Vendorspace is a job marketplace for vendors to find events to work with

Vendorspace

Artificial Intelligence, Data & the Blockchain I’ve been watching how the Artificial Intelligence (AI) area has been embracing the blockchain lately. What can result from fusing AI, data and the blockchain can change the way both AI and the blockchain work altogether as the old saying goes “The whole is greater than the sum of its parts.”― Meanwhile, Google CEO Sundar Pichai says and while others predict blockchain technology can . Lets see what happens when you mix the two by taking a look at 3 of my favorites… Aristotle . the future of Google is AI change the world Doc.ai is a conversational platform for on-demand, quantified biology. Using conversational AI, their advanced natural language dialog system is designed for vertical medical domains, and can generate insights on blood, genomics, microbiome, environmental and anatomical data. According , they believ they are “entering a world where artificial intelligence and natural language understanding are bringing unmatched benefits to healthcare.” Doc.ai Doc.a They are designing a platform for participants to use to do deep data trials, by collecting the data for themselves or their loved ones. In this massive distributive system participants can unite and collaborate with individuals, with groups, with academia and with organizations and corporations. The Neuron Network will evolve in 3 phases: robo-doctor is designed to guide users to collect their full biological profiles, ready for predictive models. Data Collection doc.ai Users will be able to connect this profile with AI researchers and data scientists to model their condition individually or in group. Data Model Users will be able to deploy a biowallet, so their information (stored on their devices) can be sold in the network. Data transactions Cindicator creates the social and technological infrastructure needed to make effective decisions under the conditions of the high uncertainty of the new economy. By combining a large number of diverse financial analysts and a set of machine-learning models into a single system, we are developing a Hybrid Intelligence infrastructure for the efficient management of investors’ capital in traditional financial and crypto-markets. Cindicator cuts through this centralization bias by aggregating opinion from a wide range of diverse forecasters from different countries with different professional backgrounds, with different personal experience. The results, which like a prediction market are always more accurate than even the most expert opinion, are sold to hedge funds and traders looking for an edge. Cindicator Vectorspace Minimizing risk associated to cryptocurrency investing is what the platform offers by enabling anyone to create and trade custom ‘Token Baskets’. They have a product called ‘ ’ that is in use today in terms of generating unique customized token baskets based on trends in social media, global and local search trends and just about anything else you can think of. Vectorspace Crypto Discover According to the mission statement: “The Vectorspace AI platform enables dynamically generated intelligent “token baskets” based on user-selected trends that exist in search, social media and news. The reason we’ve included the word ‘intelligent’ is based on the ability for these baskets to determine for themselves whether or not to include additional cryptocurrencies or components from related baskets that may increase overall returns. Baskets that interact this way with one another will conduct these kinds of transactions between one another using the Vectorspace utility token, VEC which is also required to dynamically generate baskets. Components within the dynamically generated baskets can be made up of cryptocurrencies along with other new innovations for stores of value in the cryptocurrency space that may arise in the future. Traders, investors and funds can optimize basket returns by applying filters such as technical analysis indicators and custom algorithms. Human researchers and curators of baskets are allocated VEC for their work in this area. The VEC utility token can be used to purchase blocks of dynamic basket generations in addition to subscriptions to higher level features offered by the platform including transacting baskets as trades on exchanges. VEC utility tokens are awarded to creators of top performing baskets which are displayed on a leader board each month.” The team has also released something I’ve never seen before called ‘real-time interactive infographics’ for cryptocurrencies. Here’s . Along with this they have a I recently installed that allows you to send it any keyword or URL and it will return a basket of related cryptocurrencies. an example slack bot You can get an invite to their pre-ICO by signing up and their whitepaper is located . here here Conclusion It would seem safe to predict that companies providing a platform, ecosystem or data engines that power other AI companies carry a unique and powerful value proposition. Much of this has to do with building in cryptocurrency-based data transaction networks between machines. It’s likely that groups executing in this area will continue to take on momentum as more investors begin to discover this unique opportunity for big gains with companies fusing AI, data and the blockchain… You might also be interested in ‘ ’ How-To Guide: Purchasing ICO Tokens

