Yup. I’m done with Facebook. However, it’s tough to delete your account. Read the message above. I exited out of this screen, suspended my account instead, but then went back 15 minutes later and actually deleted it. Well – I started the deletion process. I don’t know what day I’m on, but I think I’m close to 14 days. So, I’m still “deleting” apparently.
The only inconvenience I’ve noticed so far are all the sites where I used Facebook as the sign-on authenticator (rather than setting up a separate email/password combo.) I think I’m through most of that – at least the sites I use on a regular basis. For the first few days, I accidentally ended up on the Facebook login screen which was pleasantly filled out with my login beckoning me to log back in. I resisted the siren song of restarting my Facebook account before the 14
One of our themes is Protocol. We’ve been investing in companies built around technology protocols since 1994. One of my first investments, when I moved to Boulder in 1995, was in a company called Email Publishing, which was the very first email service provider. SMTP has been very good to me.
We made some of the early investments in companies built around RSS, including FeedBurner and NewsGator. RSS is a brilliant, and very durable, protocol. The original creators of the protocol had great vision, but the history and evolution of RSS were filled with challenges and controversy. Like religious conflict, the emotion ran higher than it needed to and the ad-hominem attacks drove some great people away from engaging with the community around the protocol.
And then Facebook and Twitter took over. RSS Feed Readers mostly vanished, and the feed became the “Twitter feed.” After a while, Facebook realized this
Modern data science emerged in tech, from optimizing Google search rankings and LinkedIn recommendations to influencing the headlines Buzzfeed editors run. But it’s poised to transform all sectors, from retail, telecommunications, and agriculture to health, trucking, and the penal system. Yet the terms “data science” and “data scientist” aren’t always easily understood, and are used to describe a wide range of data-related work.
What, exactly, is it that data scientists do? As the host of the DataCamp podcast DataFramed, I have had the pleasure of speaking with over 30 data scientists across a wide array of industries and academic disciplines. Among other things, I’ve asked them about what their jobs entail.
The big question around self-driving cars, for many people, is: When will the technology be ready? In other words, when will autonomous vehicles be safe enough to operate on their own? But there has been far less attention paid to two equally important questions: When will the driving environment be ready to accommodate self-driving cars? And where will this technology work best?
Self-driving cars are the most challenging automation project ever undertaken. Driving requires a great deal of processing and decision making, which must be automated. On top of that, there are many unpredictable external factors that must be accounted for, and therefore many ways in which the driving environment must change.
Many leading American digital firms, including Google, Amazon, eBay, and Uber, have successfully expanded internationally by introducing their products, services, and platforms in other countries. However, they have all failed in China, the world’s largest digital market.
The widely touted reasons for these failures include censorship by the Chinese government and cultural differences between China and the West. While these factors undoubtedly have played a role, such explanations are overly simplistic. Google, for example, has succeeded in dominating many foreign markets that have radically different political systems and cultures (including Indonesia, Thailand, and Saudi Arabia). And these factors have not stopped Western multinationals from succeeding in China in car manufacturing, fast-moving consumer goods, and even sectors where culture plays a key role, such as beer, coffee shops, fast food, and the film industry. There are deeper reasons behind the systematic failure of Western digital firms in
Recent advances in artificial intelligence (AI) and computer technology are causing us to think again about some really basic questions: what is a firm? What can firms do better than markets? And what are the distinctive qualities of firms in a world of smart contracts and AI?
While there has been a lot of discussion about “what’s left for humans?” as AI improves at exponential rates — the customary answer is that humans need to focus on the things they are uniquely good at, such as creativity, intuition, and personal empathy — I think we now have to ask, “what’s left for firms?”
In many ways this is an old question, because it takes us back to the arguments of Nobel Laureates Ronald Coase and Oliver Williamson that firms exist to coordinate complex forms of economic activity in an efficient way. If computer technology has
Kathryn Hume, VP of integrate.ai, discusses the current boundaries between artificially intelligent machines, and humans. While the power of A.I. can conjure up some of our darkest fears, she says the reality is that there is still a whole lot that A.I. can’t do. So far, A.I. is able to accomplish some tasks that humans might need a lot of training for, such as diagnosing cancer. But she says those tasks are actually more simple than we might think – and that algorithms still can’t replace emotional intelligence just yet. Plus, A.I. might just help us discover new business opportunities we didn’t know existed.
