Machine Learning. Human Intelligence. Smarter Decisions.
What is AI?
AI is defined as “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”
AI is not a new idea.
The ideas and theories behind AI first came to light during the summer of 1956. A small team of mathmaticians and scientists gathered at Dartmouth College for nearly two months to discuss and brainstorm with each other. The initial proposal set before the group focused their efforts on “computers, natural language processing, neural networks, theory of computation, abstraction and creativity.” This workshop is considered to be the beginning of our exploration and discovery into the world of Artificial Intelligence.
Although the idea of AI and is not new, we are certainly seeing new techinical developments and the emergence of use cases, especially within blockchain technologies.
Difference between AI and Human Intelligence
AI is computer technology developed by humans which focuses on programs and algorithms that allow learning and adaptation to occur. AI uses a variety of functions and advanced algorithms to process, inspect, and find patterns in data sets.
Human Intelligence incorporates those things which computers cannot replicate, such as emotions, motivation, awareness, and applying logic based on personal experiences. HI is a cognitive process that incorporates active learning and reasoning, and those can be processed differently in every human’s mind.
The Six Core Components
of Artificial Intelligence
Natural Language Processing
Described as the science that enables machines to analyze, translate, and interpret data to solve complex problems. The three types of ML are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised: Labeled data is used find patterns and correlations between data. The input and the output data are defined.
Unsupervised: Unlabeled data is used to help build data relationships, find patterns, and identify correlations within the data set.
Reinforcement: Rules are defined within programs, and the machine executes a task based on the results of the established rules.
A quote from Jason Brownlee describes ML like this: “Machine Learning is getting computers to program themselves. If programming is automation, then machine learning is automating the process of automation.”
Neural Networks are systems designed to learn how to perform specific tasks by analyzing data and then executing those tasks without limitations of existing rules. They are networks that are designed to operate in a similar manner to our own biological signal-sending brain. These networks are also called Artificial Neural Networks (ANNs).
A system of nodes exist and the use of inputs and outputs determine if (and how) data is sent to the next layer. Programmers develop powerful algorithms to assist with training these networks and improving the accuracy of their results.
Repetitive training and algorithm tuning results the machine’s ability to rapidly process and classify data as quickly as the data can proceed through the layers. Internet search engines such as Google are excellent examples of a finely tuned neural network in action.
The field of Robotics is an exciting area making incredible advancements using AI. When combined with Machine Learning, robots can be programed to use both real data and past outcomes to learn how to solve problems and improve efficiencies.
The combination of robotics, sensors, and AI can be seen in many industries, such as in manufacturing, automotive, oil and gas, retail, security/safety, and logistics. The healthcare industry has experienced remarkable growth in robotics, a good example being eye surgeries. Robotics are utilized when there is a need for high levels of precision at a high rate of speed with extremely low tolerance for variance.
In addition to Machine Learning, Deep Learning is incorporated so that robots can interact with humans and learn with every interaction they have.
Natural Language Processing
Natural Language Processing (NLP) is used in communications between computer systems and humans and is considered to be a type of machine translation. Within AI, NLP helps computers interpret human language and bridges gaps between our diverse languages and how a computer analyzes that language in both speech and text.
NLP is important because it allows computers to translate words in a consistent manner. Systems using NLP can process massive amounts of data without growing tired or making errors due to fatigue.
NLP within AI is critical because within Machine Learning (supervised and unsupervised) there will always be an element of human dialect, grammar rules, and misused terminology. NLP helps analyze those variances in text and voice and translates them into machine language that a computer can understand. Thank you Spell Check and Alexa.
Expert Systems replicate the decision-making process and intelligence of expert humans. Samudyata Bhat provides a great explanation: “Breaking down an expert system essentially is an AI software that uses knowledge stored in a knowledge base to solve problems.”
Systems designed like this rely heavily on information and knowledge stored within the system for constant analysis based on user searches and requests.
Processes executed within Expert Systems typically utilize if/then statements or rules, comprised of two parts: “If” is the precondition, and “Then” is the consequence.
Expert Systems such as robust search engines and document-creating/editing software are designed to be reliable, consistent, responsive, and thorough.
One limitation of expert systems that is worth of mentioning is that they lack creativity and cannot replicate our ability to develop a unique solution.