Unpacking 3 Cryptocurrencies: Doc.ai, Cindicator & Vectorspace

I finally beat the S&P 500 by 10%. This might not sound like much but when we’re dealing with large amounts of capital and with good liquidity, the profits are pretty sweet for a hedge fund. More aggressive approaches have resulted in much higher returns. It all started after I read a paper by Gur Huberman titled “ ,” (with Tomer Regev, Journal of Finance, February 2001, Vol. 56, №1, pp. 387–396). The research described an event that occurred in 1998 with a public company called EntreMed (ENMD was the symbol at the time): Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar “A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed’s stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday. It closed above 30 in the three following weeks. The enthusiasm spilled over to other biotechnology stocks. The potential breakthrough in cancer research already had been reported, however, in the journal , and in various popular newspapers ~including the Times! more than five months earlier. Thus, enthusiastic public attention induced a permanent rise in share prices, even though no genuinely new had been presented.” Nature information Among the many insightful observations made by the researchers, one stood out in the conclusion: “[Price] movements may be concentrated in stocks that have some things in common, but these need not be economic fundamentals.” I wondered if it was possible to cluster stocks based on something other than what’s usually used. I started digging around for datasets and after a few weeks describing strength of “known and hidden relationships” between stocks and of the Periodic Table designed by . I found one that included scores elements Vectorspace AI Having a background in computational genomics, this also reminded me of how relatively unknown the relationships are between genes and their cell signaling networks. However, when we analyze the data, we begin to see new connections and correlations we may not have been able to predict previously: Expression patterns of selected genes involved signaling pathways for cell plasticity, growth and differentiation — https://www.researchgate.net/figure/263706740_fig4_Expression-patterns-of-selected-genes-involved-signaling-pathways-for-cell-plasticity Equities, like genes, are influenced via a massive network of strong and weak hidden relationships shared between one another. Some of these influences and relationships can be predicted. One of my goals was to create long and short clusters of stocks or “basket clusters” I could use to hedge or just profit from. This would require an unsupervised machine learning approach to create clusters of stocks that would share strong and weak relationships with one another. These clusters would double as “baskets” of stocks my firm could trade. I started by downloading the . The dataset is based on relationships between elements in the periodic table and public companies. In the future I’d like to work with cryptocurrencies and create baskets similar to what these guys are doing but that’s a future project. dataset here here Then using Python and a subset of the usual machine learning suspects — scikit-learn, numpy, pandas, matplotlib and seaborn, I set out to understand the shape of the dataset I was dealing with. (To do some of this I looked to a Kaggle Kernel titled “ ”. Principal Component Analysis with KMeans visuals Output: a quick view of the first 5 rows: A Pearson Correlation of concept features. In this case, minerals and elements from the periodic table: Output: (ran against the first 16 samples for this visualization example). It’s also interesting to see how elements in the periodic table correlate to public companies. At some point, I’d like to use the data to predict breakthroughs a company might make based on their correlation to interesting elements or materials. Measuring ‘Explained Variance’ & Principal Component Analysis (PCA) Explained variance = (total variance - residual variance). The number of PCA projection components that should be worth looking at can be guided by the Explained Variance Measure which is also nicely described in Sebastian Raschka’s article on Principal Component Analysis: http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html Output: From this chart we can see that a large amount of variance comes from the first 85% of the predicted Principal Components. It’s a high number so let’s start at the low end and model for just a handful of Principal Component. More information on analyzing a reasonable number of Principal Components can be found . here Using scikit-learn’s PCA module, lets set n_components = 9. The second line of the code calls the “fit_transform” method, which fits the PCA model with the standardized movie data X_std and applies the dimensionality reduction on this dataset. Output: We don’t really observe even faint outlines of clusters here so we should likely continue adjusting n_component values until we see something we like. This relates to the “art” part of data science and art. Now lets try the K-means to see if we are able to visualize any distinct clusters in the next section. K-Means Clustering A simple K-Means will now be applied using the PCA projection data. Using scikit-learn’s KMeans() call and the “fit_predict” method, we compute cluster centers and predict cluster indices for the first and third PCA projections (to see if we can observe any appreciable clusters). We then define our own color scheme and plot the scatter diagram as follows: Output: This K-Means plot looks more promising now as if our simple clustering model assumption turns out to be right, we can observe 3 distinguishable clusters via this color visualization scheme. Of course, there are many different ways to cluster and visualize a dataset like this as shown . here Using seaborn’s convenient pairplot function I can automatically plot all the features in the dataframe in pairwise manner. We can pairplot the first 3 projections against one another and visualize: Output: Building Basket Clusters How you fine tune your clusters is up to you. There’s no silver bullet for this and much of it depends on the context in which you’re operating in. In this case, stocks, equities and the financial markets defined by hidden relationships. Once you’re satisfied with your clusters and have set scoring thresholds to control whether certain stocks qualify for a cluster you can then and trade them as baskets or use the baskets as signals. The list of things you can do with this kind of approach is largely based on your creativity and how well you might be able to optimize using deep learning variants to optimize the returns of each cluster based on which concepts to cluster or data points such as the size of a company’s short interest or float (available shares on the open market). extract the stocks for a given cluster You might notice a few interesting traits in the way these clusters trade as baskets. Sometimes there’s divergence from the S&P or general Market. This can offer opportunities for arbitrage based essentially on ‘information arbitrage’. Some clusters can correlate to . Google search trends It might be interesting to see clusters related to materials and their supply chain as mentioned in this article: “ ”. Using the dataset, I only operated on the feature column labels: ‘Cobalt’, ‘Copper’, ‘Gallium’ and ‘Graphene’ just to see if I might uncover any interesting hidden connections between public companies working in this area or exposed to risk in this area. These baskets are also compared against the returns of the S&P (SPY). Zooming in on 10 materials and their supply chains By using historical price data, which is readily available at outlets like , , or Yahoo Finance, you can then aggregate price data to generate projected returns visualized using : Quantopian Numerai Quandl HighCharts The returns I gained from the cluster above beat the S&P by a nice margin, which means you would have approximately an extra 10% over the S&P annual. I’ve seen more aggressive approaches net close to 70% annual. Now I have to admit that I do a few other things that I have to keep black-boxed due to the nature of my work, but from what I’ve observed so far, at least exploring or wrapping new quantitative models around this approach could turn out to be quite worth it and with the only downside being a different kind of signal you could pipe into another system. Generating short basket clusters could be more profitable than long basket clusters. This approach needs its own article and before the next Black Swan event. Getting ahead of parasitic, symbiotic and sympathetic relationships between public companies that share known and hidden relationships can be fun and profitable if you’re into machine learning. In the end, one’s ability to profit seems to be all about how clever they can get in coming up with powerful combinations of feature labels, or “concepts”, when generating these kinds of datasets. My next iteration on this kind of model should probably include a separate algorithm for auto-generating feature combinations or unique lists. Perhaps based on near real-time events that might affect groups of stocks with hidden relationships that only humans, outfitted with unsupervised machine learning algorithms, can predict. Gaëtan R., gaetanconsulting@gmail.com — Financial Data Consultant — Geneva Switzerland

