This is an Artificial Intelligence booster video featuring Bill Gates, Tim Cook, Warren Buffett, Barack Obama, Elon Musk, Sundar Pichai and Jeff Bezos and a promotion for Simplilearn’s Artificial Intelligence course.
According to the report How AI Boosts Industry Profits and Innovations, AI is predicted to increase economic growth by an average of 1.7 percent across 16 industries by 2035. The report goes on to say that, by 2035, AI technologies could increase labor productivity by 40 percent or more, thereby doubling economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this domain. Let us see what our business leaders have to say about this.
The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. for engineers with the required skills.
The information revolution is in the process of taking us into a new world of distributed networks as the organizational paradigm of the information age. The combination of telecommunication networks and computerized coordination enables us to replace centralized management within closed hierarchies with open networks. As the underlying technology matures we are able to convert more and more systems that were previously closed and centralized and have them manage through automated networks.
We have talked in previous videos about blockchain technology and its evolution, but its true significance and potential can only really be understood in the context of broader technological change brought about in the ongoing evolution of the internet and information technology. Today powerful new technologies are coalescing to take us into a new technological paradigm, these include the rise of advanced analytics coupled with datafication and the Internet of Things, the blockchain will have to work synergistically with these if the true capacities are to be realized.
In this video, Jason Ball introduces some basic concepts of quantum mechanics and quantum computing, including its advantages and disadvantages.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community.
Jason Ball is a PhD student in quantum information science, a former teacher, and the father of a budding young scientist, Jason has a passion for teaching physics.
In this video, we are going to trace the past present and future of blockchain technology. Over the past years what we call the blockchain has been evolving fast, from the original Bitcoin protocol to the second generation Ethereum platform to today where we are in the process of building what some call blockchain 3.0 In this evolution we can see how the technology is evolving from its initial form as essentially just a database, to becoming a fully fledged globally distributed cloud computer.
In this video, we are going to talk about the basics of blockchain as a technology.
On its most basic level, the blockchain can be understood as a new kind of database, at least this is its original design. What is different about this database is that it is distributed.
Digital databases have been around for awhile now but until recently they have been designed to centralize information on one computer or within one organization. The blockchain though uses a distributed network of computers to maintain a shared database.
In this video, we are going to give a very high-level overview looking at the primary dimensions of this technology that we call the blockchain. We will firstly talk about the underlying technology, then the distributed ledgers that this technology supports, then the token economies those can be built on that ledger system. We will only touch upon these topics here to get an overview before going into them in more detail in future videos
The Blockchain is a term that has come to mean many things to many people. For developers, it is a set of protocols and encryption technologies for securely storing data on a distributed network.
For business and finance, it is a distributed ledger and the technology underlying the explosion of new digital currencies.
For technologists, it is the driving force behind the next generation of the internet.
For others, it is a tool for radically reshaping society and economy taking us into a more decentralized world.
Whichever way you look at it, blockchain has become a term that captures the imagination and fascinates many, as the implications of such technology are truly profound. For the first time in human history, people anywhere can trust each other and transact within large peer-to-peer networks without centralized management.
Trust is established, not by centralized institutions, but by protocols, cryptography and computer code. This greatly strengthens our capacity for collaboration and cooperation between organizations and individuals within peer networks, enabling us to potentially form global networks of collaboration without centralized formal institutions; unprecedented but hugely relevant in an age of globalization and a new set of 21st-century challenges that require mass collaboration.
The blockchain is a complex technological, economic and social phenomenon. It calls into question what might have seemed to be established parameters of the modern world like currency, economics, trust, value, and exchange.
To make sense of this one needs to understand it in a holistic context all the way from its technicalities to its aspirational potential.
This course is designed to do exactly that, by giving an 360 degree overview to the different dimensions of the technology, its potential application within various industries and its far-reaching implications for society and economy.
In the first section of the course, we give an overview to the blockchain both on a technical and nontechnical level. We also discuss the importance of the blockchain within the context of the emerging next generation internet.
In the second section we talk about the blockchain as a so call “trust machine” and how it enables transparency and collaboration.
We will look at distributed ledger technology, talking about smart contracts, Ethereum, and decentralized applications.
