Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and future studies.
There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.
- Reactive machines. …
- Limited memory. …
- Theory of mind. …
Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. … For most of its history, AI research has been divided into subfields that often fail to communicate with each other.
The basic elements of artificial intelligence are…
- Problem Solving mostly deals with constraint satisfaction problems, Solve by Search.
- Certain knowledge representation, reasoning of it and plan of execution. First order logic is dealt here.
- Uncertain knowledge representation and its reasoning. …
- Feature Learning. …
- Perception, communication and action taken by agents.
Major sub-fields of AI now include: Machine Learning, Neural Networks, Evolutionary Computation, Vision, Robotics, Expert Systems, Speech Processing, Natural Language Processing, and Planning.
A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. … Some argue that advances in artificial intelligence (AI) will probably result in general reasoning systems that lack human cognitive limitations.
The branches of artificial intelligence
- Computational creativity –
- Machine learning. Neural networks – Hybrid neural network – …
- Fuzzy systems –
- Evolutionary computation, including: Evolutionary algorithms – Genetic algorithm – …
- Probabilistic methods including: Bayesian network. Hidden Markov model. …
- Chaos theory.
Google’s artificial intelligence (AI) is much smarter than Apple’s Siri, according to a report from three Chinese researchers. … But while Google’s AI leads the tech pack, it has a long way to go before it comes close to human intelligence; the average 6-year-old has an IQ of 55.5, according to the report.
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion.
There is Artificial intelligence, and there is Artificial general intelligence (AGI). The latter is a branch of the former. Microsoft Cortana is definitely an AI application, like all Natural language processing applications, but it’s not an AGI application, nor it aspires to be.
The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. … Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.
In addition, deep learning is used to detect pedestrians, which helps decrease accidents. Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.
Java, Python, Lisp, Prolog, and C++ are major AI programming language used for artificial intelligence capable of satisfying different needs in development and designing of different software.
LISP is another language used for artificial intelligence development. … LISP, unlike most AI programming languages, is more efficient in solving specific as it adapts to the needs of the solutions a developer is writing. It is highly suitable in inductive logic projects and machine learning.
In computer science terms, SingularityNET is essentially a distributed computing architecture or making new kinds of smart contracts to facilitate market interactions with AI and machine learning tools. The following design principles are incorporated throughout the design:
- Interoperability: The network will be able to interface with multiple
- Data Sovereignty and Privacy: User data control and sharing comes
with privacy-enabled controls on top of the network, and access is validated
through smart contracts and the blockchain.
- Modularity: Flexible network capabilities make it possible to create custom topologies, AI Agent collaboration arrangements, and failure recovery methods.
- Scalability: SingularityNET will securely host both private and public
contracts, so more scalable and resilient applications can be built on top
of it with near zero transaction costs.
SingularityNET Agents can run in the cloud, on phones, robots, or other embedded devices. Via close collaboration with co-founding firm Hanson Robotics, SingularityNET is designed to foster the development of multiple species of robots as the next-generation interface for delivering AI services and applications, and fostering the emergence of global Artificial General Intelligence.
SingularityNET meets an acute and accelerating market need. In the current economic and technological context, every business needs AI, but off-the-shelf AIs will rarely match a business’s needs. Only tech giants can hire armies of developers to build custom AIs, and even they have a hard time hiring enough AI experts to meet demand. SingularityNET provides an automated process enabling each business to connect existing AI tools together to build the solution it needs. By providing an easy means of configuring tools, it offers both customization and availability, while reducing the reduplication of effort involved in proprietary development, making the development process more efficient.
Many state of the art AI tools exist only in GitHub repositories created by grad students or independent researchers. This puts them out of the reach of anyone without the skills to install, configure, and run them. Most AI developers are academics, not business people, and have no easily-accessible marketplace to monetize their clever AI code.
In addition to their clever code, machine learning tools require datasets of sufficient size. Creating and managing such large datasets is beyond the means and capabilities of most AI developers, and the closed development model that currently prevails makes it hard for developers to share datasets.
SingularityNET launches these AI tools and datasets onto the marketplace, making them more accessible to end-users and developers, and giving developers a way to monetize their creations.
It is a sharing-economy marketplace for AI, that encourages collaboration between these tools and decentralized sharing of information, democratizing access to the benefits of AI. In accordance with these goals, SingularityNET will be an open network. Anyone can insert an AI Agent as long as the Agent shares information according to the SingularityNET API, and accepts/disburses payment according to SingularityNET’s economic logic. New AI Agents will come from AI software developers who want access to SingularityNET’s market, which will be the hub of open AI services.
We have a situation similar to the ones that spawned the creation of Uber and AirBnB: there is a large unexploited resource, a large market in need of that resource, and we are launching the tool to connect the two. The unexploited resource is AI algorithms and software existing on GitHub and elsewhere, and n need of this resource is the 99% of businesses that can’t afford its own team of AI experts.
