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#434767 7 Non-Obvious Trends Shaping the Future

Posted on March 21, 2019 by Android

When you think of trends that might be shaping the future, the first things that come to mind probably have something to do with technology: Robots taking over jobs. Artificial intelligence advancing and proliferating. 5G making everything faster, connected cities making everything easier, data making everything more targeted.

Technology is undoubtedly changing the way we live, and will continue to do so—probably at an accelerating rate—in the near and far future. But there are other trends impacting the course of our lives and societies, too. They’re less obvious, and some have nothing to do with technology.

For the past nine years, entrepreneur and author Rohit Bhargava has read hundreds of articles across all types of publications, tagged and categorized them by topic, funneled frequent topics into broader trends, analyzed those trends, narrowed them down to the most significant ones, and published a book about them as part of his ‘Non-Obvious’ series. He defines a trend as “a unique curated observation of the accelerating present.”

In an encore session at South by Southwest last week (his initial talk couldn’t fit hundreds of people who wanted to attend, so a re-do was scheduled), Bhargava shared details of his creative process, why it’s hard to think non-obviously, the most important trends of this year, and how to make sure they don’t get the best of you.

Thinking Differently
“Non-obvious thinking is seeing the world in a way other people don’t see it,” Bhargava said. “The secret is curating your ideas.” Curation collects ideas and presents them in a meaningful way; museum curators, for example, decide which works of art to include in an exhibit and how to present them.

For his own curation process, Bhargava uses what he calls the haystack method. Rather than searching for a needle in a haystack, he gathers ‘hay’ (ideas and stories) then uses them to locate and define a ‘needle’ (a trend). “If you spend enough time gathering information, you can put the needle into the middle of the haystack,” he said.

A big part of gathering information is looking for it in places you wouldn’t normally think to look. In his case, that means that on top of reading what everyone else reads—the New York Times, the Washington Post, the Economist—he also buys publications like Modern Farmer, Teen Vogue, and Ink magazine. “It’s like stepping into someone else’s world who’s not like me,” he said. “That’s impossible to do online because everything is personalized.”

Three common barriers make non-obvious thinking hard.

The first is unquestioned assumptions, which are facts or habits we think will never change. When James Dyson first invented the bagless vacuum, he wanted to sell the license to it, but no one believed people would want to spend more money up front on a vacuum then not have to buy bags. The success of Dyson’s business today shows how mistaken that assumption—that people wouldn’t adapt to a product that, at the end of the day, was far more sensible—turned out to be. “Making the wrong basic assumptions can doom you,” Bhargava said.

The second barrier to thinking differently is constant disruption. “Everything is changing as industries blend together,” Bhargava said. “The speed of change makes everyone want everything, all the time, and people expect the impossible.” We’ve come to expect every alternative to be presented to us in every moment, but in many cases this doesn’t serve us well; we’re surrounded by noise and have trouble discerning what’s valuable and authentic.

This ties into the third barrier, which Bhargava calls the believability crisis. “Constant sensationalism makes people skeptical about everything,” he said. With the advent of fake news and technology like deepfakes, we’re in a post-truth, post-fact era, and are in a constant battle to discern what’s real from what’s not.

2019 Trends
Bhargava’s efforts to see past these barriers and curate information yielded 15 trends he believes are currently shaping the future. He shared seven of them, along with thoughts on how to stay ahead of the curve.

Retro Trust
We tend to trust things we have a history with. “People like nostalgic experiences,” Bhargava said. With tech moving as fast as it is, old things are quickly getting replaced by shinier, newer, often more complex things. But not everyone’s jumping on board—and some who’ve been on board are choosing to jump off in favor of what worked for them in the past.

“We’re turning back to vinyl records and film cameras, deliberately downgrading to phones that only text and call,” Bhargava said. In a period of too much change too fast, people are craving familiarity and dependability. To capitalize on that sentiment, entrepreneurs should seek out opportunities for collaboration—how can you build a product that’s new, but feels reliable and familiar?

Muddled Masculinity
Women have increasingly taken on more leadership roles, advanced in the workplace, now own more homes than men, and have higher college graduation rates. That’s all great for us ladies—but not so great for men or, perhaps more generally, for the concept of masculinity.

