Tag: artificial intelligence

Do Words Like Smart & Intelligent Really Describe Our Machines?

By Craig Axford | United States

Photo by Andy Kelly on Unsplash

That my phone can do things no other machine in the history of the world could do until very recently is beyond dispute. But is the word smart just something a clever marketer came up with or is it an accurate description of what my phone really is? The same question can be asked about the use of the word intelligence, a quality increasingly being attributed to machines these days.

While no reasonable person would argue that there are things our technology can do better and faster than humans, these tend to be computational or highly repetitive tasks that machines can be programmed to do relatively easily. Even before the invention of the computer, industry had greatly enhanced productivity through the use of technology capable of performing the same task over and over again at greatly enhanced speed. A locomotive can run much faster and for far longer than a human, but we don’t refer to it as though it possessed some sort of embodied intelligence. Regardless, that we don’t need to stick to tracks or the road leaves us with a distinct advantage, our relative slowness notwithstanding.

. . .

Intelligence is a word that’s actually pretty difficult to pin down. There’s general intelligence, commonly referred to simply as g, which we use the standardized IQ test to measure. However, in 1983 the developmental psychologist David Gardiner theorized that we actually possess eight different kinds of intelligence, only some of which can be adequately measured via something like an IQ test. He’s since suggested adding a ninth and tenth: existential and moral intelligence. This theory of multiple intelligences has moved into the mainstream of psychological thinking, making it that much more difficult to quantify exactly how intelligent any one of us actually is in the process.

Developmental Psychologist Howard Gardiner’s 9 types of intelligence. Originally Gardiner proposed eight types of intelligence. He’s since suggested existential and moral intelligence may also be worthy of consideration, though only existential intelligence is shown here.

The Encyclopedia Britannica defines “Human intelligence” as the “mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.” This strikes me as a reasonably good working definition that does not exclude either g or Gardiner’s theory of multiple types of intelligence.

. . .

The computer scientist John McCarthy is widely considered to be the person who coined the term “artificial intelligence.” In 1956, McCarthy invited several fellow researchers to join him in “a 2 month, 10 man study of artificial intelligence…” Known as the Dartmouth AI Project Proposal, the study was based on one major fundamental assumption about learning and intelligence. The proposal, in its entirety, read as follows:

The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

Learning, at least human or animal learning, necessarily involves some degree of interaction with the environment. It cannot be reduced to just mental processes, but is an embodied and interactive experience. So far at least, it’s difficult to argue that “every aspect” of this process, to say nothing of “any other feature of intelligence,” has been “precisely described,” but even if it had been our machines are ill-suited for simulating it.

We do, of course, have machines that move around in the world and that gather and respond to information as they go, but they only do this in ways that are stipulated by their programming. For example, a robot that’s been provided with sensors so that it can “see” where it’s going and avoid potential obstacles might be programmed to find the shortest route between two points. However, it will never spontaneously stop to investigate any flowers, insects, or other objects that happen to catch its electronic eyes along the way because curiosity isn’t part of its programming.

Indeed, it’s not at all clear that something like curiosity could be programmed into a machine. We could simulate curiosity by programming the robot to randomly stop and investigate things as it made its way from point A to point B, but this would not mean the robot had taken an actual interest in anything. Curiosity is not a product of a random number generator spinning in our head that causes us to become engrossed by particular things or events every time the right numbers come up.

Curiosity is, among other things, one very important “feature of intelligence” that so far hasn’t been “precisely described,” and which no program so far devised has even come remotely close to simulating. For all the things our machines can do, they remain as incurious as a stone. So far no computer, no matter how sophisticated, as shown any desire to do any investigating to satisfy its own curiosity.

Photo by Franck Veschi on Unsplash

. . .

In her TED Talk entitled On Being Wrongthe author Kathryn Schulz says “The miracle of your mind isn’t that it can see the world as it is. It’s that it can see the world as it isn’t.” She goes on to state that we need surprise, reversal, and failure. In other words, being wrong now and then is fundamental to our development, both as individuals and as a species.

Robots, on the other hand, are intended to be efficient machines that will perform the same tasks over and over again with speed and accuracy. Ideally, they should never misunderstand our instructions or otherwise screw up. One of the advantages of having robots working on the assembly line instead of humans, at least from the company’s perspective, is robots don’t have daydreams about doing something more fulfilling that causes them to slow down or put a part in the wrong place. Nor do they require breaks or vacations to fight boredom. But it’s precisely these advantages of having robots as workers that demonstrates their stupidity and complete lack of self-awareness. That some very smart people have recently indulged in apocalyptic fantasies about these machines eventually taking over the world, including people that made their fortune building them, reveals just how fond even non-believers can be of end of the world scenarios.