Knowing which organizations perform the best on any particular dimension used to require subjective surveys or painstaking research. Today, the data to answer those questions exists — it’s captured by the software-as-a-service firms whose services companies use to run their businesses. Mainstream software companies are beginning to hold “data mirrors” up to their customers, allowing scoring and benchmarking of their customers’ strategies. We’ve already seen that it’s possible to use external data to evaluate firms on what business models they are employing, and what those business models mean for their valuations. Those analyses rely on publicly available data sources, but software providers have accumulated growing amounts of private data on almost every aspect of their customers’ technology, operations, people, and strategies. It’s time that these data accumulators begin to share insights back to the creators of all this data, and several firms are beginning
Companies are cutting supply chain complexity and accelerating responsiveness using the tools of artificial intelligence. Through AI, machine learning, robotics, and advanced analytics, firms are augmenting knowledge-intensive areas such as supply chain planning, customer order management, and inventory tracking.
What does that mean for the supply chain workforce?
It does not mean human workers will become obsolete. In fact, a new book by Paul Daugherty and H. James Wilson debunks the widespread misconception that AI systems will replace humans in one industry after another. While AI will be deployed to manage certain tasks, including higher-level decision making, the technology’s true power is in augmenting human capabilities — and that holds true in the supply chain.
Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection — and even to predict and prevent crimes.
While today’s basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are. For instance, banks have been using transaction monitoring systems for decades based on pre-defined binary rules that require the output to be manually checked. The success rate is generally low: On average, only 2% of the transactions flagged by the systems ultimately reflect a true crime or malicious intent. By contrast, today’s machine-learning
It was going to be the factory of the future. Dubbed the “Alien Dreadnought,” Tesla’s new manufacturing facility in Fremont, California, was designed to be fully automated — no humans need apply. If all went well, AI-powered robots would enable the company to achieve a weekly production of 5,000 Model 3 electric cars to keep up with burgeoning demand. But Tesla fell far short of that mark, manufacturing just 2,000 vehicles a week. The problem, as the company painfully discovered, was that full automation wasn’t everything it was cracked up to be. According to CEO Elon Musk, the sophisticated robots actually slowed down production instead of speeding it up.
Tesla’s solution was to shut down production to address the bottlenecks and then to erect a large temporary structure — essentially a tent — for additional capacity. The company has also hired hundreds of workers to revamp production
Earlier this summer, my friend Michael told me about a small investment his team made up in Seattle in the Amazon ecosystem. We were about to move houses and with all the impending details that process was generating, I initially didn’t give it a proper look. As we were reviewing new deals, we flagged this one for being different in nature. And as we dug in more, we began to uncover how little we know about a new potential business line for Amazon.
Today, Downstream officially launched, though it is already in the market and helping Fortune 1000 companies get smarter about their spend on Amazon. You can read about their news today here in Geekwire, and I’m happy to be co-investing alongside friends like Michael, Micah, Dave, Chris, and others in Downstream’s seed round.
Marriott recently teamed up with Amazon to offer a hospitality version of the e-commerce giant’s Echo devices in select hotel rooms. Now, when guests want to order room service or housekeeping, they can simply ask Alexa, the voice of their disembodied personal concierge. Travelers with an Alexa device at home can book a car rental or hotel through Expedia and Kayak. Similarly, Google Assistant, which can be used via Google Home devices, smartphones, or smartwatches, can track flight prices and status, suggest nearby restaurants, convert currency, give directions, and provide same-day updates on traffic to airports. People can even book flights through voice-enabled Google Search.
On many fronts, artificial intelligence-powered smart speakers and apps seem poised to become the world’s virtual travel agents. Virtual personal assistants like Alexa are moving rapidly from nifty gadgets for techies to household appliances and mobile devices ingrained in everyday life.