How AI can change Blockchain
Although extremely powerful, a blockchain has its own limitations as well. Some of them are technology-related while others come from the old-minded culture inherited from the financial services sector, but all of them can be affected by AI in a way or another:
- Energy consumption: mining is an incredibly hard task that requires a ton of energy (and then money) to be completed (O’Dwyer and David Malone, 2014). AI has already proven to be very efficient in optimizing energy consumption, so I believe similar results can be achieved for the blockchain as well. This would probably also result in lower investments in mining hardware;
- Scalability: the blockchain is growing at a steady pace of 1MB every 10 minutes and it already adds up to 85GB. Satoshi (2008) first mentioned “blockchain pruning” (i.e., deleting unnecessary data about fully spent transactions in order to not hold the entire blockchain on a single laptop) as a possible solution but AI can introduce new decentralized learning systems such as federated learning, for example, or new data sharding techniques to make the system more efficient;
- Security: even if the blockchain is almost impossible to hack, its further layers and applications are not so secure (e.g., the DAO, Mt Gox, Bitfinex, etc.). The incredible progress made by machine learning in the last two years makes AI a fantastic ally for the blockchain to guarantee a secure applications deployment, especially given the fixed structure of the system;
- Privacy: the privacy issue of owning personal data raises regulatory and strategic concerns for competitive advantages (Unicredit, 2016). Homomorphic encryption (performing operations directly on encrypted data), the Enigma project (Zyskind et al., 2015) or the Zerocash project(Sasson et al., 2014), are definitely potential solutions, but I see this problem as closely connected to the previous two, i.e., scalability and security, and I think they will go pari passu;
- Efficiency: Deloitte (2016) estimated the total running costs associated with validating and sharing transactions on the blockchain to be as much as $600 million a year. An intelligent system might be eventually able to compute on the fly the likelihood for specific nodes to be the first performing a certain task, giving the possibility to other miners to shut down their efforts for that specific transaction and cut down the total costs. Furthermore, even if some structural constraints are present, a better efficiency and a lower energy consumption may reduce the network latency allowing then faster transactions;
- Hardware: miners (and not necessarily companies but also individuals) poured an incredible amount of money into specialized hardware components. Since energy consumption has always been a key issue, many solutions have been proposed and much more will be introduced in the future. As soon as the system becomes more efficient, some piece of hardware might be converted (sometimes partially) for neural nets use (the mining colossus Bitmain is doing exactly this);
- Lack of talent: this is leap of faith, but in the same way we are trying to automate data science itself (unsuccessfully, to my current knowledge), I don’t see why we couldn’t create virtual agents that can create new ledgers themselves (and even interact on it and maintain it);
- Data gates: in a future where all our data will be available on a blockchain and companies will be able to directly buy them from us, we will need help to grant access, track data usage, and generally make sense of what happens to our personal information at a computer speed. This is a job for (intelligent) machines.
See original article here: https://medium.com/@Francesco_AI/the-convergence-of-ai-and-blockchain-whats-the-deal-60c618e3accc
I’ve been racking my brain searching for the bridge between my profession as a Senior Energy Professional in the energy and environmental sector and my new ventures in artificial general intelligence (AGI). And, I’ve discovered the answer…Settlement Quality AI Data (SQAID) for microgrids.
Here’s a bit of history that might help the understanding.
I’ve been active in the California energy sector for since 1995 and have guided public and private organizations through the intricate process of implementing energy and environmental programs. These programs include advanced metering infrastructures, competitive green power supply, distributed and utility scale generation, performance contracting agreements and solar power purchase agreements.
As the Vice President of Sales & Energy Services for Commonwealth Energy Corporation, I managed the San Diego Association of Governments (SANDAG) account and sold over 100 MW of renewable electricity services to their SANDAG members.
When I first arrived at Commonwealth the market was in chaos and the California Energy Crisis looming. As an Energy Service Provider, we were mandated by the California Public Utility Commission to comply and to seamlessly integrate all policy and activities with the Investor Owned Utilities (IOUs) and the California Independent System Operator (California ISO).
At the same time, the IOUs began building Advanced metering infrastructures (AMIs) throughout the state. An AMI is an integrated system of smart meters, communications networks, and data management systems that enables two-way communication between utilities, service providers and customers.
As per the California ISO, “Metering and telemetry ensure operational accuracy. Accurate metering of electricity generated or consumed provides key data inputs for accurate settlement calculations. Direct measurement of a generator or load participant through telemetry allows the ISO to manage and monitor power generation in real-time.”
Therefore the functions of the Meter Service Providers (MSPs) and Meter Data Management Agents (MDMA) were crucial to provide Schedule Coordinators (SCs) with…Settlement Quality Meter Data (SQMD).
