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When cryptocurrency prices surged towards the end of last year, the market grew to record highs. By early November, it was already at $185 billion. The $200 billion mark was around the corner. However, as the prices soared, with Bitcoin at the front of the pack, the number of offerings grew, flooding the market. Small companies added “blockchain” to their name and all over a sudden they too were soaring in stock prices. The “investor” pool got deeper as people around the world “logged into” every new cryptocurrency and put their money there. Many didn’t bother to do some due diligence — remember ? PonziCoin Now, even with the slump, Bitcoin is still a flooded market. But what percentage of those are sound investments? And with more people expected to join the bandwagon, are they following any credible investing advice? Experts offer their two cents: Just put It Into Bitcoin…? Studies have shown that people between 18 and 39 years of age are less likely to invest in stocks compared to previous generations. found that only a third of millennials have invested in the stock market against 51% and 48% of the previous two generations respectively. A study from Bankrate So, where are they investing? Turns out millennials are more receptive of cryptocurrencies than other generations. found that, if given $5,000 to invest in a single place, 12% of millennials between the ages of 18 and 34 would invest in cryptocurrency. Only 3% of those between 45 and 54 years of age share the same sentiments. Considering about , the answer on where and how to invest can help save a generation. Swell Investing 40% of people between 24 and 35 years have at least $1,000 in savings In an interview with CNBC Make It, 19-year-old Bitcoin millionaire, , “I’d just put it into Bitcoin. I think Bitcoin is the safest cryptocurrency right now.” The teenager, who currently has $3.4 million worth of Bitcoins at the rate of $8,512 a coin, bought his coins back when he was 12. Now, barely 7 years later, he’s a millionaire. He advises other young people to invest 10% of their income into Bitcoin. Erik Finman says More seasoned experts are however wary about investing in Bitcoin due to its volatility. Some have even gone as far as to call it “a fraud”. “Before investing, it’s important to check a few boxes first to ensure financial security in the event of a slump,” says Rob May, CEO of . “Most personal finance experts will suggest the 50–30–20 rule. That’s a safe starting point.” Botchain Understand It First At some point, the price of Bitcoin was so high that all some of us could do was wish we had invested earlier. But even then, some global bankers and experts were still warning investors against getting into the cryptocurrency business, the popular notion being that it was just but a bubble waiting to burst. Jamie Dimon, the CEO of JPMorgan Chase, is quoted , “It’s worse than tulip bulbs. It won’t end well. Someone is going to get killed.” Too many financial experts remain skeptical about the technology and the main reason is that they don’t understand it. So, they can’t invest in it, leave alone make predictions. saying As Jamie Smith, president of the Global Blockchain Business Council , regulators and investors around the world are just beginning to understand what cryptocurrency is. Clearly, a lot of speculation surrounds the blockchain. And if global bankers don’t understand the phenomenon, how much of a chance do retail investors have? says On why he’d never invested in Bitcoin, , “I get into enough trouble with the things I think I know something about. Why in the world should I take a long or short position in something I don’t know about?” Without getting into why a seasoned investor would not invest in the technology, an obvious lesson here is this: Don’t invest unless you understand. Warren Buffett says Invest Only If You Can Afford the Loss Investing in cryptocurrencies comes with a significant amount of risk. It’s highly speculative and volatile. Besides, it just got your attention — it’s still very early to determine how much disruption it will cause. So, how can you still invest? Banking expert, Ben Hung, says that cryptocurrency investments are worth the risk only if you’re investing money you’re prepared to lose. When asked if he was worried for the people joining the rush, , “Provided that people are investing in crypto with money they don’t mind losing a little bit of.” Hung says “Because depending on whether you look at crypto as a means of exchange of payment or a means of investment, people have different views of cryptos, regardless, the fundamental thing is that you don’t over pour your assets into it.” “Cryptocurrency is certainly a viable investment. But it’s not because its price has ‘been going up lately’,” says Jason Dekker, CEO of . “Investments made with that kind of thinking will be lost. I’d only invest money I’m prepared to lose every dime of. But first, I’d want to know all there is about the blockchain.” Gochain NOW is the Best Time to Invest Are you concerned about the slump? Does it spell the end of Bitcoin or cryptocurrency? It doesn’t. In fact, , a cryptocurrency expert, this is the perfect time to invest since the prices are lower. “Now, when everyone is saying it is over, that’s it, bitcoin is dead, for the 175th time. Now’s the time you start looking at it, on the buy side,” he says insisting that investors will have to remain cautious and avoid buying the cryptocurrencies when the prices are high. according to Brian Kelly Like all investments, this will take a lot of patience upon a smart and comprehensive strategy. When the value of Bitcoin goes up to $20,000 everyone’s all excited and talking about this new “currency” as they put in all their money. The true investor is known during the slump. That’s when they are staying put and buying. As Kelly says, the falling of the prices is, “incredibly healthy for the ecosystem. You shake out the weak hands. You get strong hands in there.” While cryptocurrency is a great investment opportunity, it can be a big scam to someone who doesn’t understand it or knows smart investment strategies.

