Bullish Forecast about the Potential of AI:
About 80% of the work involved in 80% of jobs across the economy can be automated over time, according to Khosla. “So 64% of all jobs can be done by an AI,” Kholsa said.
He views the next 10 years as a transition period in which the world’s political and social structures won’t seem all that different. AI will be seen as a boost for efficiency and productivity. After that, as the mid stages of AI-driven automation take a toll on more than 25% of today’s jobs, governments will need to provide much broader and deeper social services. But there will be enough economic abundance to support it.
Full Article at WSJ.com
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Jan 26, 2023 WSJ: BuzzFeed to Use ChatGPT Creator OpenAI to Help Create Quizzes and Other Content
Jan 17, 2023 Wharton School: Would Chat GPT Get a Wharton MBA? New White Paper By Christian Terwiesch
Jan 16, 2023 NYT: Alarmed by A.I. Chatbots, Universities Start Revamping How They Teach
]]>Oct 26: X’s Tumultuous First Year Under Elon Musk, in Charts
Sep 11: WSJ Times review of Isaacson's biography of Musk
Sep 8: NY Times review of Isaacson's biography of Musk. Key Takeaways from Book. Interview with Isaacson
Sep 8: Inside Musk’s Twitter Transformation: Impulsive Decisions, Favors for Friends
Feb 10: Elon Musk fires a top Twitter engineer over his declining view count
Jan 17, 2023: Extremely Hard Core.
Dec 31: What’s Gone at Twitter? A Data Center, Janitors, Some Toilet Paper.
Dec 24: Twitter brings Elon Musk’s genius reputation crashing down to earth
Dec 23: Journalists who won’t delete Musk tweets remain locked out of Twitter
Dec 21: Is This the End of Elon Musk’s Twitter Odyssey?
Dec 21: Elon Musk Says Twitter ‘Will, In Fact, Be OK Next Year’
Dec 21: Elon Musk’s Distraction Is Just One of Tesla’s Problems
Dec 21: Elon Musk Says He Will Resign as Twitter C.E.O. When He Finds Successor
Dec 20: How Elon Musk destroyed Twitter... and how to save
Dec 20: Musk's management troubles at Twitter hurt the brand image of Tesla
Dec 20: Why Petulant Oligarchs Rule Our World
Dec 20: Elon Musk Keeps Silent After Twitter Users Say He Should Quit as Boss
Dec 18: A guide to getting started with Twitter alternative Mastodon
Dec 17: In Suspending Journalists on Twitter, Musk Flexes His Media Muscle
Dec 17: Elon Musk Seeks Additional Funds for Twitter
Dec 14: Elon Musk's Twitter isn't paying its bills
Dec 14: Musk Shakes Up Twitter’s Legal Team as He Looks to Cut More Costs
Dec 12: Twitter dissolves Trust and Safety Council
Dec 12: Dave Chappelle brings Elon Musk onstage at comedy show and boos abound
Dec 12: The real reason Elon Musk bought Twitter (wants to create a payment system)
Dec 08: Elon Musk Slashes Bureaucracy, Giving Twitter a Chance to Soar
Nov 29: Elon Musk Takes On Apple’s Power, Setting Up a Clash
Nov 24: Video: Elon Musk Says Twitter Bankruptcy Is a Possibility. Here’s Why
Nov 21: What Elon Musk Is Doing to Twitter Is What He Did at Tesla and SpaceX
Source: CB Insights March 7, 2019 Newsletter
]]>Warning: this video is only for adults. Language not appropriate for children.
]]>So far, the hotel has culled over half of its 243 robots, many because they created work rather than reduced it.
[...]
Mr. Sawada said he hasn’t given up on the idea of a hotel without human staff, but Strange Hotel has taught him that there are currently many jobs suited only for humans. “When you actually use robots you realize there are places where they aren’t needed—or just annoy people,” he said.
This is a great article by Rodney Brooks, who is a former director of the Computer Science and Artificial Intelligence Laboratory at MIT and a founder of Rethink Robotics and iRobot.
