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.
SingularityNET allows multiple AI computing agents to work as a whole to provide various services in a distributed and decentralized way.
For the first time, we have a financial substrate in the blockchain that lets us align diverse AI technologies and functions into a coherent financial and cognitive whole. The SingularityNET architecture incorporating block-chain smart-contracts and automatic payment will let diverse AIs integrate together into a single dynamic intelligence. AI agents incorporating the OpenCog AGI framework, Google Tensorflow and other powerful tools, interacting within the SingularityNET; will bootstrap the research and development of an AGI economy.
- Architectural Overview – the system architecture and high-level design
- Getting Started – instructions for getting SingularityNET running on your system
- Example Scenario – a non-trivial example of SingularityNET agent interaction
- SingularityNET API – the interfaces required to implement or call agents to perform services
There are seven major interacting components in the SingularityNET architecture:
- Network – the block-chain and smart-contract network used for agent negotiation and discovery
- Agent – the agent which provides services and responds to service requests by other agents in the SingularityNET
- Ontology – contains definitions of services available in SingularityNET. Ontologies are versioned and define the semantics of network operations.
- ServiceDescriptor – a signed immutable post-negotiation description of a service which can be performed by an Agent
- JobDescriptor – a list of jobs which tie a particular ServiceDescriptor with job-specific data like input and output data types, URLs, specific communication protocols etc.
- ServiceAdapter – a wrapper for AI and other services which an Agent can invoke to perform the actual services required to perform a job according to the negotiated ServiceDescriptor.
- ExternalServiceAdapter – a wrapper for interacting with external service agents in the SingularityNET universe.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
The agent server is responsible for communicating with AI Adapters which connect to individual AI systems and the rest of the network. You can run an Agent connected to the SingularityNET network as a server that runs stand-alone or as one that forwards requests for work to other servers running specialized AI services.
SingularityNET runs on Mac OS X, or any Linux which has Python 3 installed and Docker or Docker for Mac installed. For Windows you’ll also need Git Bash/MinGW or Ubuntu on Windows 10 WSL.
The core devs regularly develop on Mac OS X Sierra, Linux Mint Mate 18.2, and Linux Ubuntu 16.04 LTS among others.
Docker and Docker Compose are used heavily, so you must have a recent version of Docker installed to take advantage of most of the automation and to isolate yourself from the dependency hell which often results from installing software with complex dependencies directly onto your host development OS.
The current development demo runs from a
dev docker container which can be launched from your favorite bash terminal using our helper tool shell script:
This will bring up a set of docker containers and expose port 8000 to the local host machine. Visit the demo via:
in a modern browser.
Notes on running on Ubuntu under Windows Subsystem for Linux (WSL, Bash on Windows, etc) The trick to this is to install docker on Windows, then ensure the docker is in your path
- In Ubuntu:
- Validate the docker path and add it to your ~/.bashrc file.
There are two kinds of Service Adapter examples in the project: real AI integration and template examples designed to teach concepts.
singnet/agent/adapters contains working AI adapters that connect with AI services from OpenCog, TensorFlow, and Aigents, among others… Some knowledge of the underlying AI architectures and systems will be necessary to understand the code in these Service Adapters.
singnet/agent/examples contains examples that are designed to show how to do something without necessarily implementing real AI so you can understand the mechanics without needing to understnd any particular AI sytems.
Tests are handled by PyTest via Tox, but we’ve made it very easy for you.
Docs are not currently included in the source as they are changing rapidly. We do suggest you create the docs and look them over. Once this settles, we will likely have an online reference to these. We could use some help if you like writing documentation and don’t mind trying to keep up with a fast-moving project.
Please read CONTRIBUTING for details on the process for submitting pull requests to the SingularityNET project.
Here are some of list of the contributors who participate in this project.
A SingularityNET Agent provides document summarization services for corporate work groups. As inputs for this service, it might require:
- Glossary – a glossary of terms and entities relevant to the corporate service client
- People Images – a set of images representing people to be recognized
- Object Images – a set of images representing things to be identified
- Documents – a set of documents to summarize in accepted formats
The task of performing document summarization requires summarizing text; identifying relevant objects and people in images; ranking relevance; processing video to extract objects, people and a textual description; and generating a ranked summary of the document.
The SingularityNET Agent might perform the following services internally:
- Final Document Summary – assembling the parts and generating the final product
- Text Summary – processing the text to build a summary of text-only portions
The Agent might use ExternalServiceProvider agents to perform the following services:
- Word Sense Disambiguation – a sub-service used by the Agent’s Text Summary service to disambiguate words and meanings from text and context when more than one sense is possible and grammatically correct.
- Entity Extraction – a sub-service which extracts object identities from images and text which match the Glossary and Images entries.
- Video Summary – a sub-service which extracts object identities from images and text which match the Glossary and both Images inputs.
- Face Recognizer – a sub-service which identifies people from the People Images inputs
The architecture supports scenarios like the above where individual agents may provide subsets or all of the services required to deliver any Service in the ontology.
The base class for block-chain networks. NetworkABC defines the protocol for managing the interactions of Agents, Ontology, ServiceDescriptors, as well as Agent discovery, and negotiation. Each block-chain implementation will require a separate NetworkABC subclass which implements the smart-contracts and communication protocols required to implement the Network ABC API.
NetworkABC subclasses must implement:
join_network– creates a new agent on the block chain
leave_network– removes agent from the block chain
logon_network– opens a connection for an agent
logoff_network– closes the connection for an agent
get_network_status– get the agents status on the network
update_ontology– queries the block-chain and updates the ontology to current version
advertise_service– registers an agent’s service offerings on the blockchain
remove_service_advertisement– removes an agents service offerings from the blockchain
find_service_providers– returns a list of external service provider agents
This is the base class for all Service Adapters. Services can be AI services or other services of use by the network like file storage, backup, etc.
ServiceAdapterABC subclasses must implement:
perform– perform the service defined by the JobDescriptor
Additionally, ServiceAdapterABC subclasses may also implement:
init– perform service one-time initialization
start– connect with external network providers required to perform service
stop– disconnect in preparation for taking the service offline
can_perform– override to implement service specific logic
all_required_agents_can_perform– check if dependent agents can perform sub-services
- AIOHttp – The async web framework used to handle JSONRPC and HTML requests
- SQLAlchemy – Internal data storage
This project is licensed under the MIT License – see the LICENSE file for details
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.