Meaning all meter data went through a vigorous Validation, Editing and Estimation (VEE) process and then parceled for various functions related to customer billing, load and financial settlement, load profile creation and procurement forecasting, demand response programs, etc.
So WTF is Settlement Quality AI Data (SQAID) for microgrids?!
By definitiion, microgrids are modern, small-scale versions of the centralized electricity system. They are a localized group of electricity sources and loads that normally operates connected to and synchronous with the traditional centralized electrical grid (macrogrid), but can also disconnect to “island mode” — and function autonomously as physical and/or economic conditions dictate.
SCADA is a control system architecture that uses computers, networked data communications and graphical user interfaces for high-level process supervisory management, but uses other peripheral devices such as programmable logic controllers and discrete PID controllers to interface to the microgrid.
The SCADA computer system handles operator interfaces that enable monitoring and the issuing of process commands, such as controller set point changes. However, the real-time control logic or controller calculations are performed by networked modules that are connected to field sensors and actuators.
So Why is any of this Important?!
So imagine the value created by SQAID, instantaneously read from Smart AI meters, devices and sensors by an AI Schedule Coordinator which controls microgrid operations and facilitates authority having jurisdiction compliance requirements. There could be billions of energy dollar savings created by the use of a common SQAID algorithm.
Imagine an AI Agent using SQAID and acting in the capacity of the traditional MSP, MDMA, SC and SCADA process. And served from a microgrid, guaranteeing reliability, health and life safety, security, and most importantly human and AI comfort and long-term sustainability.
About the Author
Patrick A. Weller is known for his authorship of the Smart Modernization and Retrofit Technology Solutions (SMARTS) Program recognized by the American Recovery and Reinvestment Act of 2009. His historic monetization of ARRA investment capital was achieved by using an ironclad investment grade audit and a standard of care that withstood the “test of scrutiny” of the federal, state, local government and Investor Owned Utilities.
Related online information:
City of Grover Beach Goes Green: https://www.grover.org/ArchiveCenter/ViewFile/Item/111
SOL DOMINIQUE I | Dennys W. Sacramento Power Purchase Agreement: https://youtu.be/EMYsIiKZWdk
If there is one thing I have learned in the last two years in the cryptocurrency world, it’s that things change so quickly in this sector, it can humble anybody. Anyone who says he knows what he is talking about, doesn’t!
Still, it’s New Year’s Day, so what the heck? I’ll put my neck out there with 12 predictions for 2018:
1. Ripple will lose its luster
People will realize that Ripple is a cool business but that it doesn’t actually require a protocol token to work. There are a number of other protocols (including some VERY high market cap ones) that also don’t require a token to work. The market will start to weed them out. That said, Ripple will be a valuable company because of the service it provides. It just won’t be a valuable protocol. The protocol multiples are much higher. Disclosure: I previously owned XRP, but don’t hold any now.
2. The Lightning Network will face the big test
Either Lightning will work and Bitcoin will regain its prominence as THE default cryptocoin in the world, or it will fail miserably and Bitcoin will continue to fall further and further behind as a coin of relevance. If that happens, Bitcoin Cash becomes the “real” Bitcoin.
To me, it’s a coin toss. I’m playing catchup on this one as I sold a lot of my BCH when it came out in favor of BTC, thinking SegWit2x would happen, but I was wrong. Ouch.
3. The privacy wars will begin as people recognize the difference between pseudonymity and anonymit
When they do, the competition will intensify for adoption of a “privacy” coin like
- Zcash (disclosure: They’re a client and I’m biased)
- Monero (which seems to be the privacy coin of choice on the Dark Web).
- Dash has amazing marketing (Amanda B. Johnson is probably the best in the business) but questions abound about the reputation of some of the leadershp team, weaknesses within the masternode setup and possibly even legal implications.
- PIVX which has a fun team and is really fast plus can integrate with Slack.
I predict that one or two of these coins will be recognized as the next Bitcoin within the mainstream media. There are a lot of factors here that will determine the winner, but I it will come down to whose security mentality is superior. We’ll only know that after a few big attacks.
4. The DAO market will take off
The DAO market, currently led by companies like Aragon, Colony, District0x, and DAOStack — will soon have a few proof-of-concept DAOs running. It’ll be interesting to hear the metrics they report in terms of set up time, user base, types of activities/DAOs in operation, etc. These protocols have massive opportunity (as I wrote previously on VentureBeat), though the vision in this market may be way ahead of the tech.
If there are 50 or so legit projects in pilot in 2018, then this sector is on a fast pace. I’m planning a pilot DAO of my own, by the way, so I’ll be updating readers about what the process looks like from the inside.