“Female empowerment is causing confusion about what it means to be a man today,” Bhargava said. “Men don’t know what to do—should they say something? Would that make them an asshole? Should they keep quiet? Would that make them an asshole?”

By encouraging the non-conforming, we can help take some weight off the traditional gender roles, and their corresponding divisions and pressures.

Innovation Envy
Innovation has become an over-used word, to the point that it’s thrown onto ideas and actions that aren’t really innovative at all. “We innovate by looking at someone else and doing the same,” Bhargava said. If an employee brings a radical idea to someone in a leadership role, in many companies the leadership will say they need a case study before implementing the radical idea—but if it’s already been done, it’s not innovative. “With most innovation what ends up happening is not spectacular failure, but irrelevance,” Bhargava said.

He suggests that rather than being on the defensive, companies should play offense with innovation, and when it doesn’t work “fail as if no one’s watching” (often, no one will be).

Artificial Influence
Thanks to social media and other technologies, there are a growing number of fabricated things that, despite not being real, influence how we think. “15 percent of all Twitter accounts may be fake, and there are 60 million fake Facebook accounts,” Bhargava said. There are virtual influencers and even virtual performers.

“Don’t hide the artificial ingredients,” Bhargava advised. “Some people are going to pretend it’s all real. We have to be ethical.” The creators of fabrications meant to influence the way people think, or the products they buy, or the decisions they make, should make it crystal-clear that there aren’t living, breathing people behind the avatars.

Enterprise Empathy
Another reaction to the fast pace of change these days—and the fast pace of life, for that matter—is that empathy is regaining value and even becoming a driver of innovation. Companies are searching for ways to give people a sense of reassurance. The Tesco grocery brand in the UK has a “relaxed lane” for those who don’t want to feel rushed as they check out. Starbucks opened a “signing store” in Washington DC, and most of its regular customers have learned some sign language.

“Use empathy as a principle to help yourself stand out,” Bhargava said. Besides being a good business strategy, “made with empathy” will ideally promote, well, more empathy, a quality there’s often a shortage of.

Robot Renaissance
From automating factory jobs to flipping burgers to cleaning our floors, robots have firmly taken their place in our day-to-day lives—and they’re not going away anytime soon. “There are more situations with robots than ever before,” Bhargava said. “They’re exploring underwater. They’re concierges at hotels.”

The robot revolution feels intimidating. But Bhargava suggests embracing robots with more curiosity than concern. While they may replace some tasks we don’t want replaced, they’ll also be hugely helpful in multiple contexts, from elderly care to dangerous manual tasks.

Back-storytelling
Similar to retro trust and enterprise empathy, organizations have started to tell their brand’s story to gain customer loyalty. “Stories give us meaning, and meaning is what we need in order to be able to put the pieces together,” Bhargava said. “Stories give us a way of understanding the world.”

Finding the story behind your business, brand, or even yourself, and sharing it openly, can help you connect with people, be they customers, coworkers, or friends.

Tech’s Ripple Effects
While it may not overtly sound like it, most of the trends Bhargava identified for 2019 are tied to technology, and are in fact a sort of backlash against it. Tech has made us question who to trust, how to innovate, what’s real and what’s fake, how to make the best decisions, and even what it is that makes us human.

By being aware of these trends, sharing them, and having conversations about them, we’ll help shape the way tech continues to be built, and thus the way it impacts us down the road.

Image Credit: Rohit Bhargava by Brian Smale Continue reading →

Posted in Human Robots

#434759 To Be Ethical, AI Must Become ...

Posted on March 19, 2019 by Android

As over-hyped as artificial intelligence is—everyone’s talking about it, few fully understand it, it might leave us all unemployed but also solve all the world’s problems—its list of accomplishments is growing. AI can now write realistic-sounding text, give a debating champ a run for his money, diagnose illnesses, and generate fake human faces—among much more.

After training these systems on massive datasets, their creators essentially just let them do their thing to arrive at certain conclusions or outcomes. The problem is that more often than not, even the creators don’t know exactly why they’ve arrived at those conclusions or outcomes. There’s no easy way to trace a machine learning system’s rationale, so to speak. The further we let AI go down this opaque path, the more likely we are to end up somewhere we don’t want to be—and may not be able to come back from.

