AI News, Google's New AI Is a Master of Games, but How Does It Compare to ... artificial intelligence
Blockchain: The complete guide
Blockchain, which began to emerge as a real-world tech option in 2016 and 2017, is poised to change IT in much the same way open-source software did a quarter century ago.
And in the same way Linux took more than a decade to become a cornerstone in modern application development, Blockchain will likely take years to become a lower cost, more efficient way to share information and data between open and private business networks.
Based on a distributed, peer-to-peer (P2P) topology, blockchain or distributed ledger technology (DLT) allows data to be stored globally on thousands of servers – while letting anyone on the network see everyone else's entries in real-time.
For businesses, however, blockchain holds the promise of transactional transparency – the ability to create secure, real-time communication networks with partners around the globe to support everything from supply chains to payment networks to real estate deals and healthcare data sharing.
So while blockchain isn't going to replace traditional corporate relational databases, it does open new doors for the movement and storage of transactional data inside and outside of global enterprises.
Driven mainly by financial technology (fintech) investments, blockchain has seen a fast uptick in adoption for application development and pilot tests in a number of industries and will generate more than $10.6 billion in revenue by 2023, according to a report from ABI Research.
First and foremost, blockchain is a public electronic ledger built around a P2P system that can be openly shared among disparate users to create an unchangeable record of transactions, each time-stamped and linked to the previous one.
Blockchain standards organizations, universities and start-ups have proposed newer consensus protocols and methods for spreading out the computational and data storage workload to enable greater transactional throughput and overall scalability – a persistent problem for blockchain.
In a word, bitcoin – the wildly hyped cryptocurrency that allows for payment transcations over an open network using encryption and without exposing the identities of individual bitcoin owners.
Other forms of cryptocurrency or virtual money, such as Ether (based on the Ethereum blockchain application platform), have also gained significant traction and opened new venues for cross-border monetary exchanges. (Ethereum was introduced in 2013 by developer Vitalik Buterin, who was 19 at the time.) The term bitcoin was first...
well, coined in 2008 when Satoshi Nakamoto (likely a pseudonym for one or more developers) wrote a paper about a 'peer-to-peer version of electronic cash that would allow online payments to be sent directly from one party to another without going through a financial institution.'
second form of blockchain, known as private or permissioned blockchain, allows companies to create and centrally administer their own transactional networks that can be used inter- or intra-company with partners.
After piloting a blockchain-based produce supply chain tracking system, Walmart and Sam's Club are telling suppliers to get their product data into the system so they can begin tracking produce from farm to store.
De Beers,which controls about 35% of the world's diamond production, has also launched a blockchain-based supply chain to track diamonds for authenticity and to help ensure they aren’t coming from war-torn regions where miners are exploited.
For example, New York-based ShelterZoom this year is launching a real estate mobile application that lets real estate agents and clients see all offers and acceptances in real time online.
'In order to move anything of value over any kind of blockchain, the network [of nodes] must first agree that that transaction is valid, which means no single entity can go in and say one way or the other whether or not a transaction happened,' Tapscott said.
If a distributed ledger is to achieve adoption by financial technology (FinTech) companies and compete with payment networks hundreds of times faster, it must find a way to boost scalability and throughput and address latency problems.
or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.
The company made headlines in 2016 after its AlphaGo program beat a human professional Go player Lee Sedol, the world champion, in a five-game match, which was the subject of a documentary film.
more general program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese chess) after a few days of play against itself using reinforcement learning.
During one of the interviews, Demis Hassabis said that the start-up began working on artificial intelligence technology by teaching it how to play old games from the seventies and eighties, which are relatively primitive compared to the ones that are available today.
DeepMind has opened a new unit called DeepMind Ethics and Society and focused on the ethical and societal questions raised by artificial intelligence featuring prominent philosopher Nick Bostrom as advisor.
In 2017 DeepMind released GridWorld, an open-source testbed for evaluating whether an algorithm learns to disable its kill switch or otherwise exhibits certain undesirable behaviours.
To date, the company has published research on computer systems that are able to play games, and developing these systems, ranging from strategy games such as Go
According to Shane Legg, human-level machine intelligence can be achieved 'when a machine can learn to play a really wide range of games from perceptual stream input and output, and transfer understanding across games[...].'