Generating Alpha with Vectorspace AI NLP/NLU Correlation Matrix Datasets: Equities vs The Periodic…

How to use a Gatsby Static Site to Manage Paid Subscriptions 

On January 3rd 2019, Bristol-Myers Squibb ( ) Celgene ( ) for $74 billion. CELG rose instantly overnight from 66.64 to 87.86 per share, giving it a 31.8% gain. Although most professionals missed out on these gains, there were other hidden opportunities to be had, if you knew where and how to find them. BMY acquired CELG In this article, we’ll be discussing a group of equities that also rose in value but not immediately. This group has relatively unknown and indirect relationships to CELG. The delay in their rise was sufficient enough that it would have allowed for funds, traders or investors to take positions, and then profit. This is an example of information arbitrage in the financial markets. It happens from time to time and when it does, most people miss the opportunity. This also relates to a paper titled “ ,” (with Tomer Regev, Journal of Finance, February 2001, Vol. 56, №1, pp. 387–396). The research described an event with a company called EntreMed (ENMD was the symbol at the time): Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar “A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed’s stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday. It closed above 30 in the three following weeks. The enthusiasm spilled over to other biotechnology stocks. The potential breakthrough in cancer research already had been reported, however, in the journal , and in various popular newspapers ~including the Times! more than five months earlier. Thus, enthusiastic public attention induced a permanent rise in share prices, even though no genuinely new information had been presented.” Nature Among the many insightful observations made by the researchers, one stood out in the conclusion: “[Price] movements may be concentrated in stocks that have some things in common, but these need not be economic fundamentals.” Capturing information arbitrage opportunities can be done using advanced techniques in the area of (NLP/NLU). This also includes processing of correlation matrix datasets based on data such as public company profiles, encyclopedias, peer-reviewed scientific literature, news and patents located . Natural Language Processing and Understanding here Using a NLP/NLU correlation matrix dataset of US publicly traded equities, we generated a cluster (or basket) of companies that have relationships to CELG based on symbiotic, parasitic and sympathetic . latent entanglement What we found, with the top five scoring equities, were unique opportunities to profit ahead of the market. We’ve produced the following slides with charts to show where gains should have been locked in. Although not all equities performed, it’s clear to see where information arbitrage opportunities existed after the CELG acquisition. Especially pronounced movers were , and in relation to CELG. The benchmark used to compare against for the first set of charts is the S&P 500 and for the second set of charts, the IHE (iShares Pharma ETF). AGIO ADRO EPZM Benchmark: S&P 500 1. AGIO: 2. ADRO: 3. SHPG: 4. XLRN: 5. EPZM: Source: [ ] PDF Benchmark: IHE (iShares Pharma ETF) 1. AGIO: 2. ADRO: 3. SHPG: 4. XLRN: 5. EPZM: Source: ] [ PDF You can query the dataset used to generate the CELG basket . There’s also an API available on the same page. here The resulting output: Conclusion The basket ( , , , & ) generated by the dataset included many more equities, but as stated earlier, we chose to compare using the top five scoring symbols. AGIO ADRO SHPG XLRN EPZM The delayed reaction of , and is the result of a delay in the way the market absorbs information. AGIO ADRO EPZM We don’t know how many people missed out on these trades but one thing we do know is that the movement in CELG was nowhere near being priced into , and efficiently. This left an opportunity for a lot of money to be made for those that uncovered these relationships early. AGIO ADRO EPZM This means that when the news came out that CELG was going to be acquired for $74 billion, one could have generated a basket of equities with hidden relationships to CELG, positioned in them ahead of the market and profited from a rising tide created by CELG. As we continue to produce our correlation matrix datasets for traditional public companies and cryptocurrencies based on NLP/NLU, we’ll make them available, along with customized versions, to any holders of our token, . For more information feel free to reach out to us on or at anytime! VXV telegram vectorspace.ai

Generating Alpha from Information Arbitrage in the Financial Markets with NLP Datasets: 水涨船高