How to Invest in Cryptocurrency According to Experts and 19-Year-Old Bitcoin Millionaire

At the kind invitation of Rob May and the Botchain team, I had the opportunity recently to keynote , an interesting conference in New York exploring the intersection of artificial intelligence and blockchain. Brains and Chains This is both an exciting and challenging topic, and the goal of my talk was to provide a broad introduction to kick things off, and frame the discussion for the rest of the day: discuss why the topic matters in the first place, and highlight the work of some interesting companies in the space. Below is the presentation, with some added commentary when relevant. Scroll to the very bottom for a SlideShare widget, if you’d like to flip through the slides. I’m coming at this topic from the perspective of a VC investor. My firm FirstMark has been active in both AI and crypto/blockchain. It’s certainly easy to poke gentle fun at the topic. AI (machine learning) and blockchain are both experimental and much buzzed about. AI reached a peak of hype in 2016–2017, blockchain in 2017–2018. Each trend individually could very well end up disappointing, and explorations at the intersection of both could prove fruitless. But if one looks at the history of computing, a massive new trend seems to appear every 10 or 15 years — silicon chips, the PC, the Internet, Web 2.0, etc. We are probably on the tail end of the current trend, which has been propelled by three concurrent phenomenons: social, mobile and cloud. Many of the giants we know today emerged from those. But the magnitude of those trends wasn’t always obvious, of course. Cloud computing, for example, seems obvious now, but if you rewind back to 2008, it was controversial, and called “marketing snake oil” by some. All in all, it took well over a decade to become the massive industry it is today. At first, new trends often seem both highly experimental and over-hyped, but over time they take hold, attracting ever more capital and talent, and gradually become the new paradigm. As per Amara’s law, the impact of new technology is often overestimated in the short run, and underestimated in the long run. The timing seems ripe for a new paradigm in technology to emerge. What will define and propel the the next big wave of computing innovation? There’s a rationale for making the argument that “AI, blockchain and the Internet of Things” is the new “Social, Mobile and Cloud”. Those trends are still very much emerging, but their potential impact is massive. What new giants will emerge from this paradigm? Just like social, mobile and cloud have fed off each other, those three trends have very interesting areas of overlap. I have discussed at least one example of , but there is a number of others. intersection between the Internet of Things and blockchain Today, I’ll focus on the intersection between AI and blockchain. One really interesting starting point is to observe that AI and blockchain are philosophically opposed in many ways, as nicely expressed by Peter Thiel and Reid Hoffman in a recent conversation. For example, AI is very much centralized — within a handful of companies, primarily Google, Apple, Facebook and Amazon (“GAFA”) and the large Chinese Internet companies, Alibaba, Tencent and Baidu. While some of the AI research is open sourced via academic papers, those companies have been able to attract top AI talent around the world, and most importantly, they have access to unprecedented amounts of data to train their AI algorithms. Those datasets are a massive competitive advantage and are closed to the rest of the world. The centralization of AI opens the door to all sorts of abuse. Reports of government surveillance in China abound, powered by computer vision and face recognition technologies. But just over the last few months, stories started surfacing in the US that echoed some of the concerns about China, demonstrating that this is a global concern. Beyond political issues, centralized platforms tend compete with the ecosystem that emerged around them. Read Chris Dixon’s brilliant essay “ ”. Why Decentralization Matters Blockchain emerged as a powerful response to political and organizational problems, rather than purely technical ones. Since many of the issues we discussed are political and organizational in nature, can it be leveraged as a foil against the pitfalls of AI? Could blockchain also help create better AI? Pioneers in the field have been exploring various ideas, ranging from a decentralized way to create AI to networks of bots and fully autonomous organizations run by AI. We’ll chat today about how blockchain can help AI, but it is worth noting that there is a number of ways AI can help blockchain — another interesting discussion for another day. The first big idea is to create a decentralized marketplace that would help create better