Hassabis has mentioned the popular e-sport game StarCraft as a possible future challenge, since it requires a high level of strategic thinking and handling imperfect information.
As opposed to other AIs, such as IBM's Deep Blue or Watson, which were developed for a pre-defined purpose and only function within its scope, DeepMind claims that its system is not pre-programmed: it learns from experience, using only raw pixels as data input.
Without altering the code, the AI begins to understand how to play the game, and after some time plays, for a few games (most notably Breakout), a more efficient game than any human ever could.
In October 2015, a computer Go program called AlphaGo, developed by DeepMind, beat the European Go champion Fan Hui, a 2 dan (out of 9 dan possible) professional, five to zero.
Go is considered much more difficult for computers to win compared to other games like chess, due to the much larger number of possibilities, making it prohibitively difficult for traditional AI methods such as brute-force.
AlphaStar uses a reinforced learning to learn the basics based on replays from human players, and later played against itself to enhance its skills.
After training these networks employed a lookahead Monte Carlo tree search (MCTS), using the policy network to identify candidate high-probability moves, while the value network (in conjunction with Monte Carlo rollouts using a fast rollout policy) evaluated tree positions.
DeepMind has also collaborated with the Android team at Google for the creation of two new features which will be available to people with devices running Android Pie, the ninth installment of Google's mobile operating system.
It is the first time DeepMind has used these techniques on such a small scale, with typical machine learning applications requiring orders of magnitude more compute power.
In August 2016, a research programme with University College London Hospital was announced with the aim of developing an algorithm that can automatically differentiate between healthy and cancerous tissues in head and neck areas.
Staff at the Royal Free Hospital were reported as saying in December 2017 that access to patient data through the app had saved a ‘huge amount of time’ and made a ‘phenomenal’ difference to the management of patients with acute kidney injury.
Additionally, in February 2018, DeepMind announced it was working with the U.S. Department of Veterans Affairs in an attempt to use machine learning to predict the onset of acute kidney injury in patients, and also more broadly the general deterioration of patients during a hospital stay so that doctors and nurses can more quickly treat patients in need.
Privacy advocates said the announcement betrayed patient trust and appeared to contradict previous statements by DeepMind that patient data would not be connected to Google accounts or services.
The agreement shows DeepMind Health had access to admissions, discharge and transfer data, accident and emergency, pathology and radiology, and critical care at these hospitals.
This included personal details such as whether patients had been diagnosed with HIV, suffered from depression or had ever undergone an abortion in order to conduct research to seek better outcomes in various health conditions.
In May 2017, Sky News published a leaked letter from the National Data Guardian, Dame Fiona Caldicott, revealing that in her 'considered opinion' the data-sharing agreement between DeepMind and the Royal Free took place on an 'inappropriate legal basis'.
DeepMind, a subsidiary of Alphabet that’s focused on cracking artificial intelligence, has announced a new landmark in that grand quest: beating humans atgalactic warfare.
The news: AlphaStar, the company’s latest learning algorithm, defeated professional Starcraft II players for the first time, scoring 10 wins and one loss against the pros, called TLO and MaNa.
The popular real-time strategy game involves players competing as one of three races to building structures and engaging in combat across a sprawling battlefield.Practice, practice: AlphaStar learned to play within an environment called the AlphaStar League.
The techniques developed for playing the game could potentially prove useful in many practical situations where complex strategy is required: think trading or even military planning.Higher score: Starcraft II is not only extremely complex.
Google's StarCraft-playing AI is crushing pro gamers
First, artificial intelligence created by Google's DeepMind mastered the game Go.
That was a feat computer scientists had long struggled to achieve with AI since Go, which involves players alternating at placing black and white stones on a 19-by-19 grid, can be played with a near infinite number of moves.
In a blog post on Thursday, DeepMind outlined challenges that its AI faced learning to play StarCraft II, such as the inability of players to see everything that's happening at once and the use of continuous gameplay (rather than players taking turns).
First, they spent three days training a neural network — a machine-learning algorithm modeled after the way neurons work in a brain — on replays of human players' StarCraft II games.
This neural network was used to create a number of computer-based competitors that played many, many rounds of the game against each other, learning from their experiences, over the course of two weeks.
David Silver, co-lead researcher at DeepMind, said the team building AlphaStar thought a lot about fairness and wanted the bot to play in a way similar to humans.
- On 3. marts 2021
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