When you combine a great team, strategy, technology, product-to-market fit and track record of execution, you’ve got the ingredients for a great success. In reviewing a number of cryptocurrencies to include in our groups fund, part of my due diligence process involves making sure that cryptocurrency groups qualify in at least 4 out of 5 on this list: Team Strategy Technology Product-to-Market fit Track Record of Execution When it comes to meeting the criteria above, there should be few exceptions. Fundamental questions arise around each of these points such as: Team How long have the team members known one another? Have they worked together in the past? If so, for what period of time and through what kinds of circumstances? Strategy Do they have a well thought out strategy? Is their strategy too similar to that of their competitors. Do they know who their competitors are? Technology Is their technology unique or hard to duplicate? Is their approach scalable? Do they have any ‘ ’? technical debt Product-to-Market fit Since most early cryptocurrency groups are operating with a whitepaper alone, you’ll most likely have to do your own product-to-market fit for them. If they have a working product, ask them to see any customer feedback they can share. Track Record of Execution Does the team or any of its members have a track record that shows they’ve accomplished or executed anything to completion in the past? If so, how was it received? What was the projects impact, level of innovation and creativity? Here are a select group of cryptocurrencies that meet most of the criteria mentioned above and that might also be worth positioning in sooner than later before they list on larger exchanges: ( ) Current Price: Quant Exchange IDEX: QNT $1.24 Site: https://www.quant.network/our-technology/ From their site: “Quant Networks solutions help healthcare organisations by limiting the complexity of adopting blockchain technology.” That quote is already sets the tone for a valuable crypto. The most common problem with user adoption in crypto is the complexity. It really is just too complicated for the average non-technical person to jump through all the hoops to trade and invest in crypto. Quant is here to solve that! They also intend to advance science which is another significant indicator that QNT is a high quality crypto: “This in turn will improve their operational inefficiencies, so they can instead focus on what truly matters — helping patients and advancing science.” QNT has also recently joined the the (GLBC), composed of more than 100 large companies, law firms, software companies and universities focused on developing standards and compliance guidelines for blockchain and smart contract technologies in the business of law. More on this . Global Legal Blockchain Consortium here Given Quant’s reasonable total supply of 45M, our firm see’s Quant as one of the top 10 runners of this year. Time to load up! — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — - ( ) Current Price: Vectorspace Exchange IDEX-only: VXV $0.45c Site: https://vectorspace.ai These guys actually have a product called QuantBot, similar to the company above! What’s interesting about VXV here is that they are doing an AMA in front of 16 million readers of the science community at this Sunday. This is a real crypto with real technology folks. More on the AMA can be found here — WOW! reddit.com/r/askscience vectorspace.ai/reddit-ama.html They have an API here v that uses a “wallet-enabled” API key which means everyone will have to buy VXV on the open market! ectorspace.ai/api.html Their partners include , the US Dept of Energy (DOE), Google and a reddit data engineering company. Lawrence Berkeley Lab pushshift.io, From their site: “The datasets are correlation matrices engineered with ‘feature attributes’ or continuously valued distance calculations. These ‘feature vectors’ are related to today’s ‘word embedding’ methods used in Natural Language Processing (NLP). Resulting feature vectors consist of scored and ranked relationships between entities and surrounding human language that define their contexts. What we do is not magic but basic and fundamental to any machine learning effort, however, as Andrew Ng put it, “Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine learning” is basically feature engineering.” — Machine Learning and AI via Brain simulations. We’ve had the opportunity to operate in different knowledge domains such as finance where we’ve developed datasets that allow for context-controlled correlations of sympathetic, parasitic and symbiotic known and hidden relationships between entities such as public companies correlated to the periodic table of elements, cryptocurrencies, human genes, diseases, phytochemicals, pharmaceuticals and other entities. So far we’ve received about 350 custom dataset requests in this area.” VXV can go beyond the moon folks. — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — Aeternity (AE) Current Price: $1.07 Site: https://aeternity.com In very simple terms, æternity is a new blockchain platform, but it is also a vision for the future. This vision is being realized by a global community of believers, convinced in the disruptive potential of scalable, public blockchain technology. It is a movement that aims to propose alternatives to the existing governance, economic and financial intermediaries. From a technological point of view, æternity is a public, open-source, blockchain-based distributed computing and digital-asset platform that builds upon decentralized cryptographic P2P technology. The platform is open to anyone, anywhere in the world to use. For more detailed information, take a look at the dedicated to æternity or visit the . Wikipedia article GitHub page AE are the access tokens to the æternity blockchain and act as a unit of account for the resources spent on æternity. AE tokens are the energy (or “crypto-fuel”) used to power any application implemented on the platform. Every action (and even inaction) on the æternity platform costs something, even if only fractions of cents. AE tokens are the fuel for the new types of trustless apps (or æpps) that will be enabled by æternity. For more information regarding applications, visit the or visit the . æpps website æpps FAQ Aeternity is certainly a top performer! — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — - ) Current Price: Aurora DAO (Exchange IDEX: AURA $0.07c Site: https://auroradao.com/ What is Aurora? Aurora is a suite of DAPPs and protocols that make up a fully decentralized banking and financial network. The components of Aurora support the boreal, a decentralized stablecoin, and together enable global banking in the new currency. Revenue from the various components of Aurora will be used both to increase the asset reserves backing the boreal, allowing Aurora to expand boreal banking, and to provide economic incentives that ensure the system runs as designed. IDEX will be the first exchange to operate on the Snowglobe network, transitioning from a semidecentralized to a fully decentralized exchange. All fees from IDEX are remitted to the Aurora reserves. As the reserves grow Aurora can increase the value of outstanding loans, generating additional revenue for the Aurora network. Fees from the Aurora banking system will then flow back to those who stake the AURA token and provide the economic foundation that holds Aurora and Snowglobe together. Similar to other blockchain networks, Snowglobe requires a well-designed incentive structure to ensure that the integrity of the order book and transaction sequence are not compromised. Aurora has its own native network token, AURA, that aligns the interests of Aurora and the Snowglobe operators. All of the revenue from Aurora is used to compensate those who stake their AURA and provide security for the Snowglobe network. AURA staking aligns the economic interests of the operators with the health of the network, and makes it extremely costly for any would be attacker to disrupt operations. Prior to the launch of Snowglobe, all revenue from IDEX will be deposited into the boreal reserves. This revenue will be collected for future use in aligning economic incentives with AURA holders after the Snowglobe protocol is live. In the event that there is not enough revenue from Aurora to provide sufficient security for the platform, fees from IDEX may be used directly to enforce security in the system, bypassing the remittance to Aurora reserves. AURA definitely has the right stuff! This one is on our watchlist for double digits! — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — - ( ) Current Price: TomoChain Exchange IDEX: TOMO $0.29c Site: http://tomochain.com TomoChain is interesting because they are already up and running with a real blockchain product and getting great reviews similar to QNT and VXV. From their site: “The Tomochain testnet 2.0 launch is a great proof marking a major step toward our mission to be a leading force in building the Internet of Value. Tomochain is The Most Efficient Platform for Token Economy, designing its infrastructure and working to create an alternative financial system which is more secure, transparent, efficient, inclusive and equitable for everyone.” I like this valuable piece of information found in their whitepaper as it also validates their current product releases: “A consensus protocol is considered live if it can eventually propagate and make valid transactions onto the blockchain [12]. An occurrence of a liveness fault is when transaction omission, information withholding, or message reordering, among a number of violations are observed. This type of fault is unlikely to happen in TomoChain because the block creation masternodes list is ordered in a predetermined way for each epoch, thus if even an attacking masternode omits some transactions, the latter will be processed and validated by the next honest masternode in the next block.” Conclusion There’s a part of the due diligence process that’s more of an art than a science. It’s when you include another valuable but less tangible component, namely the Story. There was once a time when our fund was solely focused on traditional stocks and some of those were but that is of course, a another story. called ‘story stocks’, These plays have massive upside and positioning on the ground floor here before they list on larger exchanges can result in tremendous profit! Happy Trading!