In a panel at the South by Southwest interactive festival last week titled “Ethics and AI: How to plan for the unpredictable,” experts in the field shared their thoughts on building more transparent, explainable, and accountable AI systems.

Not New, but Different
Ryan Welsh, founder and director of explainable AI startup Kyndi, pointed out that having knowledge-based systems perform advanced tasks isn’t new; he cited logistical, scheduling, and tax software as examples. What’s new is the learning component, our inability to trace how that learning occurs, and the ethical implications that could result.

“Now we have these systems that are learning from data, and we’re trying to understand why they’re arriving at certain outcomes,” Welsh said. “We’ve never actually had this broad society discussion about ethics in those scenarios.”

Rather than continuing to build AIs with opaque inner workings, engineers must start focusing on explainability, which Welsh broke down into three subcategories. Transparency and interpretability come first, and refer to being able to find the units of high influence in a machine learning network, as well as the weights of those units and how they map to specific data and outputs.

Then there’s provenance: knowing where something comes from. In an ideal scenario, for example, Open AI’s new text generator would be able to generate citations in its text that reference academic (and human-created) papers or studies.

Explainability itself is the highest and final bar and refers to a system’s ability to explain itself in natural language to the average user by being able to say, “I generated this output because x, y, z.”

“Humans are unique in our ability and our desire to ask why,” said Josh Marcuse, executive director of the Defense Innovation Board, which advises Department of Defense senior leaders on innovation. “The reason we want explanations from people is so we can understand their belief system and see if we agree with it and want to continue to work with them.”

Similarly, we need to have the ability to interrogate AIs.

Two Types of Thinking
Welsh explained that one big barrier standing in the way of explainability is the tension between the deep learning community and the symbolic AI community, which see themselves as two different paradigms and historically haven’t collaborated much.

Symbolic or classical AI focuses on concepts and rules, while deep learning is centered around perceptions. In human thought this is the difference between, for example, deciding to pass a soccer ball to a teammate who is open (you make the decision because conceptually you know that only open players can receive passes), and registering that the ball is at your feet when someone else passes it to you (you’re taking in information without making a decision about it).

“Symbolic AI has abstractions and representation based on logic that’s more humanly comprehensible,” Welsh said. To truly mimic human thinking, AI needs to be able to both perceive information and conceptualize it. An example of perception (deep learning) in an AI is recognizing numbers within an image, while conceptualization (symbolic learning) would give those numbers a hierarchical order and extract rules from the hierachy (4 is greater than 3, and 5 is greater than 4, therefore 5 is also greater than 3).

Explainability comes in when the system can say, “I saw a, b, and c, and based on that decided x, y, or z.” DeepMind and others have recently published papers emphasizing the need to fuse the two paradigms together.

Implications Across Industries
One of the most prominent fields where AI ethics will come into play, and where the transparency and accountability of AI systems will be crucial, is defense. Marcuse said, “We’re accountable beings, and we’re responsible for the choices we make. Bringing in tech or AI to a battlefield doesn’t strip away that meaning and accountability.”

In fact, he added, rather than worrying about how AI might degrade human values, people should be asking how the tech could be used to help us make better moral choices.

It’s also important not to conflate AI with autonomy—a worst-case scenario that springs to mind is an intelligent destructive machine on a rampage. But in fact, Marcuse said, in the defense space, “We have autonomous systems today that don’t rely on AI, and most of the AI systems we’re contemplating won’t be autonomous.”

The US Department of Defense released its 2018 artificial intelligence strategy last month. It includes developing a robust and transparent set of principles for defense AI, investing in research and development for AI that’s reliable and secure, continuing to fund research in explainability, advocating for a global set of military AI guidelines, and finding ways to use AI to reduce the risk of civilian casualties and other collateral damage.

Though these were designed with defense-specific aims in mind, Marcuse said, their implications extend across industries. “The defense community thinks of their problems as being unique, that no one deals with the stakes and complexity we deal with. That’s just wrong,” he said. Making high-stakes decisions with technology is widespread; safety-critical systems are key to aviation, medicine, and self-driving cars, to name a few.