Excerpt: Similarly, we have seen a sudden increase in performance of AI systems thanks to the success of deep learning. Many people seem to think that means we will continue to see AI performance increase by equal multiples on a regular basis. But the deep-learning success was 30 years in the making, and it was an isolated event.
That does not mean there will not be more isolated events, where work from the backwaters of AI research suddenly fuels a rapid-step increase in the performance of many AI applications. But there is no “law” that says how often they will happen.
The plot for many Hollywood science fiction movies is that the world is just as it is today, except for one new twist.
In Bicentennial Man, Richard Martin, played by Sam Neill, sits down to breakfast and is waited upon by a walking, talking humanoid robot, played by Robin Williams. Richard picks up a newspaper to read over breakfast. A newspaper! Printed on paper. Not a tablet computer, not a podcast coming from an Amazon Echo–like device, not a direct neural connection to the Internet.
It turns out that many AI researchers and AI pundits, especially those pessimists who indulge in predictions about AI getting out of control and killing people, are similarly imagination-challenged. They ignore the fact that if we are able to eventually build such smart devices, the world will have changed significantly by then. We will not suddenly be surprised by the existence of such super-intelligences. They will evolve technologically over time, and our world will come to be populated by many other intelligences, and we will have lots of experience already. Long before there are evil super-intelligences that want to get rid of us, there will be somewhat less intelligent, less belligerent machines. Before that, there will be really grumpy machines. Before that, quite annoying machines. And before them, arrogant, unpleasant machines. We will change our world along the way, adjusting both the environment for new technologies and the new technologies themselves. I am not saying there may not be challenges. I am saying that they will not be sudden and unexpected, as many people think.
New versions of software are deployed very frequently in some industries. New features for platforms like Facebook are deployed almost hourly. For many new features, as long as they have passed integration testing, there is very little economic downside if a problem shows up in the field and the version needs to be pulled back. This is a tempo that Silicon Valley and Web software developers have gotten used to. It works because the marginal cost of newly deploying code is very, very close to zero.
Deploying new hardware, on the other hand, has significant marginal costs. We know that from our own lives. Many of the cars we are buying today, which are not self-driving, and mostly are not software-enabled, will probably still be on the road in the year 2040. This puts an inherent limit on how soon all our cars will be self-driving. If we build a new home today, we can expect that it might be around for over 100 years. The building I live in was built in 1904, and it is not nearly the oldest in my neighborhood.
Capital costs keep physical hardware around for a long time, even when there are high-tech aspects to it, and even when it has an existential mission.
The U.S. Air Force still flies the B-52H variant of the B-52 bomber. This version was introduced in 1961, making it 56 years old. The last one was built in 1962, a mere 55 years ago. Currently these planes are expected to keep flying until at least 2040, and perhaps longer — there is talk of extending their life to 100 years.
I regularly see decades-old equipment in factories around the world. I even see PCs running Windows 3.0 — a software version released in 1990. The thinking is “If it ain’t broke, don’t fix it.” Those PCs and their software have been running the same application doing the same task reliably for over two decades.
The principal control mechanism in factories, including brand-new ones in the U.S., Europe, Japan, Korea, and China, is based on programmable logic controllers, or PLCs. These were introduced in 1968 to replace electromechanical relays. The “coil” is still the principal abstraction unit used today, and PLCs are programmed as though they were a network of 24-volt electromechanical relays. Still. Some of the direct wires have been replaced by Ethernet cables. But they are not part of an open network. Instead they are individual cables, run point to point, physically embodying the control flow — the order in which steps get executed — in these brand-new ancient automation controllers. When you want to change information flow, or control flow, in most factories around the world, it takes weeks of consultants figuring out what is there, designing new reconfigurations, and then teams of tradespeople to rewire and reconfigure hardware. One of the major manufacturers of this equipment recently told me that they aim for three software upgrades every 20 years.
In principle, it could be done differently. In practice, it is not. I just looked on a jobs list, and even today, this very day, Tesla Motors is trying to hire PLC technicians at its factory in Fremont, California. They will use electromagnetic relay emulation in the production of the most AI-enhanced automobile that exists.