Real Cryptocurrencies Set to Outperform in the Short & Long Term

( ) has set up a working group to investigate ways the world’s largest asset manager can take advantage of the fast-growing cryptocurrency market despite its chief executive lambasting the bitcoin sector. BlackRock BLK The $6.3tn investment powerhouse has created a team from different parts of the business to investigate cryptocurrencies and their underlying infrastructure, blockchain, according to two people familiar with the matter. The working group, which includes New York multi-asset investment strategist Terry Simpson, will examine whether BlackRock should invest in bitcoin futures, one of the people said. Sources said the team of experts is also looking at what BlackRock’s competitors are doing with cryptocurrencies and how that could impact its business. The working group will present its findings to senior management. A BlackRock spokeswoman said the company had been “looking at blockchain technology for several years” but declined to comment on cryptocurrencies specifically. The creation of the working group marks a turnaround for the company after Larry Fink, the chief executive, said late last year that bitcoin was merely “speculative” and that the only reason it thrived was due to its anonymity. “It is an instrument people use for money laundering,” Fink said. At the time, the CEO said bitcoin and other cryptocurrencies were “far from” being an opportunity for institutional investors, adding that none of BlackRock’s clients wanted to invest in it. BlackRock is not the first to change tack. Last year, JPMorgan’s CEO Jamie Dimon called cryptocurrencies a “fraud” and said he would sack any banker he caught trading them. The bank, however, later moved its head of fintech into a new role to explore the use of digital currencies. Digital currencies continue to polarise the City with some of the largest names in finance including Mark Mobius and Davide Serra and economists Joseph Stiglitz and Nouriel Roubini openly speaking out against them. However, today firms are increasingly interested in getting into the asset class. Fidelity Investments, which manages $2.4tn in assets, has begun a hiring spree, while Goldman Sachs said in May that it is setting up a crypto trading desk. Long time blockchain and cryptocurrency fund Bitcoin Investment Trust ( ) have been long-time managers of digital asset companies. GBTC A few examples of cryptocurrencies being targeted by new crypto and AI-focused funds include dydx or Vectorspace AI. According to another , “In May, the Swiss bank UBS said it was moving 80 staff into a new artificial-intelligence division, dubbed the Strategic Development Lab, which is dedicated to defending its fixed-income trading turf against hungry fintech startups. source Later the same month Deutsche Börse, the German stock exchange, said it had earmarked €270m for investment in big data analysis, blockchain, robotics and AI. Meanwhile, the City Corporation’s top envoy to the EU, Jeremy Browne, said late last month that he thinks AI will be more disruptive to UK financial firms than Brexit, in the long term. Quant Insight and its backers are betting that banks and asset managers will also tap into new tech by buying in services like the one it offers. The analytics firm said it has grown customer numbers by 50% this year, and has added new staff to sell its services in Asia and the US. It is also working with “major banks” on creating bespoke indexes and equity baskets — groups of stocks that share certain characteristics and are likely to move in a similar way in response to given macro events.” , a well-known Silicon Valley venture capital firm, has raised $1.8 billion to invest in startups from cryptocurrency to beauty supplies, hoping to build on its streak of lucrative bets in companies such as Snap Inc ( ), the firm’s partners told Reuters. Lightspeed Ventures SNAP At the same time, another notable VC firm stated: Andreessen Horowitz “We believe that just as the last three megatrends — mobile, social, and cloud — intersectedand reinforced each other, so will the next three megatrends — next-gen computing devices, AI, and crypto.” — a16zcrytpo.com It’s an exciting time for funds of all sizes to prepare for a what could be a never-before-seen change in financial history and opportunity.

