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April 21, 2019
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Artificial Intelligence

Startups Weekly: Zoom CEO says its stock price is ‘too high’

in Adam Fisher/alex wilhelm/Artificial Intelligence/ATG/Bessemer Venture Partners/ceo/chief executive officer/Co-founder/Delhi/Economy/editor-in-chief/Entrepreneurship/Eric Yuan/fastly/Finance/India/jonathan shieber/laser/lucas matney/marc raibert/online pharmacy/photo sharing/Pinterest/Politics/Private equity/russ heddleston/SoftBank/SoftBank Group/Startup company/Startups/Toyota/Uber/Venture Capital/video conferencing/world wide web/Zencargo by

When Zoom hit the public markets Thursday, its IPO pop, a whopping 81 percent, floored everyone, including its own chief executive officer, Eric Yuan.

Yuan became a billionaire this week when his video conferencing business went public. He told Bloomberg that he actually wished his stock hadn’t soared quite so high. I’m guessing his modesty and laser focus attracted Wall Street to his stock; well, that, and the fact that his business is actually profitable. He is, this week proved, not your average tech CEO.

I chatted with him briefly on listing day. Here’s what he had to say.

“I think the future is so bright and the stock price will follow our execution. Our philosophy remains the same even now that we’ve become a public company. The philosophy, first of all, is you have to focus on execution, but how do you do that? For me as a CEO, my number one role is to make sure Zoom customers are happy. Our market is growing and if our customers are happy they are going to pay for our service. I don’t think anything will change after the IPO. We will probably have a much better brand because we are a public company now, it’s a new milestone.”

“The dream is coming true,” he added. 

For the most part, it sounded like Yuan just wants to get back to work.

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IPO corner

You thought I was done with IPO talk? No, definitely not:

  • Pinterest completed its IPO this week too! Here’s the TLDR: Pinterest popped 25 percent on its debut Thursday and is currently trading up 28 percent. Not bad, Pinterest, not bad.
  • Fastly, a startup I’d admittedly never heard of until this week, filed its S-1 and displayed a nice path to profitability. That means the parade of tech IPOs is far from over.
  • Uber… Surprisingly, no Uber IPO news this week. Sit tight, more is surely coming.

$1B for self-driving cars

While I’m on the subject of Uber, the company’s autonomous vehicles unit did, in fact, raise $1 billion, a piece of news that had been previously reported but was confirmed this week. With funding from Toyota, Denso and SoftBank’s Vision Fund, Uber will spin-out its self-driving car unit, called Uber’s Advanced Technologies Group. The deal values ATG at $7.25 billion.

Robots!

The TechCrunch staff traveled to Berkeley this week for a day-long conference on robotics and artificial intelligence. The highlight? Boston Dynamics CEO Marc Raibert debuted the production version of their buzzworthy electric robot. As we noted last year, the company plans to produce around 100 models of the robot in 2019. Raibert said the company is aiming to start production in July or August. There are robots coming off the assembly line now, but they are betas being used for testing, and the company is still doing redesigns. Pricing details will be announced this summer.

Digital health investment is down

Despite notable rounds for digital health businesses like Ro, known for its direct-to-consumer erectile dysfunction medications, investment in the digital health space is actually down, reports TechCrunch’s Jonathan Shieber. Venture investors, private equity and corporations funneled $2 billion into digital health startups in the first quarter of 2019, down 19 percent from the nearly $2.5 billion invested a year ago. There were also 38 fewer deals done in the first quarter this year than last year, when investors backed 187 early-stage digital health companies, according to data from Mercom Capital Group.