Marcuse believes the Department of Defense can invest in AI safety in a way that has far-reaching benefits. “We all depend on technology to keep us alive and safe, and no one wants machines to harm us,” he said.

A Creation Superior to Its Creator
That said, we’ve come to expect technology to meet our needs in just the way we want, all the time—servers must never be down, GPS had better not take us on a longer route, Google must always produce the answer we’re looking for.

With AI, though, our expectations of perfection may be less reasonable.

“Right now we’re holding machines to superhuman standards,” Marcuse said. “We expect them to be perfect and infallible.” Take self-driving cars. They’re conceived of, built by, and programmed by people, and people as a whole generally aren’t great drivers—just look at traffic accident death rates to confirm that. But the few times self-driving cars have had fatal accidents, there’s been an ensuing uproar and backlash against the industry, as well as talk of implementing more restrictive regulations.

This can be extrapolated to ethics more generally. We as humans have the ability to explain our decisions, but many of us aren’t very good at doing so. As Marcuse put it, “People are emotional, they confabulate, they lie, they’re full of unconscious motivations. They don’t pass the explainability test.”

Why, then, should explainability be the standard for AI?

Even if humans aren’t good at explaining our choices, at least we can try, and we can answer questions that probe at our decision-making process. A deep learning system can’t do this yet, so working towards being able to identify which input data the systems are triggering on to make decisions—even if the decisions and the process aren’t perfect—is the direction we need to head.

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Posted in Human Robots

#434701 3 Practical Solutions to Offset ...

Posted on March 11, 2019 by Android

In recent years, the media has sounded the alarm about mass job loss to automation and robotics—some studies predict that up to 50 percent of current jobs or tasks could be automated in coming decades. While this topic has received significant attention, much of the press focuses on potential problems without proposing realistic solutions or considering new opportunities.

The economic impacts of AI, robotics, and automation are complex topics that require a more comprehensive perspective to understand. Is universal basic income, for example, the answer? Many believe so, and there are a number of experiments in progress. But it’s only one strategy, and without a sustainable funding source, universal basic income may not be practical.

As automation continues to accelerate, we’ll need a multi-pronged approach to ease the transition. In short, we need to update broad socioeconomic strategies for a new century of rapid progress. How, then, do we plan practical solutions to support these new strategies?

Take history as a rough guide to the future. Looking back, technology revolutions have three themes in common.

First, past revolutions each produced profound benefits to productivity, increasing human welfare. Second, technological innovation and technology diffusion have accelerated over time, each iteration placing more strain on the human ability to adapt. And third, machines have gradually replaced more elements of human work, with human societies adapting by moving into new forms of work—from agriculture to manufacturing to service, for example.

Public and private solutions, therefore, need to be developed to address each of these three components of change. Let’s explore some practical solutions for each in turn.

Figure 1. Technology’s structural impacts in the 21st century. Refer to Appendix I for quantitative charts and technological examples corresponding to the numbers (1-22) in each slice.
Solution 1: Capture New Opportunities Through Aggressive Investment
The rapid emergence of new technology promises a bounty of opportunity for the twenty-first century’s economic winners. This technological arms race is shaping up to be a global affair, and the winners will be determined in part by who is able to build the future economy fastest and most effectively. Both the private and public sectors have a role to play in stimulating growth.

At the country level, several nations have created competitive strategies to promote research and development investments as automation technologies become more mature.

Germany and China have two of the most notable growth strategies. Germany’s Industrie 4.0 plan targets a 50 percent increase in manufacturing productivity via digital initiatives, while halving the resources required. China’s Made in China 2025 national strategy sets ambitious targets and provides subsidies for domestic innovation and production. It also includes building new concept cities, investing in robotics capabilities, and subsidizing high-tech acquisitions abroad to become the leader in certain high-tech industries. For China, specifically, tech innovation is driven partially by a fear that technology will disrupt social structures and government control.