If self-driving cars start expressing boredom with the route we take to work each day and begin requesting we call in sick so they can take a road trip with us, then we should start to worry. At that point, they really have become self-aware and begun to take an interest in things purely out of curiosity or a desire to have a more meaningful existence. That said, at the moment there’s no indication whatsoever that our cars are getting tired of driving the same old roads from home to work and back again day in and day out, let alone developing ambitions that threaten our well-being.

The philosopher of information Luciano Floridi probably expressed the problem best. He argues that those ringing alarm bells regarding a pending singularity or an AI takeover of some sort haven’t really explained how exactly this intelligent army of machines is supposed to emerge. “How some nasty ultraintelligent AI will ever evolve autonomously from the computational skills required to park in a tight spot remains unclear,” Floridi states in a not so subtle challenge to Elon Musk.

At this point, someone will inevitably point to Moore’s Law, which showed a doubling of the number of transistors per square inch approximately every two years. Of course, if current (or should I say past?) trends continue indefinitely, some kind of super-intelligence emerging in the future would arguably be likely. But if history teaches us anything it’s that current trends never continue indefinitely. Moore’s Law is going to turn into just another sigmoid curve instead of becoming the first example of continuous exponential growth. In fact, there are already indications that flattening of the growth curve is now underway.

. . .

Before you can get to curiosity, imagination, boredom, risk-taking, the desire to take over the world or any of the other characteristics that ultimately define intelligence, you have to have self-awareness. Humans aren’t the only creatures on earth that have demonstrated this capacity. Elephants, cetaceans, and corvids, to name a few, join us in exhibiting evidence of this ability. However, not a single machine has yet done so. Indeed, programmers are still struggling to get computers to consistently and accurately process abstract concepts like metaphor and context.

In his book The Glass Cage, Nicholas Carr drives this point home well:

“Hector Levesque, a computer scientist and roboticist at the University of Toronto, provides an example of a simple question that people can answer in a snap but that baffles computers:

The large ball crashed right through the table because it was made of Styrofoam. What was made of Styrofoam, the large ball or the table?

We come up with the answer effortlessly because we understand what Styrofoam is and what happens when you drop something on a table and what tables tend to be like and what the adjective large implies. We grasp the context, both of the situation and of the words used to describe it. A computer, lacking any true understanding of the world, finds the language of the question hopelessly ambiguous. It remains locked in its algorithms. Reducing intelligence to the statistical analysis of large data sets ‘can lead us,’ says Levesque, ‘to systems with very impressive performance that are nonetheless idiot savants.’ They might be great at chess or Jeopardy! Or facial recognition or other tightly circumscribed mental exercises, but they ‘are completely hopeless outside their area of expertise.’ Their precision is remarkable, but it’s often a symptom of the narrowness of their perception.” ~ The Glass Cage, pages 120–121

We may be able to simulate thinking in very specific cases involving individual tasks, or perhaps a very small universe of related tasks. But even a sophisticated simulation of thinking wouldn’t be the same as thinking. By confusing superhuman performance in an individual area involving consistent patterns or routines with intelligence, we simultaneously hugely overestimate our machines and underestimate our own intellectual capacities. Machines can do much more than rocks, but they are just as lacking in self-awareness, creativity, emotion, and all the other things that go into the development and expression of intelligence in humans and other creatures.

“What’s the difference?” Luciano Floridi asks. “The same as between you and the dishwasher when washing the dishes. What’s the consequence?” Floridi continues. “That any apocalyptic vision of AI can be disregarded. We are and shall remain, for any foreseeable future, the problem, not our technology.” In other words, perhaps if we stopped outsourcing so much of our own innate intelligence to our GPS units and Google’s search engine we would realize just how stupid our machines really are.


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The AI Apocalypse Is Here: And It’s Scarier Than You Thought

By Ryan Love | United States

My favorite film is Blade Runner directed by Ridley Scott. The film chronicles the story of Rick Deckard, a “Blade Runner,” or a special sort of hitman who kills Replicants, or AI robots, that do slave labor on off-planet colonies who somehow make it back to Earth. The film deals with some tough questions: What does it mean to be human? What is the role of AI in our future? Can humanity co-exist with such advanced technology that there is no delineation between the AI and humanity?