Startup capital

Byton loses co-founder and former CEO, reported $500M Series C to close this summer
Lyric raises $160M from VCs, Airbnb
Brex, the credit card for startups, raises $100M debt round
Ro, a D2C online pharmacy, reaches $500M valuation
Logistics startup Zencargo gets $20M to take on the business of freight forwarding
Co-Star raises $5M to bring its astrology app to Android
Y Combinator grad Fuzzbuzz lands $2.7M seed round to deliver fuzzing as a service

Extra Crunch

Hundreds of billions of dollars in venture capital went into tech startups last year, topping off huge growth this decade. VCs are reviewing more pitch decks than ever, as more people build companies and try to get a slice of the funding opportunities. So how do you do that in such a competitive landscape? Storytelling. Read contributor’s Russ Heddleston’s latest for Extra Crunch: Data tells us that investors love a good story.

Plus: The different playbook of D2C brands

And finally, for the first of a new series on VC-backed exits aptly called The Exit. TechCrunch’s Lucas Matney spoke to Bessemer Venture Partners’ Adam Fisher about Dynamic Yield’s $300M exit to McDonald’s.

#Equitypod

If you enjoy this newsletter, be sure to check out TechCrunch’s venture-focused podcast, Equity. In this week’s episode, available here, Crunchbase News editor-in-chief Alex Wilhelm and I chat about rounds for Brex, Ro and Kindbody, plus special guest Danny Crichton joined us to discuss the latest in the chip and sensor world.

News Source = techcrunch.com

Industrial robotics giant Fanuc is using AI to make automation even more automated

in Artificial Intelligence/Asia/bin-picking/Delhi/fanuc/India/industrial automation/Industrial Robotics/manufacturing/Politics/robotics/TC Sessions: Robotics + AI by

Industrial automation is already streamlining the manufacturing process, but first those machines must be painstakingly trained by skilled engineers. Industrial robotics giant Fanuc wants to make robots easier to train, therefore making automation more accessible to a wider range of industries, including pharmaceuticals. The company announced a new artificial intelligence-based tool at TechCrunch’s Robotics + AI Sessions event today that teaches robots how to pick the right objects out of a bin with simple annotations and sensor technology, reducing the training process by hours.

Bin-picking is exactly what it sounds like: a robot arm is trained to pick items out of bins and used for tedious, time-consuming tasks like sorting bulk orders of parts. Images of example parts are taken with a camera for the robot to match with vision sensors. Then the conventional process of training bin-picking robots means teaching it many rules so it knows what parts to pick up.

“Making these rules in the past meant having to through a lot of iterations and trial and error. It took time and was very cumbersome,” said Dr. Kiyonori Inaba, the head of Fanuc Corporation’s Robot Business Division, during a conversation ahead of the event.

These rules include details like how to locate the parts on the top of the pile or which ones are the most visible. Then after that, human operators need to tell it when it makes an error in order to refine its training. In industries that are relatively new to automation, finding enough engineers and skilled human operators to train robots can be challenging.

This is where Fanuc’s new AI-based tool comes in. It simplifies the training process so the human operator just needs to look at a photo of parts jumbled in a bin on a screen and tap a few examples of what needs to be picked up, like showing a small child how to sort toys. This is significantly less training than what typical AI-based vision sensors need and can also be used to train several robots at once.

“It is really difficult for the human operator to show the robot how to move in the same way the operator moves things,” said Inaba. “But by utilizing AI technology, the operator can teach the robot more intuitively than conventional methods.” He adds that the technology is still in its early stages and it remains to be seen if it can be used during in assembly as well.

News Source = techcrunch.com

Amazon’s one-two punch: How traditional retailers can fight back

in 6 River Systems/Amazon/Artificial Intelligence/Column/Delhi/e-commerce/eCommerce/getvu/IBM/India/jeff bezos/Kiva Systems/locus robotics/magazino/merchandising/online retail/online shopping/physical retail/Politics/retail/retailers/siemens/TC/whole foods by

If you think physical retail is dead, you couldn’t be more wrong. Despite the explosion in e-commerce, we’re still buying plenty of stuff in offline stores. In 2017, U.S. retail sales totaled $3.49 trillion, of which only 13 percent (about $435 billion) were e-commerce sales. True, e-commerce is growing at a much faster annual pace. But we’re still very far from the tipping point.