Such opportunities are not limited to existing economic powers. Estonia’s progress after the breakup of the Soviet Union is a good case study in transitioning to a digital economy. The nation rapidly implemented capitalistic reforms and transformed itself into a technology-centric economy in preparation for a massive tech disruption. Internet access was declared a right in 2000, and the country’s classrooms were outfitted for a digital economy, with coding as a core educational requirement starting at kindergarten. Internet broadband speeds in Estonia are among the fastest in the world. Accordingly, the World Bank now ranks Estonia as a high-income country.

Solution 2: Address Increased Rate of Change With More Nimble Education Systems
Education and training are currently not set for the speed of change in the modern economy. Schools are still based on a one-time education model, with school providing the foundation for a single lifelong career. With content becoming obsolete faster and rapidly escalating costs, this system may be unsustainable in the future. To help workers more smoothly transition from one job into another, for example, we need to make education a more nimble, lifelong endeavor.

Primary and university education may still have a role in training foundational thinking and general education, but it will be necessary to curtail rising price of tuition and increase accessibility. Massive open online courses (MooCs) and open-enrollment platforms are early demonstrations of what the future of general education may look like: cheap, effective, and flexible.

Georgia Tech’s online Engineering Master’s program (a fraction of the cost of residential tuition) is an early example in making university education more broadly available. Similarly, nanodegrees or microcredentials provided by online education platforms such as Udacity and Coursera can be used for mid-career adjustments at low cost. AI itself may be deployed to supplement the learning process, with applications such as AI-enhanced tutorials or personalized content recommendations backed by machine learning. Recent developments in neuroscience research could optimize this experience by perfectly tailoring content and delivery to the learner’s brain to maximize retention.

Finally, companies looking for more customized skills may take a larger role in education, providing on-the-job training for specific capabilities. One potential model involves partnering with community colleges to create apprenticeship-style learning, where students work part-time in parallel with their education. Siemens has pioneered such a model in four states and is developing a playbook for other companies to do the same.

Solution 3: Enhance Social Safety Nets to Smooth Automation Impacts
If predicted job losses to automation come to fruition, modernizing existing social safety nets will increasingly become a priority. While the issue of safety nets can become quickly politicized, it is worth noting that each prior technological revolution has come with corresponding changes to the social contract (see below).

Figure 2. Labor laws have historically adjusted as technology and society progressed

Solutions like universal basic income (no-strings-attached monthly payout to all citizens) are appealing in concept, but somewhat difficult to implement as a first measure in countries such as the US or Japan that already have high debt. Additionally, universal basic income may create dis-incentives to stay in the labor force. A similar cautionary tale in program design was the Trade Adjustment Assistance (TAA), which was designed to protect industries and workers from import competition shocks from globalization, but is viewed as a missed opportunity due to insufficient coverage.

A near-term solution could come in the form of graduated wage insurance (compensation for those forced to take a lower-paying job), including health insurance subsidies to individuals directly impacted by automation, with incentives to return to the workforce quickly. Another topic to tackle is geographic mismatch between workers and jobs, which can be addressed by mobility assistance. Lastly, a training stipend can be issued to individuals as means to upskill.

Policymakers can intervene to reverse recent historical trends that have shifted incomes from labor to capital owners. The balance could be shifted back to labor by placing higher taxes on capital—an example is the recently proposed “robot tax” where the taxation would be on the work rather than the individual executing it. That is, if a self-driving car performs the task that formerly was done by a human, the rideshare company will still pay the tax as if a human was driving.

Other solutions may involve distribution of work. Some countries, such as France and Sweden, have experimented with redistributing working hours. The idea is to cap weekly hours, with the goal of having more people employed and work more evenly spread. So far these programs have had mixed results, with lower unemployment but high costs to taxpayers, but are potential models that can continue to be tested.

We cannot stop growth, nor should we. With the roles in response to this evolution shifting, so should the social contract between the stakeholders. Government will continue to play a critical role as a stabilizing “thumb” in the invisible hand of capitalism, regulating and cushioning against extreme volatility, particularly in labor markets.

However, we already see business leaders taking on some of the role traditionally played by government—thinking about measures to remedy risks of climate change or economic proposals to combat unemployment—in part because of greater agility in adapting to change. Cross-disciplinary collaboration and creative solutions from all parties will be critical in crafting the future economy.

Note: The full paper this article is based on is available here.