$6.3T BlackRock Targets Cryptocurrency & They’re Not the Only Ones

When the internet first made it’s presence known to the masses in the mid 1990’s, the public was understandably skeptical. Sure, people understood intuitively that computers were going to change the world, but up until that point the average person was not interacting with, or being effected by them on a daily basis. Or at least so they thought. The reality is that computers and the proto-internet were already streamlining aspects of the military, government, and mercantilism starting by the 1960’s. But despite those early use cases, in the 1980’s the majority of us were still not using computers for work nor did the majority of us have them in our homes. At most we had a Texas Instruments calculator to balance the checkbook or a Commodore 64 you could use to play games on a monochromatic screen like The Oregon Trail or Lode Runner. But gradually developments in personal computing allowed lay people to do more things with these mystery boxes composed of hydrocarbon plastics and filled with circuit boards and wires. Bulletin boards accessible via modems had strictly been the domain of “computer nerds” and those mysterious teenaged “hackers” that could effortlessly start WWIII as depicted in the film War Games. But when companies like AOL and Netscape made the worldwide web more easily accessible to the average consumer via their home telephone lines, the drawing of a new era with incalculable civilizational ramifications was unquestionably upon us. Of course, it didn’t look that way at the time. And to a large extent, it never really does except in hindsight. Even technologists and otherwise experts in the field cannot predict how and at what pace a technology will develop and create ripples of change throughout the world and in our day to day lives. At the time, that GeoCities Dave Matthews Band website wasn’t exactly setting the world on fire. What exactly did it do? What problems did it solve? How was it going to change the way we live? Well, it wasn’t. But early iterations of personal computing and the internet had enough of a hook to grab the public’s imagination and their desire to know more, to interact more, and to experience new things. And of course, there was money to be made. And that capital investment allowed these technologies to be further developed into whichever now nearly ubiquitous device you’re using right now to read these words. In discussing the crypto space and blockchain, AI, VR, and next-gen computing, it’s often been a reiterated truism that we are in a stage analogous to those early dial-up days of the internet where future use-cases were unclear but adoption seemed like an inevitable outcome to all but the old and visionless who had lived their lives in an analog world and assumed it would therefore always be as such. The quote from at the beginning of this article asserts that the trifecta of blockchain, AI, and quantum computing will work synergistically to create use-cases and applications that will change our lives in ways so potentially disruptive and liberating that we simply cannot conceive of them. As these technologies go forward, particularly in the case of machine learning and autonomous machine interaction, the telescopic and exponential growth curve in both applications and related markets is probably limitless. In fifteen years time our future selves will look back upon contemporary cryptocurrency exchanges and cloud computing in the same way our present selves experience a floppy disk drive. We will wonder how we ever managed without advanced robotics, artificial intelligence, virtual reality, and completely decentralized peer to peer mediums of exchange. Hopefully, we will also have availed ourselves to invest in these technologies and to be the ones that got in early on what everyone else will be using tomorrow. Below are three examples of companies that represent just such a future synthesis of blockchain and AI technology. a16zcrypto Designed to be the new generation blockchain, MATRIX leverages the latest artificial intelligence technology to resolve the aforementioned challenges. The fusion of blockchain and AI technologies enables MATRIX to build a revolutionary cryptocurrency, which support significantly boosted transaction speed, superior accessibility to general users, enhanced security under malicious attacks, and highly flexible operations. The challenges facing today’s blockchains have to be resolved before the ideal of cryptocurrency can really become reality. We believe the artificial intelligence technology, which has received an unprecedented growth in the past decade, provides out-of-box solutions to address the challenges. MATRIX is designed to be an intelligent chain to unleash the potential power of the blockchain technology. In this section, we briefly review the objectives of MATRIX. Although smart contracts give blockchains the essential capability to handle scaled commercial behaviors, they need the users to be able to write programs in a given programming language. With MATRIX, no programming expertise is needed any more for designing smart contracts. The unique code generation technique of MATRIX allows automatic conversion of an abstract description of a smart contract into an executable program. Matrix only requires users to input the core elements (e.g., input, output, and transaction conditions) of a contract with a scripting language. Then a code generator based on a deep neural network is able to automatically convert the script into an equivalent program. Smart contract programs may call functions offered by the host system and/or third-party libraries. Also, programs running on different computers in a distributed framework do not provide any guarantee for execution time. Such openness and decentralization are the reflection of the essential spirit of blockchains, but give birth to various sources of security threats. In fact, the lack of security is plaguing the smart contracts. The MATRIX blockchain is equipped with a power AI security engine consisting of four major components, 1) a rule- based semantic and syntactic analysis engine for smart contracts, 2) a formal verification toolkit to prove the security properties of smart contracts, 3) an AI-based detection engine for transaction model identification and security checking, and 4) a deep learning based platform for dynamic security verification and enhancement. Today all public chains are suffering from the problem of long transaction latency and low transaction throughput. Specifically, it takes over 30 minutes for Bitcoin to finish one transaction, while the transaction throughput of Ethereum is only 10 Transfer Per Second (TPS). In fact, a blockchain depends on a P2P network to validate transactions. Since a transaction needs to be broadcasted to all nodes in a network, the overall latency has to increase as long as more nodes are joining the network. MATRIX resolves the problem by dynamically selecting a delegation network in which all nodes are voted as delegates of others. All Proof-of-Work (PoW) processing is only allocated inside the delegation network, which only incurs a much smaller latency due to the smaller number of nodes. The selection process is random in the sense that a node is selected with a probability proportional to its Proof-of-Stake (PoS). The online version of MATRIX will support a throughput of 100,000 TPS. MATRIX is designed to be a highly flexible blockchain. The flexibility is twofold. First, MATRIX offers access control and routing services so as to allow seamless integration private chains into a common public chain. Such a feature meets the requirements many industry and government players for authorization, while at the same time allows necessary information flow from a