Amazon, the e-commerce giant, is playing an even longer game than everyone thinks. The company already dominates online retail — Amazon accounted for almost 50 percent of all U.S. e-commerce dollars spent in 2018. But now Amazon is eyeing the much bigger prize: modernizing and dominating retail sales in physical locations, mainly through the use of sophisticated data analysis. The recent reports of Amazon launching its own chain of grocery stores in several U.S. cities — separate from its recent Whole Foods acquisition — is just one example of how this could play out.

You can think of this as the Amazon one-two punch: The company’s vast power in e-commerce is only the initial, quick jab to an opponent’s face. Data-focused innovations in offline retail will be Amazon’s second, much heavier cross. Traditional retailers too focused on the jab aren’t seeing the cross coming. But we think canny retailers can fight back — and avoid getting KO’d. Here’s how.

The e-commerce jab starts with warehousing

Physical storage of goods has long been crucial to advances in commerce. Innovations here range from Henry Ford’s conveyor belt assembly line in 1910, to IBM’s universal product code (the “barcode”) in the early 1970s, to J.C. Penney’s implementation of the first warehouse management system in 1975. Intelligrated (Honeywell), Dematic (KION), Unitronics, Siemens and others further optimized and modernized the traditional warehouse. But then came Amazon.

After expanding from books to a multi-product offering, Amazon Prime launched in 2005. Then, the company’s operational focus turned to enabling scalable two-day shipping. With hundreds of millions of product SKUs, the challenge was how to get your pocket 3-layer suture pad (to cite a super-specific product Amazon now sells) from the back of the warehouse and into the shippers’ hands as quickly as possible.

Make no mistake: Amazon’s one-two retail punch will be formidable.

Amazon met this challenge at a time when automated warehouses still had massive physical footprints and capital-intensive costs. Amazon bought Kiva Systems in 2012, which ushered in the era of Autonomous Guided Vehicles (AGVs), or robots that quickly ferried products from the warehouse’s depths to static human packers.

Since the Kiva acquisition, retailers have scrambled to adopt technology to match Amazon’s warehouse efficiencies.  These technologies range from warehouse management software (made by LogFire, acquired by Oracle; other companies here include Fishbowl and Temando) to warehouse robotics (Locus Robotics, 6 River Systems, Magazino). Some of these companies’ technologies even incorporate wearables (e.g. ProGlove, GetVu) for warehouse workers. We’ve also seen more general-purpose projects in this area, such as Google Robotics. The main adopters of these new technologies are those companies that feel Amazon’s burn most harshly, namely operators of fulfillment centers serving e-commerce.

The schematic below gives a broad picture of their operations and a partial list of warehouse/inventory management technologies they can adopt:

It’s impossible to say what optimizations Amazon will bring to warehousing beyond these, but that may be less important to predict than retailers realize.

The cross: Modernizing the physical retail environment

Amazon has made several recent forays into offline shopping. These range from Amazon Books (physical book stores), Amazon Go (fast retail where consumers skip the cashier entirely) and Amazon 4-Star (stores featuring only products ranked four-stars or higher). Amazon Live is even bringing brick-and-mortar-style shopping streaming to your phone with a home-shopping concept à la QVC. Perhaps most prominently, Amazon’s 2017 purchase of Whole Foods gave the company an entrée into grocery shopping and a nationwide chain of physical stores.

Most retail-watchers have dismissed these projects as dabbling, or — in the case of Whole Foods — focused too narrowly on a particular vertical. But we think they’re missing Bezos’ longer-term strategic aim. Watch that cross: Amazon is mastering how physical retail works today, so it can do offline what it already does incredibly well online, which is harness data to help retailers sell much more intelligently. Amazon recognizes certain products lend themselves better to offline shopping — groceries and children’s clothing are just a few examples.

How can traditional retailers fight back? Get more proactive.