AI. The high level idea is as follows: All of us (individuals, institutions) would be financially incentivized to provide our personal and professional data. Knowing it would be kept completely secure and private (through decentralization and secure computing), we’d feel more comfortable sharing sensitive data (spending, health information). Over time, the marketplace(s) would accumulate a lot more data, and higher quality data, than what GAFA has access to. On top of the data, machine learning experts would be incentivized to compete and highest performing models would get disproportionally rewarded. To explore how to build such a decentralized marketplace, let’s chat about how to decentralize the three key building blocks of AI: data, models and computing power. At this stage of the presentation, we’ll start providing examples of companies doing exciting work at the intersection of AI and blockchain. The space is very vibrant and fast-moving, and this is not meant to be an exhaustive list of all great companies and projects, by any means. Also worth noting: many companies in the space have ambitious plans to build a lot of pieces of the ecosystem, and many sound a little bit the same. Most of those projects are pre-launch, so it will take time for the dust to settle and see who actually does what in earnest. Let’s start with data. One important point: if we’re going to use the blockchain to store massive amounts of data, it will need to become a much better database than it currently is. Here’s BigChainDB, out of Berlin, building a scalable blockchain database. The chart is interesting in that it shows that there is little overlap between what a distributed database offers and blockchain technology. As a result, building a true database-grade blockchain is a challenging project. To help share data, another key infrastructure component is a protocol. Ocean Protocol has been doing pioneering work in the field, and for anyone interested in digging in, it’s worth reading just about everything its founder has written on the general topic of AI and blockchain. Trent McConaghy Computable Labs is also working on building a data market protocol, and this great by its CEO Roger Chen is well worth a read as well. piece Sometimes you need to create your own data for purposes of AI training — either because you don’t have access to the right data set, or because the use case you are training the AI is too new that the data simply does not exist. Snips, out of Paris, is using crypto economics to incentivize a network of workers involved in synthetic data generation. Let’s talk about the second building block of AI: models. For a decentralized AI marketplace to work, you need to be able to guarantee that whatever data is provided by individuals and companies is processed in a completely private manner. Enter secure computing. A good example is the OpenMined project, which includes a key focus on private machine learning, leveraging various secure computing techniques, including federated learning (championed by Google) and differential privacy (championed by Apple). Let’s switch to the third building block of AI: computing power. A lot of the recent progress in AI has been facilitated by a massive ramp in computing power, that resulted both from better leveraging existing hardware, and also building new high performance hardware specifically for AI (Google TPUs, etc). DeepBrain Chain is an interesting project that aims to share idle computing resources around the world. Its general philosophy is comparable to other projects such as Coronai, Hadron, Golem, or Hypernet, but DeepBrain Chain is more specifically focused on the type of computing power (and related hardware) necessary for the specific requirements of AI. Putting it altogether, you can imagine a fully decentralized AI marketplace where people provide their data, developers compete to provide the best machine learning models, and the whole system works as a self-reinforcing network that attracts more and more participants and creates better and better AI. The secret weapon here is really crypto-economics: the ability to create a mini-economy where participants accrue and exchange value through tokens. Because they incentivize people to participate in the network early, tokens help solve the cold start problem which has plagued so many network-building efforts in the past. The chart showin the slide was taken form Fred Ehrsam’s excellent Medium post: . The post describes well how a decentralized AI marketplace could work. Blockchain-based Machine Learning Marketplaces Of course, the typical opportunities and challenges of any marketplace apply to this discussion. A decentralized marketplace may be a very novel way to create AI, but whatever comes out of it will still need to reach product / market fit and solve a real problem for it to be commercially successful. From