public chain to a private one and vice versa. Second, MATRIX uses a reinforcement learning framework to optimize its parameters (e.g., consensus mechanism, and transaction configuration) in an evolutionary manner. The optimization paradigm ensures dynamic updating of parameters for near-optimal performance without the risk of incurring hardfork. Perhaps the most criticized part of cryptocurrency is the “waste” of energy in the mining computations. Although it is essential to attach physical value to the cryptocurrency, the mining process does not make any sense out of the world of digital currency. The problem is even worse when now over 70% of the total computing power around of world is dedicated to mining Bitcoins and others. MATRIX introduces a new mining mechanism in which miners perform the Markov Chain Monte Carlo (MCMC) computation, which is an essential tool for Bayesian reasoning. MCMC based Bayesian computing plays a fundamental role in numerous big data applications such as gene regulatory network, clinical diagnosis, video analytics, and structural modeling. As a result, a distributed network of MCMC computing nodes provide the power for solving real-world compute-intensive problems and thus build a bridge between the values in the physical and virtual worlds. The Vectorspace AI [ ]platform enables ‘Smart Basket’ (token baskets for traders, funds, ETFs) generation based on user-selected trends that exist in search, social media and news. Their ‘Smart Baskets’ are based on the ability for these baskets to determine for themselves whether or not to include additional cryptocurrencies or components from related baskets that may increase overall returns. Baskets that interact this way with one another will conduct these kinds of transactions between one another using the Vectorspace utility token VAI, which is also required to dynamically generate baskets. link Vectorspace is designed as a tool for discovery as well as a suite of sophisticated tools to help you increase your profits with other cryptocurrencies by reducing risk and maximizing your gains. Their focus is on “Advanced Natural Language Processing (NLP) & Sentiment Analysis for Smart Cryptocurrency Basket Trading” and they have live products any crypto trader can use today. Their site updates every few minutes powered by their algorithms. [ Link to Smart Baskets ] According to their : “A growing number of organizations offer the ability to trade a basket or group of cryptocurrencies with a single transaction. Similar to a traditional ETF (Exchange-Traded Fund), “token baskets” as they’re more commonly called, enable a fund or individual investor to spread their risk, diversify and lower volatility while maximizing gains with more safety and stability. This approach is valuable but incomplete. For example, taking the top 20 best performing cryptocurrencies and placing them in a basket or fund minimizes gains compared to enabling fine-grained custom creation of token baskets. Which groups of cryptocurrencies are beating the market? Why? What do they have in common? How strong are those relationships? Are those relationships well known and obvious or are they hidden relationships? Are the relationships numerical or nonnumerical, based on concepts, context and sentiment, or a combination of both?” whitepaper These are the questions Crypto Discover is designed to answer. Vectorspace AI’s Crypto Discover platform is an advanced cryptocurrency discovery engine that enables a user to automatically generate a token basket or “mini-index fund” made up of cryptocurrencies that are related to trends in news, global and local searches, concepts, context, keywords, hashtags, social media, URLs and other “dynamic content.” Join the Vectorspace Telegram group for more information on the project . here The value and power of Artificial Intelligence is growing dramatically every year, and will soon dominate the internet — and the economy as a whole. However, AI tools today are fragmented by a closed development environment; most are developed by one company to perform one task, and there is no way to plug two tools together. SingularityNET aims to become the key protocol for networking AI and machine learning tools to form a coordinated Artificial General Intelligence. SingularityNET is an open-source protocol and collection of smart contracts for a decentralized market of coordinated AI services. Within this framework, the benefits of AI become a global commons infrastructure for the benefit of all; anyone can access AI tech or become a stakeholder in its development. Anyone can add an AI/machine learning service to SingularityNET for use by the network, and receive network payment tokens in exchange. SingularityNET is backed by the SingularityNET Foundation, which operates on a belief that the benefits of AI should not be dominated by any small set of powerful institutions, but shared by all. A key goal of SingularityNET is to ensure the technology is benevolent according to human standards, and the network is designed to incentivize and reward beneficial players. The concept of a Technological Singularity is increasingly widely accepted throughout the technology and business worlds. More and more, it is realized that within the next few decades there will be a transition to a new society and economy in which machine intelligence is the dominant factor; and novel digital and organic technologies acting on multiple scales will network together to produce emergent “global brain” dynamics of unprecedented complexity and sophistication. Humanity faces many challenges on the path to a positive Singularity; among these is the contemporary global economic system. In numerous respects, today’s standard financial mechanisms and institutions are not up to the task of serving as the economic engine of a smooth transition to a broadly positive Singularity. New, more flexible, open and rapidly adaptive economic structures and dynamics are needed. Blockchain provides a powerful tool for managing transactions in a Singularity- era economy; but blockchain is just a tool, and it must be used in the right way. A blockchain-based framework designed to serve the needs of AI Agents as they interact with each other and with external customers can enable the emergence of a collective intelligence. And it is critical that this framework be designed with positive principles in mind: Democratic governance on specific issues — giving the community a voice in the system will tend to make the system act for the benefit of the community; Encouraging innovative new Agents to enter the network, and creating the conditions for Agents to act in a manner that feeds the collective intelligence; • Directing a significant percentage of the network’s efforts toward causes of broad benefit. SingularityNET has been designed to meet these requirements, via Delivering intelligence services to corporations, individuals and organizations; Fostering the emergence of increasingly powerful distributed general intelligence; Deploying artificial intelligence for ever-increasing benefit of as many humans and other sentient beings as possible. SingularityNET is explicitly designed both to be highly valuable in the current context, and to lay the groundwork for the emergence of a future self-modifying, decentralized “artificial cognitive organism” with the eventual potential for general intelligence and beneficial ethical characteristics beyond the human level. It is a practical design inspired by long theoretical thinking and prototyping on the part of the founders regarding concepts such as Artificial General Intelligence, Open-Ended Intelligence and the Global Brain. What’s your opinion on AI and cryptocurrencies exploiting AI for great benefits? Originally published at medium.com on June 28, 2018.