Those shopping experiences are unlikely to disappear. But traditional retailers (and Amazon offline) can understand much, much more about the data points between shopping and purchase. Which path did shoppers take through the store? Which products did they touch and which did they put into a cart? Which items did they try on, and which products did they abandon? Did they ask for different sizes? How does product location within the store influence consumers’ willingness to buy? What product correlations can inform timely marketing offers — for instance, if women often buy hats and sunglasses together in springtime, can a well-timed coupon prompt an additional purchase? Amazon already knows answers to most of these questions online. They want to bring that same intelligence to offline retail.

Obviously, customer privacy will be a crucial concern in this brave new future. But customers have come to expect online data-tracking and now often welcome the more informed recommendations and the convenience this data can bring. Why couldn’t a similar mindset-shift happen in offline retail?

How can retailers fight back?

Make no mistake: Amazon’s one-two retail punch will be formidable. But remember how important the element of surprise is. Too many venture capitalists underestimate physical retail’s importance and pooh-pooh startups focused on this sector. That’s extremely short-sighted.

Does the fact that Amazon is developing computer vision for Amazon Go mean that alternative self-checkout companies (e.g. Trigo, AiFi) are at a disadvantage? I’d argue that this validation is actually an accelerant as traditional retail struggles to keep up.

How can traditional retailers fight back? Get more proactive. Don’t wait for Amazon to show you what the next best-practice in retail should be. There’s plenty of exciting technology you can adopt today to beat Jeff Bezos to the punch. Take Relex, a Finnish startup using AI and machine learning to help brick-and-mortar and e-commerce companies make better forecasts of how products will sell. Or companies like Memomi or Mirow that are creating solutions for a more immersive and interactive offline shopping experience.

Amazon’s one-two punch strategy seems to be working. Traditional retailers are largely blinded by the behemoth’s warehousing innovations, just as they are about to be hit with an in-store innovation blow. New technologies are emerging to help traditional retail rally. The only question is whether they’ll implement the solutions fast enough to stay relevant.

News Source = techcrunch.com

The most overlooked path to commercialize AI is for companies to do it themselves

in Artificial Intelligence/Delhi/full stack development/India/Politics/TC by

Editor’s note: John Mannes is an investor at Basis Set Ventures, a $136 million early-stage venture capital fund focused on supporting startups using machine learning to address big problems across industries. Prior to Basis Set Ventures, John was a TechCrunch writer covering machine intelligence startups, machine learning research and major AI initiatives from big tech.

The Bessemer Process patented in 1856 by Sir Henry Bessemer is one of the inventions most closely associated with catalyzing the second industrial revolution. By reducing the impurities of iron with an innovative oxidizing air blast, the process ushered in a new wave of inexpensive, high-volume steel making.

Bessemer decided to license his patent to a handful of steel makers in an effort to quickly monetize his efforts. But contrary to expectations, technical challenges and monopolistic greed prevented large steel makers from agreeing to favorable licensing terms.

In an effort to drive adoption, Bessemer opened his own steel-making plant with the intention of undercutting competitors. The approach was so successful that each partner in the endeavor walked away from the 14-year partnership with an 81x return.

Some 162 years later, new businesses continue to struggle to convince customers to adopt new technologies — even when it’s in their best interest. Following in the footsteps of founders like Bessemer, today’s innovative startups are discovering that it often makes more sense to launch “full stack” businesses that provide a traditional service optimized with proprietary automation measures.

Chris Dixon of Andreessen Horowitz popularized the term “full-stack startup” in 2014, just before the deep-learning revolution. In his words, a full-stack startup is a company that “builds a complete, end-to-end product or service that bypasses existing companies.”

The full-stack methodology gave birth to companies like Uber and Tesla prior to the apex of the deep-learning revolution. And in today’s AI-first world of data and human labelers, full-stack startups are poised to play an even more important role in the startup ecosystem.

Going full stack comes with the advantage of being able to operate outside traditional incentive structures that limit the ability for large players in legacy industries to implement automation measures.

(Photo by Andrew Spear for The Washington Post via Getty Images.)

What does DIY AI look like?