In early June, at the invitation of the European Commission to Brussels (Belgium), I toured some fascinating AI and blockchain-based projects, which the Commission is funding. Across industrial sectors, from healthcare to energy, from construction to retail, engineers are creating new technologies with potentially disruptive implications for the current architectural order of the global economy. One of the technologies, an “AI doctor”, shows great promise for the future of healthcare in Africa.
The solution is called CareAi: an AI-powered computing system anchored on blockchain that can diagnose infectious diseases, such as malaria, typhoid fever, and tuberculosis, within seconds. The platform is engineered to serve the invisible demographic of migrants, ethnic minorities, and those unregistered within traditional healthcare systems. By bringing AI and blockchain together, CareAi uses an anonymous distributed healthcare architecture to deliver health services to patients anonymously. This makes it possible for these
The big news this morning is that Intercontinental Exchange, parent of the New York Stock Exchange, announced the formation of a new company, Bakkt, a global platform to allow consumers and institutions to buy, hold and store digital assets. Its first use cases will be for trading and conversion of Bitcoin versus fiat currencies. Partners include Microsoft, Starbucks and BCG:
“ ‘In bringing regulated, connected infrastructure together with institutional and consumer applications for digital assets, we aim to build confidence in the asset class on a global scale, consistent with our track record of bringing transparency and trust to previously unregulated markets,’ said Jeffrey C. Sprecher, Founder, Chairman and CEO of Intercontinental Exchange.
As an initial component of the Bakkt offering, Intercontinental Exchange’s U.S.-based futures exchange and clearing house plan to launch a 1-day physically delivered Bitcoin contract along with physical warehousing in November 2018, subject to CFTC Continue reading "ICE Heats up the Sector"
Hardly a day goes by without the announcement of an incredible new frontier in Artificial Intelligence (AI). From fintech to edtech, what was once fantastically improbable is now a commercial reality. There is no question that big data and AI will bring about important advances in the realm of management, especially as it relates to being able to make better-informed decisions. But certain types of decisions — particularly those related to strategy, innovation and marketing — will likely continue to require a human being who can take a holistic view and make a qualitative judgment based on a personal consideration of the context and facts. In fact, to date, there is no AI technology that is fully able to factor in the emotional, human, and political context needed to automate decisions.
For example, consider the healthcare industry, where AI is having a huge impact. Even if AI
On July 25, 2018, Facebook lost market capitalization of more than $100 billion in just two hours of trading after it announced its quarterly performance, despite exceeding analysts’ earnings forecasts. What caused this slump? It failed to meet its revenue and subscriber growth targets. This example illustrates that investors consider information beyond just earnings as value-relevant. In a recent HBR article, we claimed that modern digital companies such as Uber, Facebook, and Alphabet play an increasingly important role in the economy, but their financial statements fail to capture company’s main value drivers. In a follow up HBR article, we interviewed several chief financial officers (CFOs) of leading technology companies and senior analysts of investment banks and distilled seven key insights from those discussions. Based on these insights, we now propose a new blueprint for financial reporting of digital companies.
Articles about artificial intelligence often begin with an intention to shock readers, referencing classic works of science fiction or alarming statistics about impending job losses. But I think we get closer to the heart of AI in 2018 when we think about small and mundane ways in which AI makes work just a little easier. And it’s not necessarily the AI experts in your organization who will identify these mundane problems that AI can help solve. Instead, employees throughout the organization will be able to spot the low-hanging fruit where AI could make your organization more efficient. But, only if they know what AI is capable of doing, and what it should never do.
For example, I manage the finances for a team that travels very often, and I’ve been grateful for the intelligent guesswork that my expenses software extracts from receipts using machine learning: the merchant’s
Machine learning is increasingly being used to predict individuals’ attitudes, behaviors, and preferences across an array of applications — from personalized marketing to precision medicine. Unsurprisingly, given the speed of change and ever-increasing complexity, there have been several recent high-profile examples of “machine learning gone wrong.”
In January of 2018, Annette Zimmermann, vice president of research at Gartner, proclaimed: “By 2022, your personal device will know more about your emotional state than your own family.” Just two months later, a landmark study from the University of Ohio claimed that their algorithm was now better at detecting emotions than people are.