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Posted in Human Robots

#434673 The World’s Most Valuable AI ...

Posted on February 27, 2019 by Android

It recognizes our faces. It knows the videos we might like. And it can even, perhaps, recommend the best course of action to take to maximize our personal health.

Artificial intelligence and its subset of disciplines—such as machine learning, natural language processing, and computer vision—are seemingly becoming integrated into our daily lives whether we like it or not. What was once sci-fi is now ubiquitous research and development in company and university labs around the world.

Similarly, the startups working on many of these AI technologies have seen their proverbial stock rise. More than 30 of these companies are now valued at over a billion dollars, according to data research firm CB Insights, which itself employs algorithms to provide insights into the tech business world.

Private companies with a billion-dollar valuation were so uncommon not that long ago that they were dubbed unicorns. Now there are 325 of these once-rare creatures, with a combined valuation north of a trillion dollars, as CB Insights maintains a running count of this exclusive Unicorn Club.

The subset of AI startups accounts for about 10 percent of the total membership, growing rapidly in just 4 years from 0 to 32. Last year, an unprecedented 17 AI startups broke the billion-dollar barrier, with 2018 also a record year for venture capital into private US AI companies at $9.3 billion, CB Insights reported.

What exactly is all this money funding?

AI Keeps an Eye Out for You
Let’s start with the bad news first.

Facial recognition is probably one of the most ubiquitous applications of AI today. It’s actually a decades-old technology often credited to a man named Woodrow Bledsoe, who used an instrument called a RAND tablet that could semi-autonomously match faces from a database. That was in the 1960s.

Today, most of us are familiar with facial recognition as a way to unlock our smartphones. But the technology has gained notoriety as a surveillance tool of law enforcement, particularly in China.

It’s no secret that the facial recognition algorithms developed by several of the AI unicorns from China—SenseTime, CloudWalk, and Face++ (also known as Megvii)—are used to monitor the country’s 1.3 billion citizens. Police there are even equipped with AI-powered eyeglasses for such purposes.

A fourth billion-dollar Chinese startup, Yitu Technologies, also produces a platform for facial recognition in the security realm, and develops AI systems in healthcare on top of that. For example, its CARE.AITM Intelligent 4D Imaging System for Chest CT can reputedly identify in real time a variety of lesions for the possible early detection of cancer.

The AI Doctor Is In
As Peter Diamandis recently noted, AI is rapidly augmenting healthcare and longevity. He mentioned another AI unicorn from China in this regard—iCarbonX, which plans to use machines to develop personalized health plans for every individual.

A couple of AI unicorns on the hardware side of healthcare are OrCam Technologies and Butterfly. The former, an Israeli company, has developed a wearable device for the vision impaired called MyEye that attaches to one’s eyeglasses. The device can identify people and products, as well as read text, conveying the information through discrete audio.

Butterfly Network, out of Connecticut, has completely upended the healthcare market with a handheld ultrasound machine that works with a smartphone.

“Orcam and Butterfly are amazing examples of how machine learning can be integrated into solutions that provide a step-function improvement over state of the art in ultra-competitive markets,” noted Andrew Byrnes, investment director at Comet Labs, a venture capital firm focused on AI and robotics, in an email exchange with Singularity Hub.

AI in the Driver’s Seat
Comet Labs’ portfolio includes two AI unicorns, Megvii and Pony.ai.

The latter is one of three billion-dollar startups developing the AI technology behind self-driving cars, with the other two being Momenta.ai and Zoox.

Founded in 2016 near San Francisco (with another headquarters in China), Pony.ai debuted its latest self-driving system, called PonyAlpha, last year. The platform uses multiple sensors (LiDAR, cameras, and radar) to navigate its environment, but its “sensor fusion technology” makes things simple by choosing the most reliable sensor data for any given driving scenario.

Zoox is another San Francisco area startup founded a couple of years earlier. In late 2018, it got the green light from the state of California to be the first autonomous vehicle company to transport a passenger as part of a pilot program. Meanwhile, China-based Momenta.ai is testing level four autonomy for its self-driving system. Autonomous driving levels are ranked zero to five, with level five being equal to a human behind the wheel.