Startups like Cognition IP, a BSV portfolio company, and Atrium are good examples of this. On paper, these businesses look very similar to traditional law firms in that they employ lawyers to practice patent law and startup law, respectively. But while traditional law firms often don’t automate due to the natural incentives associated with hourly billing, full-stack startups are incentivized by consumer adoption, so they have much to gain from developing a faster, cheaper, better strategy.

In addition to rejiggering old incentive structures à la Bessemer, going full stack opens up opportunities for companies to integrate labeling workflows into more traditional roles, to reap the full benefits of virtuous feedback loops, and to avoid countless complex process integrations.

Data labeling is a critical responsibility for startups that rely on machine learning. Services like Amazon Mechanical Turk and Figure Eight work well when startups have relatively manageable data-labeling responsibilities. But when labeling and human-plus-machine cooperative decision-making are a core part of everyday operations, startups often have to hire employees to manage that workflow internally.

Scaling these teams is expensive and operationally intensive. Going full stack opens up opportunities for companies to integrate labeling workflows into other jobs. Employees traditionally tasked with performing a consumer or enterprise service can take on the extra task at reduced expense. And if their role is assisted by a machine, they will gradually become more productive over time as their assistive models get more accurate with more labeled data.

A second and inherently related benefit of going full stack is that these startups are able to generate — and own — powerful virtuous data feedback loops. Owning data flows creates more impressive moats than merely locking down static data sets. Deep Sentinel has a natural moat in the consumer security space, for example, as it not only has accurate classifiers, but accurate classifiers that continue to improve with real-world data generated in an environment it can control.

Courtesy of Flickr/Tullio Saba

Leveraging automation is a matter of balancing risks and rewards

In 1951, Ford’s VP of Operations, Del Harder, decided it was time to upgrade the company’s lines with a more fully automated system for moving materials through the production sequence. It ultimately took five years of tinkering at Ford’s Cleveland Engine Plant before the technique was ready to scale to other factories. By chaining together previously independent parts of the production sequence, Harder had created new frustrating interdependencies.

Founders today going after traditional industries like manufacturing and agriculture similarly understand that the devil is in the details when it comes to scaling. The clear advantage to startups subscribing to the full stack methodology is that they only need to worry about integrating once with their own processes.

But on the flip side, going full stack does come with its own significant scaling expenses. Venture capital as a financing vehicle only makes sense to a certain point with respect to risk, margin and dilution, so many founders attempting to execute this strategic playbook have turned to debt financing.

Fortunately, we have been in good economic times with low interest rates. Traditional full-stack businesses like Tesla and Uber have both raised significant debt, and even up-and-coming players like Opendoor have turned to this financing strategy. A nasty economic downturn could certainly throw a wrench into things for just about everyone.

Progress in technology is cyclical and success is heavily dependent on execution within extremely narrow opportunistic bands of time. It’s debatable whether capital-intensive, venture-backed companies like FedEx and Apple could have been successful if they were started in a different fundraising environment.

Like countless other automation technologies that preceded machine learning, the winners of the deep-learning revolution will be startups whose technologies are optimized to work side-by-side with humans to generate outsized returns. Going full stack is difficult, expensive and not the only way to win, but it’s an under-appreciated strategy that’s extremely relevant for today’s machine learning-enabled startups.

News Source = techcrunch.com

Ten steps to prepare for an exponential future

in America/Artificial Intelligence/bank/China/Column/Delhi/Europe/fertility/fitbit/gps/India/machine translation/mri/Politics/Smart phones/United States by

If it feels like technological change is happening faster than it used to, that’s because it is.

It took around 12,000 years to move from the agrarian to the industrial revolution but only a couple of hundred years to go from the industrial to the information revolution that’s now propelling us in a short number of decades into the artificial intelligence revolution. Each technological transformation enables the next as the time between these quantum leaps becomes shorter.

That’s why if you are looking backwards to get a sense of how quickly the world around you will change, you won’t realize how quickly our radically different future is approaching. But although this can sometimes feel frightening, there’s a lot we can do now to help make sure we ride this wave of radical change rather than get drowned by it.