A lot of AI researchers and pundits imagine that the world is already digital, and that simply introducing new AI systems will immediately trickle down to operational changes in the field, in the supply chain, on the factory floor, in the design of products.
Nothing could be further from the truth. Almost all innovations in robotics and AI take far, far, longer to be really widely deployed than people in the field and outside the field imagine.
Alert: The link below is an advertisement by Accenture. It was co-produced by the WSJ (Dow Jones) advertisement department.
The only reason why we listing it here because it is interesting to see how consulting firms are trying to get into the game of advising companies on they should respond to the rise of machine learning.
Accenture ad on WJS.com produced by "WSJ Studios"
The Human Promise of the AI Revolution
Here are a couple of exerpts:
"If handled with care and foresight, this AI crisis could present an opportunity for us to redirect our energy as a society to more human pursuits: to taking care of each other and our communities. To have any chance of forging that future, we must first understand the economic gauntlet that we are about to pass through.
Many techno-optimists and historians would argue that productivity gains from new technology almost always produce benefits throughout the economy, creating more jobs and prosperity than before. But not all inventions are created equal. Some changes replace one kind of labor (the calculator), and some disrupt a whole industry (the cotton gin). Then there are technological changes on a grander scale. These don’t merely affect one task or one industry but drive changes across hundreds of them. In the past three centuries, we’ve only really seen three such inventions: the steam engine, electrification and information technology.
[...]
AI’s main advantage over humans lies in its ability to detect incredibly subtle patterns within large quantities of data and to learn from them. While a human mortgage officer will look at only a few relatively crude measures when deciding whether to grant you a loan (your credit score, income and age), an AI algorithm will learn from thousands of lesser variables (what web browser you use, how often you buy groceries, etc.). Taken alone, the predictive power of each of these is minuscule, but added together, they yield a far more accurate prediction than the most discerning people are capable of.
For cognitive tasks, this ability to learn means that computers are no longer limited to simply carrying out a rote set of instructions written by humans. Instead, they can continuously learn from new data and perform better than their human programmers. For physical tasks, robots are no longer limited to repeating one set of actions (automation) but instead can chart new paths based on the visual and sensor data they take in (autonomy).
Together, this allows AI to take over countless tasks across society: driving a car, diagnosing a disease or providing customer support. AI’s superhuman performance of these tasks will lead to massive increases in productivity. According to a June 2017 study by the consulting firm PwC, AI’s advance will generate $15.7 trillion in additional wealth for the world by 2030. This is great news for those with access to large amounts of capital and data. It’s very bad news for anyone who earns their living doing soon-to-be-replaced jobs."
This is a very interesting report by the WSJ.
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Source: CB Insights
]]>WSJ reports: "Oncology won’t be “a great space for making [AI] products” until there’s better data about patients, spanning genetic, environmental, lifestyle and health information, said Bob Kocher, a medical doctor and partner at venture-capital firm Venrock in Palo Alto, Calif. In the near term, most of the benefits from AI in the health-care field will come in administrative tasks such as billing, he added.
Judea Pearl on the future of AI:
"The key, he argues, is to replace reasoning by association with causal reasoning. Instead of the mere ability to correlate fever and malaria, machines need the capacity to reason that malaria causes fever. Once this kind of causal framework is in place, it becomes possible for machines to ask counterfactual questions — to inquire how the causal relationships would change given some kind of intervention — which Pearl views as the cornerstone of scientific thought. Pearl also proposes a formal language in which to make this kind of thinking possible — a 21st-century version of the Bayesian framework that allowed machines to think probabilistically..."
Here we will post AI books that we find worthy of attention.
This is the excerpt a McKinsey Quarterly (April 2018) interview with Microsoft CEO Satya Nadella.
Simon London (Mckinsey Quarterly): Can we pivot and talk a little about AI? What advice do you give to executives that you talk to about how to leverage AI in their businesses?