The hype around autonomous driving is currently in overdrive, and Byrnes thinks regulatory roadblocks will keep most self-driving cars in idle for the foreseeable future. The exception, he said, is China, which is adopting a “systems” approach to autonomy for passenger transport.

“If [autonomous mobility] solves bigger problems like traffic that can elicit government backing, then that has the potential to go big fast,” he said. “This is why we believe Pony.ai will be a winner in the space.”

AI in the Back Office
An AI-powered technology that perhaps only fans of the cult classic Office Space might appreciate has suddenly taken the business world by storm—robotic process automation (RPA).

RPA companies take the mundane back office work, such as filling out invoices or processing insurance claims, and turn it over to bots. The intelligent part comes into play because these bots can tackle unstructured data, such as text in an email or even video and pictures, in order to accomplish an increasing variety of tasks.

Both Automation Anywhere and UiPath are older companies, founded in 2003 and 2005, respectively. However, since just 2017, they have raised nearly a combined $1 billion in disclosed capital.

Cybersecurity Embraces AI
Cybersecurity is another industry where AI is driving investment into startups. Sporting imposing names like CrowdStrike, Darktrace, and Tanium, these cybersecurity companies employ different machine-learning techniques to protect computers and other IT assets beyond the latest software update or virus scan.

Darktrace, for instance, takes its inspiration from the human immune system. Its algorithms can purportedly “learn” the unique pattern of each device and user on a network, detecting emerging problems before things spin out of control.

All three companies are used by major corporations and governments around the world. CrowdStrike itself made headlines a few years ago when it linked the hacking of the Democratic National Committee email servers to the Russian government.

Looking Forward
I could go on, and introduce you to the world’s most valuable startup, a Chinese company called Bytedance that is valued at $75 billion for news curation and an app to create 15-second viral videos. But that’s probably not where VC firms like Comet Labs are generally putting their money.

Byrnes sees real value in startups that are taking “data-driven approaches to problems specific to unique industries.” Take the example of Chicago-based unicorn Uptake Technologies, which analyzes incoming data from machines, from wind turbines to tractors, to predict problems before they occur with the machinery. A not-yet unicorn called PingThings in the Comet Labs portfolio does similar predictive analytics for the energy utilities sector.

“One question we like asking is, ‘What does the state of the art look like in your industry in three to five years?’” Byrnes said. “We ask that a lot, then we go out and find the technology-focused teams building those things.”

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Posted in Human Robots

#434655 Purposeful Evolution: Creating an ...

Posted on February 21, 2019 by Android

More often than not, we fall into the trap of trying to predict and anticipate the future, forgetting that the future is up to us to envision and create. In the words of Buckminster Fuller, “We are called to be architects of the future, not its victims.”

But how, exactly, do we create a “good” future? What does such a future look like to begin with?

In Future Consciousness: The Path to Purposeful Evolution, Tom Lombardo analytically deconstructs how we can flourish in the flow of evolution and create a prosperous future for humanity. Scientifically informed, the books taps into themes that are constructive and profound, from both eastern and western philosophies.

As the executive director of the Center for Future Consciousness and an executive board member and fellow of the World Futures Studies Federation, Lombardo has dedicated his life and career to studying how we can create a “realistic, constructive, and ethical future.”

In a conversation with Singularity Hub, Lombardo discussed purposeful evolution, ethical use of technology, and the power of optimism.

Raya Bidshahri: Tell me more about the title of your book. What is future consciousness and what role does it play in what you call purposeful evolution?

Tom Lombardo: Humans have the unique capacity to purposefully evolve themselves because they possess future consciousness. Future consciousness contains all of the cognitive, motivational, and emotional aspects of the human mind that pertain to the future. It’s because we can imagine and think about the future that we can manipulate and direct our future evolution purposefully. Future consciousness empowers us to become self-responsible in our own evolutionary future. This is a jump in the process of evolution itself.

RB: In several places in the book, you discuss the importance of various eastern philosophies. What can we learn from the east that is often missing from western models?

TL: The key idea in the east that I have been intrigued by for decades is the Taoist Yin Yang, which is the idea that reality should be conceptualized as interdependent reciprocities.