Here’s my essential list:

  1. Do what you can to preserve your youth
    Scientists are discovering new ways to slow the biological process of aging. It won’t be too long before doctors start prescribing pills, gene therapies, and other treatments to manage getting old as a partly curable disease. Because most of the terrible afflictions we now fear are correlated with age, medically treating aging will push off the date when we might have otherwise developed cancers, heart disease, dementia, and other killers. To maximally benefit from the new treatments for aging tomorrow, we all, no matter what our current age, need to do what we can to take care of our bodies today. That means exercising around 45 minutes a day, eating a healthy and mostly plant-based diet, trying to sleep at least seven hours a night, avoiding too much sun, not smoking, building and maintaining strong communities and support networks, and living a purposeful life. The healthier you are when the anti-age treatments arrive, the longer you’ll be able to maintain your vitality into your later years.
  2. Quantify and monitor your health
    You can’t monitor what you can’t measure. If you want to maintain optimal health, you need a way to regularly assess if you are on the right track. Monitoring your health through regular broad-spectrum blood and stool tests, constant feedback about your heart rate and sleep patterns from devices like your Apple Watch or Fitbit, having your genome sequenced, getting a full body MRI, and having a regular colonoscopy may seem like overkill to most people. But waiting until you have a symptom to start assessing your health status is like waiting until your car is careening down a hill to check if the brakes are in order. Some smart people worry that this kind of monitoring of “healthy” people will waste money, overwhelm our already overburdened healthcare system, and cause people unnecessary anxiety. But even the healthiest among us are in the early stages of developing one disease or another. Society will inevitably shift from a model of responsive sick care of people already in trouble to the predictive healthcare trying to keep people out of it. Do you want to be a dinosaur-like victim of the old model or a proactive pioneer of the new one?
  3. Freeze your essential biological materials
    Our bodies are a treasure trove of biological materials that could save us in the future, but every morning we still flush gold down the toilet. That gold, our stool, could potentially be frozen so we could repopulate our essential gut bacteria if our microbiome were to take a dangerous hit from antibiotics or illness. Skin cells could be transformed into potentially life-saving stem cells and stored for future use to help rejuvenate various types of aging cells. If our future treatments will be personalized using our own biological materials, but we’ll need to have stored these materials earlier in life to receive the full benefit of these advances. We put money in the bank to ensure our financial security, so why wouldn’t we put some of our biological materials in a bio-bank to have our youngest possible rescue cells waiting for us when we need them and help secure our physiological security?
  4. If you plan on ever having children, freeze your eggs or your sperm
    More people will soon shift from conceiving children through sex to conceiving them through IVF and embryo selection. The preliminary driver of this will be parents’ increasing recognition that they can reduce the roughly 3% chance their future children will be born with dangerous genetic mutations by having their embryos screened in a lab prior to implantation in the mother. This may seem less exciting than making babies in the back seat of a car, but the health and longevity benefits of screening embryos will ultimately overpower conception by sex kind of like how vaccinating our children has (mostly) overpowered the far more natural option of not doing so. If you are likely to conceive via IVF and embryo selection, why not freeze your eggs, sperm, or embryos when you are at your biological peak and when the chance of passing on genetic abnormalities is lower than it may be later in life?
  5. Manage your public identity
    The days of living incognito are over. No matter how aggressively some of us may try to avoid it, our lives leave massive digital footprints that are becoming an essential part of our very identities. The authoritarian government in China is planning to give “social credit“ scores evaluating the digitally monitored behavior of each citizen in a creepy and frightening way. But even in more liberal societies we will all be increasingly judged at work, at home, and in our commercial interactions based on our aggregated digital identities. These identities will be based on what we buy, what we post, what we seek, and how and with whom we interact online. Some societies and individuals are smartly trying to exert a level of control over the collection and use of this personal data, but even this won’t change the new reality that our digital identities will significantly influence what options are available to us in life and represent us after we die. Given this, and perhaps sadly, we all need to protect our privacy but also think of our public selves as brands, managing our digitally recorded activity from early on to present ourselves to the world the way we consciously want the world to know us.
  6. Learn the language of code
