research. DeepMind made significant progress with its PathNet project (see a good overview here)