3 Artificial Intelligence Cryptos Set to Start Trading!

, a well-known Silicon Valley firm, has raised $1.8 billion to invest in startups from to beauty supplies, hoping to build on its streak of lucrative bets in companies such as Snap Inc ( ), the firm’s partners told Reuters. Lightspeed Ventures venture capital cryptocurrency SNAP.N A spokesperson of Lightspeed said in a statement: “We’ve been making many investments in the fintech space over the past several years, including Affirm, Blockchain, Basis and more that are unannounced. Blockchain-based technologies are a huge area of interest for us and we have partners focused on this. In terms of a dedicated crypto fund, we have nothing to announce at this time.” At the same time, another notable VC firm stated: Andreessen Horowitz “We believe that just as the last three megatrends — mobile, social, and cloud — intersected and reinforced each other, so will the next three megatrends — next-gen computing devices, AI, and crypto.” — a16zcrytpo.com Another recent sums up the fervor in crypto among funds in a very powerful way: quote “The hedge fund financiers backing QI are far from the only City bigwigs putting cash behind AI and related fintech projects. In May, the Swiss bank UBS said it was moving 80 staff into a new artificial-intelligence division, dubbed the Strategic Development Lab, which is dedicated to defending its fixed-income trading turf against hungry fintech startups. Later the same month Deutsche Börse, the German stock exchange, said it had earmarked €270m for investment in big data analysis, blockchain, robotics and AI. Meanwhile, the City Corporation’s top envoy to the EU, Jeremy Browne, said late last month that he thinks AI will be more disruptive to UK financial firms than Brexit, in the long term. Quant Insight and its backers are betting that banks and asset managers will also tap into new tech by buying in services like the one it offers. The analytics firm said it has grown customer numbers by 50% this year, and has added new staff to sell its services in Asia and the US. It is also working with “major banks” on creating bespoke indexes and equity baskets — groups of stocks that share certain characteristics and are likely to move in a similar way in response to given macro events.” As more funds focus on AI and cryptocurrencies, just as our fund has done for the last 4 years, we anticipate a consolidation of quality with funds focused on high-end crypto technologies that utilize AI, NLP and Machine Learning. A few examples of cryptos being targeted by new crypto and AI-focused funds might be: [ ]. Matrix AI, Vectorspace AI or SingularityNet link

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