    Our lives will be increasingly manipulated by algorithms few of us understand. Most people who were once good at finding their way now just use their GPS-guided smart phones to get where they need to go. As algorithms touching many different aspects of our lives get better, we will increasingly rely on them to make plans, purchasing decisions, and even significant life choices for us. Pretty much every job we might do and many other aspects of our lives will be guided by artificial intelligence and big data analytics. Fully understanding every detail of how each of these algorithms function may be impossible, but we’ll be even more at their mercy if we don’t each acquire at least a rudimentary understanding of what code is and how it works. If you can read one book about code, that’s a start. Learning the fundamental of coding will do even more to help you navigate the fast arriving algorithmic world.
  7. Become multicultural
    Pretty much wherever you were in the 18th century, you needed to understand Europe to operate effectively because European power then defined so many parts of the world. The same was true for understanding United States in the 20th century understanding America was imperative for most people living outside of the United States because US actions influenced so many aspects of their lives. For many people living in 20th century America, understanding the rest of the world was merely interesting. As China rises and Global power decentralizes in the 21st-century, we’ll all need to learn more about China, India, and other new power, population, and culture centers than ever before. This won’t just help you become a more well-rounded person, it will give you a far greater chance of success in most anything you’ll be doing. Although machine translation will make communicating across languages pretty seamless, you’ll need a cultural fluidity and fluency to succeed in the 21st century world. The good news is that people motivated to learn about other groups and societies now have more resources than ever before to do so. If you want to be ready for our multicultural, multinational future, you’d better start doing all you can to learn about other cultures and societies now.
  8. Become an obsessive learner
    Technological change has been a constant throughout human history, but the pace of change is today accelerating far more rapidly than ever before. As innovations across the spectrum of science and technology empower, inspire, and reinforce each other, multiple technological transformations are converging into a revolutionary whole far greater than the sum of its parts. This unprecedented rate of change will mean that much of your knowledge will start becoming obsolete as soon as you acquire it. To keep up in your career and life, you’ll need to dedicate yourself to a lifetime of never ending, aggressive, continuous, and creativity-driven learning. The only skill worth having in an exponential world will be knowing how to learn and a passion for doing it. Call me an old-fashioned futurist, but this learning process must include reading lots of books to help you understand where we have come from and how the disparate pieces of information fit together to create a larger story. This type of knowledge will be an essential foundation of the wisdom we’ll each and all need to navigate our fast-changing world.
  9. Invest in physical community
    We humans are social species. A primary reason we rose to the top of the food chain and built civilization is that our brains are optimized for collaborating with those around us. When we bond with our partners and friends, we realize one of our essential cord needs as humans. That’s why people in solitary confinement tend to go a bit crazy. But although our progression from feeling our sense of connection, belonging, and community has expanded from the level of clan to village to city to country to, in some ways, the world, we are still not virtual beings. We may get a little dopamine hit whenever someone likes our tweet or Facebook post, but most of us still need a connected physical community around us in order to be happy and to realize our best potential. With all of the virtual options that will surround us – chatbots engaging us in witty repartee, virtual assistants managing our schedules, and even friends messaging from faraway lands among them – our virtual future must remain grounded in our physical world. To build your essential community of flesh and blood people, you must invest in deep and meaningful relationships with the people physically around you.
  10.   Don’t get stuck in today The olden days were, at least in most peoples’ minds, always better. We used to have better values, a better work ethic, better communities. We used to walk to school uphill in both directions! But while we do need to hold on to the best of the past, we also need to march boldly into the future. Because the coming world will feel like science fiction, will all need to be like science fiction writers  imagining the world ahead and positioning ourselves to shape it for the better. The technologies of the future will be radically new but we’ll need to draw on the best of our ancient value systems to use them wisely. The exponential future is coming faster than most of us appreciate or are ready for. Like it or not, we are now all futurists.

News Source = techcrunch.com

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