In the third section, we introduce you to the workings of token economies illustrating how the blockchain and distributed ledgers can work to build vibrant ecosystems through the use of tokens to incentivize behavior.
In the last section of the course, we will be looking at specific applications of the blockchain to economy, society, technology, and environment, looking at both existing practical applications and potential future applications.
The blockchain is a so-called emerging technology that is currently experiencing very rapid evolution. Within the space of just two or three years it has already gone through changes in its technical implementation and our understanding of what it is and can be, have already changed significantly in just this brief time. As such our aim is to future proof this course by not dwelling excessively on existing technical implementations but presenting more a conceptual understanding of the blockchain within the broader process of change of the emerging next generation internet.
The blockchain is much more than a technology, it is also a culture and community that is passionate about creating a more equitable world through decentralization. It is a movement to disrupt the disruptors, to redesign the internet and in so doing shake up existing centralized incumbents. Throughout the course, we will introduce you to this culture and its aspirations.
The course is non-technical in nature, it is an introductory course and thus all terms will be explained it should be accessible to anyone with a basic understanding of web technologies and economics.
Prediction markets are speculative markets, very similar to futures markets, which have been designed so that the prices can be interpreted as probabilities for events occurring and used to make predictions.
Put very simply, prediction markets enable users to trade shares in the outcomes of an event and in so doing to reveal the information that people have about the likelihood of an event occurring.
In a very elegant way, blockchain prediction markets use tokens to reduce uncertainty and find truths about future events. As they align truthful statements with token investments they create a way for people and groups to come to consensus about a shared conception of reality by using markets to create valid sources of information.
HOW IT WORKS
A number of blockchain based prediction markets now exist such as Augur, Stox or Gnosis. Using one of these networks anyone anywhere in the world can create a market for people to try and predict the outcome to some event, such as a sports match, an election, the weather, sales of a company, price fluctuations of commodities, the availability of almonds in Spain next year or the likelihood of a conflict occurring in central Asia at a given time.
Market makers provide initial funding for the market and for this receive some trading fees. Anyone can then buy and sell shares in the outcomes of that market.
Predictions are based on a binary event where something either will or won’t happen. The value of a bet will in most cases reflect the probability of an outcome materializing.
If you place a bet on a coin flip, the outcome will always be 50% heads, 50% tails. There are no external market conditions that will influence the outcome.
Luck plays a major role, and this is called gambling.
But prediction markets rely on the collective wisdom held by a group of people on the probability of a future event materializing. The current market price of a share is an estimate of the probability of an event actually occurring. The prices of each share add up to one dollar. So if you buy a share at even odds it will cost you 50 cents. If you end up being right, you’ll receive a dollar for that share. If you see a market price of 53 cents then it is reasonable to assume that there is a 53% chance that outcome will occur.
As an example, we can think about a market for the hiring of a new CEO of a company given just two candidates, Bob and Jane. In this market, one share for the Bob option pays you a euro if he is hired and pays you nothing otherwise. One share for Jane pays you a euro if you hold the share and she is hired, and pays zero if she is not. Now, suppose you think Jane has a chance of winning that position, how much would you be willing to pay for a Jane share? If a Jane share pays a dollar if Jane wins and she has a 70% chance of winning, then that share is worth 70 cents. You would be willing to pay up to 70 cents for such a share.
Suppose you enter this market and you find that Jane’s shares are selling for just 55 cents, well, that’s a buying opportunity. Something which you think is worth 70 cents is selling for 55, so then, you should buy the Jane option. In buying the shares, you would be pushing up their price. In this way, your predictions, your information, your opinions about which candidate is likely to win become incorporated into the price of a Jane share.
Imagine however you thought Jane had a 70% chance of winning but her shares were selling for 80 cents, then, you would want to sell Jane shares. Even if you really wanted Jane to win the position of CEO. To make more money you would sell the Jane shares and buy the Bob share. In this way we can see how prices come to reflect the market information.
The prediction market really boils down to one number and if there’s anyone in the world who thinks they would know better than this number they have direct financial incentives to trade this market and basically with every trade they make they feed the information into this market and in the end we have a better number from the market.