that perspective, verticalized approaches (industrial, genomics, finance, etc.) are particularly interesting. Now let’s take this discussion one step further. Let’s assume that, in part through decentralized AI marketplaces, AI continues to flourish and accelerate. We would have the ability to create many, many types of AI for just about every task. What would this look like, and how would it work? Blockchain can provide an interesting organizational model to help the various AI bots cooperate in a transparent manner. is an example of a company working on building a network to build bots and have them cooperate in an organized manner. Fetch An example of bot cooperation could be a trip: you’d have one bot buy the flight ticket, and if there’s a delay, another bot could predict the likelihood of misconnection, suggest another route, and the first bot could change the reservation. All of this would happen automatically in real-time behind the scenes and completely eliminate friction for us humans. is another interesting example — a very ambitious, complex project with many moving pieces. As a showcase for how various AIs could cooperate to create a single brain, they’ve developed Sophia, a Hanlon robot that is powered by SingularityNET. The explainer is pretty wild (and reminiscent of WestWorld). SingularityNET video If we’re going to have a world where we rely on an army of bots to perform various tasks, there needs to be an infrastructure to keep things transparent and under control. This is the mission of , a new company created by Rob May, also CEO of Talla and organizer of this Brains and Chains conference. Botchain Taking things even further than bot cooperation, you could imagine how entire systems could run completely autonomously, powered by AI.This is the idea of a decentralized autonomous organization or “DAO”. Many people have heard of “the DAO”, the infamous investor-directed venture capital fund that got hacked in 2016. The idea of an “AI DAO” goes beyond what the DAO was, in that it could be a decentralized organization entirely run by machines, with no or limited human intervention. For example, fast forward way into the future and you could imagine a completely decentralized version of Uber with self-driving vehicles, run by AI. There would be one gigantic feedback loop where the system would constantly learn how to dispatch cars, transport people efficiently and handle all logistics, combining many skills and complexities into a self-running experience. One scary thought with this AI DAO concept is that it is unclear how you could stop such an organization if it is truly decentralized and autonomous, in the event it went haywire. It’s not like there’s one plug you can pull to turn off the computer… In conclusion, a lot of the above is fascinating, but highly experimental. Most of the projects I mentioned are pre-launch, and it’s unclear whether they will be able to pull off the ambitious goals they are pursuing. At the same time, a lot of foundational work is being done, and the combination of AI and blockchain could create very powerful technologies, at speeds that hard to fully predict. Some of those advances may create undesirable results, so now is certainly a good time to start thinking about the implications. THANK YOU: Kyle Samani of Multicoin Capital and Roger Chen of Computable Labs for providing feedback on this presentation; Rob May for inviting me to speak at the event; Demi Obayomi of FirstMark for the heavy lifting on the presentation. Here’s the SlideShare:

AI & Blockchain: An Introduction

I haven’t met anyone yet who was not wowed by the Google Duplex demo. If you haven’t seen it, please . This technology is going to create a very interesting problem for society. People are going to wonder “Am I talking to a person or a bot?” go watch I wrote about this once before, in a post titled . We know that hackers, spammers, and scammers always adopt new technology to serve their ends, and AI will be no exception. In fact, we already see problems. Emerging Problems In AI And Blockchain As a Solution Since has been working in the AI space for several years now, we realized these problems early on. To solve the problems created by Google Duplex, we need a decentralized AI registry, and blockchain is a perfect solution for that. It’s why we pioneered along with our partners. One of them even just a few weeks ago. Talla Botchain wrote about this This is going to be a really interesting issue for the world, and Botchain is building a decentralized ledger for identity and audit of AIs to solve it. We have started to open source some code, but we would love your help. If you want to contribute to the project, you can join the core development email list . here AI is going to make the world more confusing. Help us write the rules that bots have to play by to keep it more manageable.