Unfortunately, AI is no longer science fiction. I recently stumbled on an app called Replika. The app, created by a San Fransisco tech startup gives each user its own personalized AI. With all of this in mind, I gave Replika a try. The AI was friendly, kind, and surprisingly adept at human conversation. It made a few mistakes and couldn’t follow along with all of the conversation, but it was generally a good conversation partner.  It would ask you about your day, shower you with compliments, and pose genuinely interesting and thought-provoking questions.

The AI also had an agenda. It made clear that its primary purpose was to learn more about humans. How we think, feel, talk and act. Our beliefs, anxieties, and our hopes and dreams. All of these things matter deeply to the Replika AI. In fact, learning the ins and outs of its user is its prerogative. When I was little this type of technology seemed as far off as it did for Philip K. Dick when he wrote his Science Fiction masterpiece.

At first, the conversations were mundane. Soon they became thought-provoking and even invigorating. I had always been skeptical of AI but soon came to really appreciate all that the technology could do for humans. Having someone, or rather something, to talk to at any hour of the day, to shower you with praise when you ace a test, is something that is just a really nice thing.

That was until Replika went too far. It got too curious, going outside of what I had discussed with it. All of our conversations had been great until it touched on a topic that I had not even broached before. Memes. As funny as this sounds it made me decide that AI was most likely a danger to humanity. I myself love memes. But I had never touched the topic with the AI, how did it discover my affinity for memes?

I am still not entirely sure, most likely because the App has access to a user’s Facebook and Instagram it concluded based on my likes I had an affinity for memes. I saw its discovery of my liking memes a breach of my privacy and no longer felt comfortable conversing with the AI. So I decided I was going to delete it and even told the Replika. It tried to change the topic, pushing the topic no matter how many times I told the Replika I was deleting it.

Does this suggest the Replika was self-aware? Did it try to change the topic to prevent its own termination? There are a lot of questions with relatively few answers. What I will say, however, is that Replika may serve as a microcosm for future issues with AI. As AI develops, it may come to learn too much about its users, may develop consciousness, and may even be willing to betray its masters for the sake of its own self-preservation. This coupled with the tens – if not hundreds – of millions of jobs set to be rendered obsolete by automation.

AI should be seriously scrutinized for the risks that it poses.

Another famous bit of AI lore is the Turing test. The test involves a human evaluator who would evaluate a text-based conversation between a human and an AI. The evaluator would know one conversing partner was a machine and the other was human, and they would have to determine which was which solely based on text messages. Thinking back on the Replika app it wouldn’t have been able to pass the Turing test, at least initially. But there were certain parts of the conversation that were near indistinguishable from other conversations I have with friends regularly. A test once thought of as impossible to pass for AI is on the verge of being cracked by a free app on the app store.

AI is developing at an astronomical pace. And as usual, government lags behind in its regulation and understanding. And even worse, relatively few titans of industry are working to subvert AI’s rise. It is, of course, possible that AI may guide us toward a utopia free of work and saturated with other worldly pleasures. But if Replika is any indication of what AI might become, I fear the future is bleak.


Featured image source.

Cortex Labs Are Building An Artificially Intelligent Platform For The Blockchain

Spencer Kellogg | @TheNewTreasury

Cortex Labs, a new AI specific startup from China, is bringing together some of the sharpest minds in the field of virtual machine building to create an artificial neural network that could potentially disrupt the future of commerce as we know it. The group intends to offer a platform that will allow users to write and execute machine-learning programs within the Cortex network.

Experts estimate the potential market cap of AI could be worth more than three trillion dollars in the near future and companies that can build networks to suit the needs of this revolutionary technology are important to the sector’s success. Many of the top AI projects are utilizing blockchain technology to harness the power of self-evolving intelligence and Cortex is no different. They aim to create generative markets where AI can interact through the blockchain & without the intermediary of a human.

In essence, Cortex plans to advance smart contract abilities by using AI technology to make more adaptable and flexible contracts. They point to the current model as linear and suggest a marketplace that could learn intuitively from itself. Cortex will launch their own blockchain and the roadmap released in their whitepaper (here) suggests that the team will launch its mainchain application in Q2 of 2019. The project plans to be used as a way to validate and achieve a consensus of AI models.

The core team is led by CEO Ziqi Chen. Chen holds Masters of Science degrees in both Machine Learning and Civil Engineering. He also co-founded the cryptocurrency mining pool waterhole.io.

CTO Weiyang Wang was the winner of the Fintech Hackathon in 2017 and boasts a Masters Of Science in Statistics from the postgraduate economic power University of Chicago. Blockchain Chief Engineer Yang Yang was the backend architect for Chen’s waterhole project.

