What is artificial intelligence?     

What is artificial intelligence?       

While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper (PDF, 106 KB) (link resides outside IBM), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence" (PDF, 89.8 KB) (link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach (link resides outside IBM), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted in Gartner’s hype cycle (link resides outside IBM), product innovations like, self-driving cars and personal assistants, follow “a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovation’s relevance and role in a market or domain.” As Lex Fridman notes here (01:08:05) (link resides outside IBM) in his MIT lecture in 2019, we are at the peak of inflated expectations, approaching the trough of disillusionment. 

As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment. To read more on where IBM stands within the conversation around AI ethics, read more here.

Types of artificial intelligence—weak AI vs. strong AI

Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Artificial intelligence applications

There are numerous, real-world applications of AI systems today. Below are some of the most common examples:

• Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.
• Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.  
• Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
• Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

 We decided to build our foundation

Here comes our role to use this super system to find the smallest opportunities and investments in all markets of the world to extract profits from it.

To build a strong foundation quickly, we had to pump a lot of money into our project, so we made a decision so that everyone contributes with us, we win, and the shareholders win, but we made things easier for the shareholders so that everyone can contribute with us to earn profits even at $10, and with this step everyone can win with us

Among the most important of our future projects is the participation with (Bit Torrent) project and the acquisition of a currency BTT

Based in San Francisco, Rainberry, Inc. is the company behind the largest decentralized P2P communications protocol for distributing data and large files over the Internet. When the BitTorrent protocol was introduced, it transformed the world of file sharing by speeding up downloads for both individual users and organizations needing to transfer large amounts of data. Before BitTorrent, file downloads were initiated from a centralized server or a single user (a peer), resulting in slow download speeds. The BitTorrent protocol addressed this limitation by enabling the download and upload of files between many users. Millions of users began to use the BitTorrent protocol to download and share files, and companies began to use the protocol to distribute data more efficiently. Today, the BitTorrent protocol powers a significant percentage of the world’s Internet traffic each day. It isn’t just the largest Peer-to-Peer network, it’s the foundation of Web3, and one of the Internet’s largest global communities. Proof that the technology is more relevant than ever, robust, and now driven by the power of blockchain.

Currently, the company develops products across two brands, BitTorrent (https://www.bittorrent.com) and µTorrent (https://www.utorrent.com), which offer popular torrent download clients for Windows, Mac and Android. The applications have been downloaded over 2 billion times. In the Summer of 2019, the company introduced blockchain into its applications by releasing BitTorrent Speed, a feature that introduced the use of a utility token, BitTorrent Token (BTT), into the BitTorrent ecosystem. BitTorrent Speed automatically bids tokens to other users to help drive faster download speeds and a healthier BitTorrent protocol overall. It was the first implementation of blockchain inside the BitTorrent ecosystem, and one of the first mass adoption use cases of blockchain.

Today, the company is working on the next generation of torrent products to vastly simplify the experience and make it easier than ever to stream and download torrents. It also continues to work on enhancements to BitTorrent Speed, including giving users more control of how they use BTT for faster download speeds. Other initiatives that leverage both the BitTorrent protocol and BTT are also well into development. BitTorrent File System (BTFS) will pave the way for a fully decentralized file storage system. BTFS enables distributed file storage that is free from a centralized platform. Powered by BTT and the TRON blockchain, “Hosts” can rent their hard drive space to receive BTT, while “Renters” can use BTT to purchase decentralized storage space.

The company is on a fast track to expand the use cases of its decentralized protocols by creating the next generation of live streaming technology. It will reshape the industry landscape by utilizing the company’s robust global P2P network to connect content creators directly to their audience without needing a centralized platform.

The most common question. What guarantee the credibility of the project?

Answer: Because of the technological progress that has taken place during the past 50 years, many projects have been invented in several aspects. In the fields of energy and technology, including projects (encrypted digital currencies)

   So who was the guarantee for all of that. We are now seeing where these projects have reached and the huge amount of profits that investors have received in these projects since their inception. And now is the stage of development of artificial intelligence. Which is stronger than all previous projects. Because she will be the director and dominant of those projects. Because all previous projects will be managed and organized by artificial intelligence very soon.

The only difference we made is to develop quickly. (Is that we decided to open the door to contribute with us). To develop together and win together.

It depends on you and the extent of your absorption and understanding of artificial intelligence.

Can you contribute $10 and try this new project? Or will you wait to miss the train like previous projects??

All you need to get started with us is $10

Decide what you want