The outcome becomes more predictable over time. This is because the payoff depends on the accurate prediction of an outcome of an event. As a larger number of people do more market research to come to the most likely conclusion, the predicted outcome will lean more favorable to one side. Current share prices over time come to reveal information about the likelihood of an event occurring according to the information gained from the market participants.
ADVANTAGES
Prediction markets are not a new invention. They are in fact centuries old and have proven their effectiveness many times.
One of the most popular current markets is the Iowa Electronic Markets. In over two decades of testing this market, in presidential elections, congressional elections, and state elections, the market prices from the Iowa Electronic Markets have turned out to be better predictors of the outcomes than have political polls.
The two key features that make them successful is that firstly, they draw upon dispersed information that is consolidated and averaged out. And secondly people have skin in the game, that is to say people are putting their own money on the line and this ensures a correspondence between what they predict and what they believe to be true.
Prediction markets work to align incentives by backing statements up with resources. They work to obtain truthful and relevant information through financial and other forms of incentives. With real money on the line, people have an incentive to think carefully when they’re investing and they have an incentive to collect, process and interpret all of the information available all over the world. The resulting market prices potentially reflect a lot of deep-seated and diverse information in a way which surveys or polling cannot.
Likewise because prediction markets rely on the collective view of many, not just one person’s research, they can efficiently aggregate a plethora of information, beliefs, and data.
These markets work on the principle of the wisdom of the crowd, which states that if you ask enough people something, their average answer is usually far more accurate than anyone expert, which creates a powerful forecasting tool.
The author James Michael Surowiecki posits that there are a number of necessary conditions for collective wisdom: independence of decision, diversity of information, decentralization of organization.
In the case of predictive markets, each participant normally has diversified information from others and makes their decision independently.
The market itself has a character of decentralization compared to expertise decisions. Because of these reasons, predictive markets are generally a valuable source to capture collective wisdom and make accurate predictions.
Equally the ability of the prediction market to aggregate information and make accurate predictions is based on the Efficient Market Hypothesis, which states that assets prices are fully reflecting all available information. For instance, existing share prices always include all the relevant related information for the stock market to make accurate predictions.
Prediction markets create a very dynamic system, as opposed to a seven-year plan or a yearly assessment, prediction markets can incorporate new information quickly and may be continuously updated.
BLOCKCHAIN
Using a blockchain as the IT infrastructure adds additional benefits to prediction markets. By creating prediction markets on a blockchain network we can ensure that the data always remains open and accessible to all parties. It removes the possibility for the centralized authority to alter results, it can thus be trusted, is secure and if designed well blockchain prediction markets may be difficult to manipulate.
Prediction markets may be used to provide liquidity and hedging around all forms of futures markets. We could have a prediction market for “will the price of bitcoin be more than ten thousand dollars on the first of January 2019.”
All futures markets could be migrated to the blockchain using prediction markets. Likewise, all betting, such as online sports betting, could be more securely and efficiently run on blockchain platforms.
Migrating all of these disparate betting, derivatives and futures markets, to the blockchain could create a much more interoperable system where different networks could automatically draw upon the wisdom of a given network through APIs that connect into the price of the token.
These prediction markets could work as networks that aggregate the best knowledge that we have about a given unknown event. With the knowledge in those networks then being accessible for automatic external use in smart contracts via APIs.
A smart contract ensuring a wedding event could plug into a token market predicting the outcome for the weather on a certain day and use the token price to calculate the likelihood and cost of a weather disturbance to formulate the cost of the insurance claim.
In machine learning the goal of training is to create an accurate model that answers questions correctly, most of the time.
In order to train a model, we need correct data.
The quality and quantity of the data acquired will determine the quality of results.
We need to prepare the data, which includes randomizing. Also some of the data is earmarked for training and other data is used for evaluation.
Choosing a machine learning model is an important step and is pertinent to what is being evaluated.
The training phase is the bulk of the machine learning workflow.
The model usually does a poor job in the beginning, but we can then help it make better decisions by adjusting it over iterative phases to reach better conclusions.
At some point one needs to determine when the adjustments are adequate for one’s needs, since the adjustments can go on forever.
Machine learning is used for answering questions and making predictions using models and data rather than human judgement.
Tensorflow is an open source machine learning library for research and production and is a great place to experiment and learn.