Fuzzy Logic is reasoning that occurs when uncertainty exists, or when a condition exists that might be difficult to determine if a given condition is true or false. It’s “fuzzy” because it might be vague or relatively undefined.
Within AI, Fuzzy Logic exists within Expert Systems when acceptable reasoning can replace accurate reasoning when uncertainties are injected into a process. We use Fuzzy Logic all the time when we ask a question and are given an “maybe” response.
Fuzzy Logic is integrated into Expert Systems and are prevalent in control systems such as highway traffic systems and altitude controls in aerospace. This kind of logic is also present within Human Resource and Personnel Evaluation programs.
The Future of AI in the Digital Asset Industry
Blockchain and Artificial Intelligence
AI is coming to our industry and it’s going to be a gamechanger. A number of excellent articles have been published regarding blockchain technology and how AI will be used within the digital asset industry. Links to a handful of these articles are posted below, and here is a portion of an excellent article on Nasdaq.com written by Anthony Clarke.
One of the key benefits of using AI in the blockchain industry is increased security. With AI, smart contracts and blockchain oracles can be more secure by detecting and preventing fraud, hacking, and other malicious activities. AI can also help detect and prevent code errors, which can help ensure that the contract is executed correctly.
Another benefit of using AI in the blockchain industry is increased efficiency. AI can automate the process of creating and executing smart contracts, making it faster and more cost-effective. It can also help filter and validate the data provided to the smart contract, ensuring that the contract is executed only when the conditions are met. This can reduce the time and costs of creating and executing contracts.
Using AI in the blockchain industry can lead to new business opportunities in addition to these benefits. For example, businesses can use AI-powered smart contracts to create new products and services, such as predictive contracts, which can help predict a contract’s outcome before it is executed. This can help businesses to make better decisions and to improve their overall performance.” See the full article here.
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ArchAngel Token and
the Archa Ecosystem
The ArchAngel project believes that AI is going to play a massive role in the development and use of blockchain technologies. The amount of data at our fingertips is massive in size, and the use of AI can help translate that data into useful products and powerful utilities.
The ArchAngel Ecosystem is built around projects that mutually support each other while they build their core utilities. Projects remain independent in ownership and management control but work together to help accelerate the delivery of features and utilities to the entire ecosystem.
We’re looking for ambitious like-minded people and projects (launched or still early in development) who are committed to building and releasing valuable products and services. Our users are our focus and our success as a collective digital asset community is our primary objective. Please check out our Whitepaper, Technical Roadmap, and our Ecosystem Roadmap for more details. You can use the below form to send us a note, we’d love to hear about your ideas. Join our Telegram and subscribe to our Twitter account as well.
Guardian Token and
the Guardian Platform
The Guardian Token is one of Archa’s Ecosystem tokens. Guardian Platform went live on 8 April 2022 with MVP form features and utilities. The platform’s underlying digital asset, Guardian Token, executed a perfect ERC20 launch on Uniswap 24-hours later on 9 April 2022.
Guardian Platform’s objective is to provide users with valuable features, tools, and utilities that cover a broad spectrum of our digital asset industry. It is being designed and built to serve as our launchpad into the digital world, armed with the tools we need to successfully execute our financial goals and strategies. The platform offers exposure for new projects through project-managed dashboards and official announcement boards. It can add Archa Ecosystem projects, features, and utilities to include Artificial Intelligence capabilities. Joining the Archa Ecosystem provides ecosystem projects immediate access to Guardian Platform and its dynamic, powerful, robust, and growing arsenal of digital asset features and tools.
Since launch, the project team has unleashed 8 Phase 1 features: Token Data, Global Xchange, Market News, Stage 5, Herald, the Marketer’s Hub, Technical Analysis tools, and a Career Connections portal.
The Platform recently entered Phase 2 of its technical development and has been following a robust Integration Roadmap and Work Breakdown Structure using an agile software development process. The Project Team has worked directly with multiple domestic and international development teams to execute concurrent platform development initiatives. Development Phase 2 is now underway, and work during this phase revolves around a custom user account framework that was developed and released with Stage 5 and Herald. Additional Phase 2 initiatives include MyGuardian, BagTrax, Genalock, and continued media content delivery, library, and media management with BrainStem.
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