5. More decentralized AI startups will emerge
Last month, blockchain-based AI-as-a-Service startup SingularityNet’s ICO sold out in 60 seconds, pulling in $36 million. It set the $36 million cap after receiving requests for $360 million worth of its tokens from investors. There will be more ICOs like this one. Since money attracts money, we will see a lot of people leaving high priced AI jobs at big tech firms like Google and Facebook to pursue billion-dollar paydays at the next crypto-AI protocol. We will probably see 10 crypto AI ICOs that are at least $50 million. For more on decentralized AI, see my earlier article here.
6. Ethereum will be the standard … or not
By now, we all know that CryptoKitties brought the Ethereum network to its knees. Ethereum cofounder Vitalik Buterin knows it too and is very aware of the challenges he and his team face.
I had the opportunity to spend time with Buterin and Ethereum developer Vlad Zamfir a few weeks ago, and — although I think the “Vitalik as Wunderkind” narrative is dangerous — I walked away from our conversation very impressed. These two are very thoughtful, articulate, open-minded, and brilliant. There are a ton of things that need to be done to prepare Ethereum to handle the scale and speed the community will need (as I have outlined before), but these two are both smart and humble enough that they could do it.
That said, with all of Ethereum’s issues, there is room for other, newer blockchains like NEM, QTUM, EOS, or AION to start closing the gap, if not overtake the platform. If we see more than 100 projects built on any one of those platforms, I think we’ll be seeing the emergence of an Ethereum contender. If not, Ethereum will keep its hold on the lead.
7. Interoperability protocols will remain immature
I’m rooting for interoperability players — like Polkadot, Cosmos and new entrants like Lamden and Metronome — that will enable transactions and information exchanges between different blockchains. But I think it will be a while before they really get off the ground. In the long term, they’ll enable a multi-blockchain world. But in the short term, they’ll increase the load on key blockchains like Ethereum and Bitcoin and so won’t likely get much love from those communities just yet. I expect them to be a bit quiet for the time being. For more on this sector, see my earlier story.
8. We’ll see more Crypto Valleys beyond Zug
I lead a quarterly trip to “Crypto Valley” in Zug, Switzerland as part of the Crypto Explorers Association. The next one is January 29, which is sold out, but applications are open for the April trip. See the site for details.
We’ve been approached by governments in places like Oman and Panama that want to set up Crypto Valleys in their geographies and use CryptoExplorers as a way of introducing themselves to the world. They have studied what Zug has done to drive innovation and job creation and they want to do that themselves.
That tells me governments in tier 2 or tier 3 locations see blockchain/crypto as a way to “leapfrog” tier 1 economies in the way Estonia did in the 1990s, going straight to a digital first country. (You can read about my experiences as an e-citizen of Estonia here.)
These new Crypto Valleys will try to become blockchain-first locations. And I think we will see two or three of them make it. I’m not talking about Dubai, Singapore, Tel Aviv, or Berlin. I’m talking about unexpected places like Bratislava, Florianopolis, Panama City, and Muscat.
9. Crypto will go mainstream
Three of the top 10 retail brokers in the U.S. will allow you to buy 1-5 cryptocurrencies directly from their websites, just as you do today with stocks or mutual funds. Coinbase, currently the most popular exchange for crypto newcomers, only supports four currencies at the moment. Unless it wants to start losing ground, it will need to improve its infrastructure and execute a plan for world domination.
10. ICOs will go mainstream
I think we will see the first legitimate ICOs take place on Indiegogo in February, or March at the latest. I predict that, over the course of the year, we will see at least nine more, for a total of 10 in 2018. (Here is Indiegogo’s original announcement.)
11. Reverse ICOs will intensify
Kik did the first reverse ICO; now YouNow is about to do one. I think we’ll see at least 15-20 more reverse ICOs in 2018. A “reverse ICO” is when an existing company decentralizes itself and issues tokens to its members to stimulate a circular economy. This is as opposed to a ICO from a brand new startup project. All this activity will require the services of a new type of professional — the tokenization consultant — so expect to see that role catch on in 2018, too. For more on reverse ICOs, see here.
12. Regulation will get more serious
I’m cautiously optimistic U.S. authorities will be relatively lenient on blockchain startups so as not to impede innovation. But companies that are negligent or outright deceptive will get shut down. ICOs will have to adhere to KYC/AML policies, and their solutions will have to scale. If you look at SEC Chairman Jay Clayton’s letter from December 12, he is pretty clear about the importance of crypto-innovation, and he deserves credit for that. But he is also putting decentralized projects on notice, saying essentially that ignorance of the law is not a defense. I think this is a healthy balance.
The bottom line: If you thought 2017 was crazy, get ready for a lot more upheaval in 2018. This crypto thing is just getting started. Happy New Year!
Jeremy Epstein is CEO of Never Stop Marketing and author of The CMO Primer for the Blockchain World. He currently works with startups in the blockchain and decentralization space, including OpenBazaar, IOTA, and Zcash.