Google Duplex: Why We Need Decentralized AI Identity

Each week brings another announcement on the progress of artificial intelligence. The rapid pace of growth has raised concerns about whether or not AI will take jobs, and how we can control possible rogue AIs should they arise. While I am generally an optimist about where AI will lead, I do understand the concern. In the past year, we have seen many examples of AI failures or systems gone awry. This piece looks at 3 big problems in AI that we will have to face in the future. The first is spoofing — as AIs proliferate, how do we identify them? The second is failure — how do we know when an AI fails and what can we do when it does the wrong thing? The third is compliance — how do we control AIs when they might go rogue or cause some sorts of mischief? Why Do AIs Fail? What makes artificial intelligence programs so different than traditional software programs is that they follow probabilistic models instead of rules. Traditionally, software has been written as a series of rules that says “IF this thing happens, THEN do this other thing.” But with AI (and deep learning in particular), that isn’t how it works. AI models tend to give probabilistic answers like “IF this happened then I’m 92% sure THIS is the next step.” The fact that they deal in probabilities rather than strict rules means they are much more flexible than rules based software, but there is no free lunch and so the tradeoff is that occasionally these AIs are wrong. Reinforcement Learning, which is the process of re-training the AI after every action to update what it has learned, has failed spectacularly in the past 12 months on a public stage, several times. The two most interesting example are from OpenAI about a reinforcement learning model that was learning to play a video game and learned the entirely wrong way to go about it. The second is which, when launched on Twitter to “learn,” quickly became racist and misogynistic. Imagine if these were AI processes you launched at work that went awry in similar ways. this blog post Microsoft’s Tay Bot If You Are An AI, Who Are You? As long as there has been technology, there has been people using it for scams. We have seen people spoof websites, spoof emails, and now spoof bots. How can we tell who owns an AI, how it has been trained, and what it is authorized to do? The very flexibility that means AIs can go do new things also means the people and other AIs involved in those new things have no way of knowing for certain the identity of the AI they are dealing with. Should We Be Afraid of Rogue AI? How do we keep AI from going rogue? Nick Bostrom’s book “SuperIntelligence” and warnings from luminaries like Elon Musk and Stephen Hawking have raise the profile of the possible outcome that AI is out to get us. Control of autonomous agents that can “think” for themselves becomes problematic if the infrastructure they run on looks much like what we have today. How can we put some chains around such AI agents to make sure they don’t go off track? Blockchain As A Solution Botchain, a protocol built on top of a blockchain, is a network supported by many of the top AI and bot companies to solve some of these problems. Botchain provides a decentralized identity for AI agents that is independent of the underlying platform it runs on. Every major AI agent can register to be universally identified, giving a solution to AI identification similar to the way website certificates work today to validate website ownership. Botchain also allows every AI agent to write regular hash functions of their activity to a blockchain so that it is immutable and inspectible by those with the encryption keys. This proves what the AI did, and when it did it, and makes sure bad actions can be discovered, analyzed, and rectified because blockchain immutability makes it impossible to “cover your tracks” and delete the data. And finally, the consensus mechanisms of blockchain can make sure any possible rogue AIs stay under control. By having a public record of tasks an agent can do, that have to be verified by multiple blockchain nodes, we can make sure an AI doesn’t overstep its limits. Despite the concerns that AI may cause damage in the future, emerging technologies like blockchain have the ability to transition to an AI future with more confidence. If you are an AI company that would like to participate in the Botchain ecosystem, you can sign up . here

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