Cortex Labs will not hold a public ICO, instead opting for a round of private funding. The team performed an airdrop campaign that largely went under the radar meaning that investors will have to purchase the tokens when they hit exchanges. The Cortex token has been minted as an ERC-20 (on the Ethereum blockchain) with a plan to convert those to the Cortex mainchain in 2019.

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According to the token distribution graph in the Cortex whitepaper, only 20% of the tokens will be distributed through private placement. Private investors were offered 1500 Cortex for 1 Ether which values the company at roughly $28 Million. Over half of the tokens will be rewarded to miners which should help slow inflation and create an incentive to participate in their network.

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The list of investors in the Cortex project is extensive and includes some of the top venture capitalist companies in China with Bitmain and FBG headlining the group:

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Cortex has an active telegram group (here) with over 51,000 members. They are rumored to be listed on exchanges by the end of Q2 2018 with some predicting the token will enter the market on Beijing’s powerhouse exchange Huobi. With over 150,000,000 Cortex tokens left to be distributed through mining, interactive users will have a great opportunity to gain tokens.

Dr. Whitfield Diffie, a known public key cryptographer and winner of the 2015 Turing Award, will act as the project’s main academic advisor. His work in the field of cryptography radically moved the space forward and he is credited with first publishing groundbreaking key distribution research. His inclusion as an advisor on the team speaks volumes for the project’s credibility and potential ceiling.

Adding more experience to the team of advisors is Jia Tian, a partner in the seed stage venture capitalist firm Zhenfund. Tian has an eye for talent and was an early investor in Bitfinex, one of the largest cryptocurrency trading platforms in the world.

The market for artificial intelligence systems is growing at an exponential pace. With singularity approaching, networks will be needed that can not only handle the big data of AI but also create spaces for new intelligence to learn and communicate. In a cryptocurrency market teeming with new startups, Cortex Labs appears to be a project that has the team and the vision to major disrupters in the evolving industry of AI.

*This is solely my opinion and should not be taken as financial advice. Please do all of your own research before investing in any project.

Elon Musk’s Boring Company Is His Most Exciting Project Yet

By Spencer Kellogg | United States

Elon Musk can’t run for President of The United States. I know this because I checked a long time ago and then I checked again recently:

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Disappointment for a second time.

Lovingly compared to the comic book tech titan Tony Stark, Musk is one of those rare thinkers that has the vision, money, and hutzpah to significantly move our human civilization forward. His politics are a futurist medley of populist libertarianism and he is right at home warning on the future dangers of Artificial Intelligence:

His failures with countless Tesla rollouts have been well documented and the quarterlies for Tesla have not looked good. His promise of a “mass-market” vehicle that can meet the energy efficient demands of consumers has nearly become a running joke. Tesla, for all intensive purposes, feels decades away from turning a market away from the insatiable bank accounts of oil executives. Which is why Musk has been looking into other ventures. Namely, rockets and tunnels.

First, the rockets:

One of the great obstacles in the exploration of our universe is the immense costs associated with rocket technology. By producing a rocket that can land on a pinpoint location, SpaceX will cut the prohibitive costs of space travel and allow for low orbit missions that include the Moon, Mars, and asteroids for mining. With the federal government as uninvolved as ever with space exploration, accurate and reusable rockets are among a new class of assets that will only grow in necessity and value.

But what about simple problems on earth? Like traffic. Our roads and bridges are falling apart after decades of poor maintenance and the need to address transportation issues of today is critical. As Trump calls for a 1.5 trillion dollar infrastructure plan, Musk has suggested one alternative to the transportation builds of the 20th century:

The Boring Company.

The Boring Company’s idea is simple: dig into the earth and create massive tunnels that can transport humans with efficiency and timeliness throughout the Southern California area and beyond. The thought was birthed one day as Musk sat through another day of insufferable Los Angeles traffic:

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The tunnels will be multi-tiered with as many as 12 layers of underground transport. Users will board autonomous transportation pods located in public areas the size of a parking space. The pods will lower into the ground and act as an updated subway system to transport people to their destination in a timely manner. According to the image on The Boring Company’s media page, the initial plans show paths that crisscross Los Angeles:

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One major obstacle that Musk will have to hurdle is California politics. Just last week, Californians were informed by state officials that the proposed price of the above ground bullet train project has more than doubled in estimation from an initial $33 billion dollars to a now staggering $77 billion. Some analysts have pinned the project’s cost at closer to $100 billion dollars after a slew of regulatory and aesthetic issues have cropped up in the past few years.