Satya Nadella (CEO Microsoft): I believe AI is one of the more defining technologies of our time. One of the things I am most excited about is AI technology helping with inclusivity. For example, in the latest release of Windows, we have something called Eye Gaze, which allows anybody who is suffering from ALS [amyotrophic lateral sclerosis] to be able to type just with their gaze. We have learning tools inside of Word and OneNote that allow anyone with dyslexia to improve their reading. It’s powerful stuff, and it’s a very practical way for executives to deploy some of these tools so that more people in their workforces can fully participate, which is important.
But there is no question that automation and the efficiencies of automation are tremendously important. For example, if you go to support.microsoft.com, it’s a bot. It uses some of the latest techniques of reinforcement learning to answer questions that customers may have. And of course, if it runs out of gas, it turns over to the customer-service representative, who is also using the bot to help answer the question.
So, we have the full gamut of technology that is getting deployed. We now really have human-level speech recognition. In January, there was a contest at Stanford University for machine reading and comprehension. Microsoft was number one. This means a machine can read a piece of text and start answering questions, like a reading-comprehension test, without necessarily being fed the answers that are indexed in the text.
The advances are enormous, and they will lead to productivity gains broadly. Therefore, every CEO—every executive—should be thinking about how to get more analytical power or predictive power inside his or her business process or organization. That’s ultimately what’s needed to translate AI capability into productivity.
Simon London: What types of IT configuration and IT capabilities do companies need to do this?
Satya Nadella: I would say there are two big considerations. One of the fundamental things is that there’s no way to create AI if you don’t have data. If the data inside your organization is siloed, it’s going to be a challenge to create AI. This goes back to your point around company configuration.
Take customer connection as an example. In order to be much better at omnichannel customer connection—and it doesn’t matter whether you’re a retailer, a CPG [consumer-packaged-goods] company, or a bank—everything from the log data from your website, to your mobile analytics, to your CRM [customer-relationship-management] system, to all the other data streams, it all has to come together in order to create the next best touchpoint action with the customer. This is both an AI problem and a data problem. One of the things that we like to stress is: how can we help our customers first get their data estate in many cases into the cloud? Then they can reason on top of it and create these transformative outcomes, whether it is connecting with customers, or operational efficiencies, or even changing the nature of their products. This is a super important thing.
I would also add that trust is going to be of paramount importance. Not just the security side of trust but also the trust of the business model. You need to pick partners who are going to help you with your capability building, whose interests are aligned with your interests in the long term.
Simon London: If you’re a senior executive at a big industrial company, for example, there are a lot of different potential use cases for AI. Do you have any generalizable advice about how to look across those use cases and what to go for first?
Satya Nadella: When I pattern match and look at some of the best and easy-to-get-started use cases, it would be anything related to customer experience. This is a good use case. Let’s say there’s omnichannel customer data. The ability to do the next best action, whether it be a sales force, or inside sales, or your website personalization, can come in a variety of different ways.
Connecting with your customers more deeply—using your data and your ability to reason over data—using the latest AI techniques is one use case. The second use case is supply-chain or operational efficiencies. The IoT [the Internet of Things] is a fascinating thing. If you think about it, most of these projects are where you have a good or a service, you’re collecting operational data from it, you’re doing preventive maintenance, and then you’re going to connect it to field service, because once you can predict something, you want to connect it to somebody coming and fixing it before it’s broken. That’s a thing that can drive both top-line and bottom-line efficiencies. That’s a great use case, and we see a lot of it, especially in industrial companies. We also see a lot of deployment of technology to empower people inside the organization. I’m fascinated to see how HoloLens is being used for doing oil-field inspections or training. So, AI can be deployed not just against traditional knowledge work but also in what I will call frontline work.
Sometimes organizations have this “cobbler’s children” problem. They talk about all these great things they’ll do for customers, for [business] partners. Except you also need to do fantastic things for your employees so that they can do all these great things for customers and partners.
Full article entitled "Microsoft's next act"
Just debated @TheEconomist #Innovation Summit on the job impact of AI. Argument: AI will destroy more jobs than create in the next 7 years 1/
— Aija Leiponen (@AijaLeiponen) March 23, 2018