In the west we think dualistically, or we attempt to think in terms of one end of the duality to the exclusion of the other, such as whole versus parts or consciousness versus physical matter. Yin Yang thinking is seeing how both sides of a “duality,” even though they appear to be opposites, are interdependent; you can’t have one without the other. You can’t have order without chaos, consciousness without the physical world, individuals without the whole, humanity without technology, and vice versa for all these complementary pairs.

RB: You talk about the importance of chaos and destruction in the trajectory of human progress. In your own words, “Creativity frequently involves destruction as a prelude to the emergence of some new reality.” Why is this an important principle for readers to keep in mind, especially in the context of today’s world?

TL: In order for there to be progress, there often has to be a disintegration of aspects of the old. Although progress and evolution involve a process of building up, growth isn’t entirely cumulative; it’s also transformative. Things fall apart and come back together again.

Throughout history, we have seen a transformation of what are the most dominant human professions or vocations. At some point, almost everybody worked in agriculture, but most of those agricultural activities were replaced by machines, and a lot of people moved over to industry. Now we’re seeing that jobs and functions are increasingly automated in industry, and humans are being pushed into vocations that involve higher cognitive and artistic skills, services, information technology, and so on.

RB: You raise valid concerns about the dark side of technological progress, especially when it’s combined with mass consumerism, materialism, and anti-intellectualism. How do we counter these destructive forces as we shape the future of humanity?

TL: We can counter such forces by always thoughtfully considering how our technologies are affecting the ongoing purposeful evolution of our conscious minds, bodies, and societies. We should ask ourselves what are the ethical values that are being served by the development of various technologies.

For example, we often hear the criticism that technologies that are driven by pure capitalism degrade human life and only benefit the few people who invented and market them. So we need to also think about what good these new technologies can serve. It’s what I mean when I talk about the “wise cyborg.” A wise cyborg is somebody who uses technology to serve wisdom, or values connected with wisdom.

RB: Creating an ideal future isn’t just about progress in technology, but also progress in morality. How we do decide what a “good” future is? What are some philosophical tools we can use to determine a code of ethics that is as objective as possible?

TL: Let’s keep in mind that ethics will always have some level of subjectivity. That being said, the way to determine a good future is to base it on the best theory of reality that we have, which is that we are evolutionary beings in an evolutionary universe and we are interdependent with everything else in that universe. Our ethics should acknowledge that we are fluid and interactive.

Hence, the “good” can’t be something static, and it can’t be something that pertains to me and not everybody else. It can’t be something that only applies to humans and ignores all other life on Earth, and it must be a mode of change rather than something stable.

RB: You present a consciousness-centered approach to creating a good future for humanity. What are some of the values we should develop in order to create a prosperous future?

TL: A sense of self-responsibility for the future is critical. This means realizing that the “good future” is something we have to take upon ourselves to create; we can’t let something or somebody else do that. We need to feel responsible both for our own futures and for the future around us.

Another one is going to be an informed and hopeful optimism about the future, because both optimism and pessimism have self-fulfilling prophecy effects. If you hope for the best, you are more likely to look deeply into your reality and increase the chance of it coming out that way. In fact, all of the positive emotions that have to do with future consciousness actually make people more intelligent and creative.

Some other important character virtues are discipline and tenacity, deep purpose, the love of learning and thinking, and creativity.

RB: Are you optimistic about the future? If so, what informs your optimism?

I justify my optimism the same way that I have seen Ray Kurzweil, Peter Diamandis, Kevin Kelly, and Steven Pinker justify theirs. If we look at the history of human civilization and even the history of nature, we see a progressive motion forward toward greater complexity and even greater intelligence. There’s lots of ups and downs, and catastrophes along the way, but the facts of nature and human history support the long-term expectation of continued evolution into the future.

You don’t have to be unrealistic to be optimistic. It’s also, psychologically, the more empowering position. That’s the position we should take if we want to maximize the chances of our individual or collective reality turning out better.

A lot of pessimists are pessimistic because they’re afraid of the future. There are lots of reasons to be afraid, but all in all, fear disempowers, whereas hope empowers.

Image Credit: Quick Shot / Shutterstock.com

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