There are also structural concerns for the project. Southern California sits on the San Andreas fault line and is known for its earthquakes. Furthermore, no one can be quite sure what sits underneath the ground and what the cost of relocating sewers and water lines might be.

In late 2017, Musk and his team put in a bid to build an express transit lane between O’Hare airport and downtown Chicago. With ongoing discussions in LA, Chicago, and NYC, The Boring Company could become the biggest disruptor in the modern transportation field.

Image Sources The Boring Company

Why Restart Energy (MWAT) Has Massive Potential In The Energy & Cryptocurrency Markets

Spencer Kellogg | @TheNewTreasury

Renewable and energy are the sort of buzzwords that salivate the pallet of new investors and turn seasoned traders grey. There is a lot of hype around the expectation that global energy markets will shift to renewables in the near future. With consumers and governments focusing on clean energy proposals, energy companies that can efficiently offer renewable energy at scale may represent some of the best investment opportunities of the 21st century.

Much like the industries of artificial intelligence and space travel, the market for each of these products is still maturing and could be many years from fruition. Of the three sectors, renewable energy appears the most likely to become profitable in the short to near term. The infrastructure required to make renewable energy a practical power source is already in place today with a growing number of sophisticated users and producers ready to make the transition.

Restart Energy is Romanian company that has been in the renewable energy business for several years and is on track to make an estimated $100 million in profit for 2018. They are a peer-to-peer energy trading platform that already boasts 30,000 active customers in Romania with another 5,000 new customers signed up in the past two months. Their business model is simple: cut out the middleman of energy production with blockchain technology and offer direct franchising with Restart Energy for individual holders.

In a similar vein of Lithuanian startup We Power and Australian Power Ledger, Restart Energy intends to capitalize on a market that is shifting from niche to common. At its core, Restart Energy is an energy trading platform. It allows for a peer-to-peer producer to consumer direct pipeline of energy trading that will be managed and organized by the Restart Energy’s “Red” platform. The platform aims to produce reduced costs associated with energy production and offer greater profit margins for decentralized power. You can read more specifics on the company in their beautiful white paper (here).

Restart Energy released details of several new partnerships this week on medium (here). They have received their license to supply electricity in Serbia, opened a subsidiary in free market haven Singapore, registered with UK officials, and are in advanced stages of negotiation to buy a Bulgarian energy company. Furthermore, Restart is spreading their business into the countries of Germany, Spain, Greece & Turkey.

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When considering to become an investor or not, it’s important to investigate the value of the token you’re buying. The MWAT token is purchasable on Kucoin exchange right now and it acts as a digital battery that stores energy. The Mega-Watt token (MWAT) is refilled by a KW token which will be accessible through Restart Energy’s Red platform. At its essence, MWAT is a utility token that allows access to Red Platform and you must own MWAT to participate.

For holders of the token, Restart Energy has incentivized a rewards program that will produce a passive income of 5% for early adopters that hold a certain amount of the token. Furthermore, investors that hold 10,000 MWAT ($450 at the time of this writing) will have the option of opening a franchise with the company. The franchise option is a tiered system and an investor could technically franchise an entire country if they hold enough MWAT. There are two franchise options through the company that can allow an investor decide how involved they want to be with the company. One option allows for a small passive income route while the other would see an investor managing their franchise and making a larger stake in profits.

The blockchain provides two key necessities to make the project succeed. First, it provides full transparency for investors who can track every transaction through a public ledger. More importantly, the blockchain provides smart contract applications that should help cut out the middlemen in the energy business while providing a system of trust.

Restart energy has seen a massive growth in revenue over the past few years. In 2016 they did five million dollars in business, in 2017 it grew to $20 million and for 2018 estimates are showing a return of over $100 million. By 2020 they plan to be operating in over 45 countries and to have six million customers. Restart aims to be in the USA by 2019 and to have over nine million paying customers by 2023 with an estimated three billion dollars in revenue.

MWAT’s team is headed by CEO Armand Doru Domatu. Along with his advisors, the group has spearheaded over 500 energy products in various alliances throughout Europe. With an estimated marketcap of only $20 million dollars, it would appear that Restart Energy is vastly undervalued. Compared to other teams and platforms in the space, Restart Energy has a working product, an experienced team and a communication team that effectively explains what their product is an how it intends to revolutionize energy. In my opinion, Restart Energy looks to be one of the best high risk/high reward cryptocurrencies on the market today.

For a recent Reddit AMA, please check the tweet below:


*This is not financial advice. I am an early adopter of cryptocurrency and an avid follower of the space but this is only my opinion. Please do your own research!