Artificial intelligence, commonly known as AI, presents a wide-spreading innovation across a wide array of industries. The term in question describes a branch of computer science that deals with creating intelligent systems capable of performing tasks that often require human intelligence. For example, this includes language interpretation, research, image recognition, pattern recognition and predictions.
AI and machine learning are not new technologies. The trend started over 65 years ago, but everyone is talking about AI these days as it is the new kid on the block.
The AI craze can be justified; today we have an efficient way to transfer, store and compute a broad amount of data in a manner that wasn’t possible in the past.
Therefore, main reasons for the hype lie in the availability of more computing power, massive amounts of data, advanced algorithms, and huge investments by big tech companies, governments, individual investors and universities.
It can be said that Generative AI kickstarted the craze as it has brought the essence of AI into everyday life. Real-world utilities are typically followed by massive adoption. Since engineers can train AI to engage in basic human tasks, people worldwide acknowledged it as a useful tool.
A good example is the notorious ChatGPT created by OpenAI. After it has been fed with broad amounts of divergent data and provided with parameters of how to use it, ChatGPT was able to do tasks similar to a human being.
When Microsoft announced launching a ChatGPT-powered form of its native Bing search engine, it took a big portion of the market. Having a new form of AI tools provides a competitive edge on the market.
Keep in mind that the current technology is not yet ready to be used in the most efficient manner. This has been confirmed by recent research. Simply put, it comes with certain errors. For example, if you ask ChatGPT a question about something it doesn’t know, the chatbot either fails to answer or provide accurate information.
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VisitChatGPT refers to a chatbot created by the AI research company OpenAI in November 2022. The AI language model has taken the world by storm as it inspired a wide array of debates and brought to the table a few real-world utilities. Users started to use ChatGPT for many human-like tasks, from writing emails to workout plans and bedtime stories for children.
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VisitThe ‘GPT’ part means ‘Generative Pre-training Transformer’ and refers to the manner the chatbot processes language. What sets this chatbot apart from past chatbots is that it was trained using reinforcement learning from human feedback.
This particular method includes the use of human AI trainers and a reward system to develop the chatbot enough to answer follow-up questions, admit mistakes, and challenge inaccurate notions.
ChatGPT is a product of a category of machine learning Natural Language Processing models known as Large Language Model (LLMs). This model is based on digesting massive volumes of text data and input datasets.
The chatbot is based on the Generative Pre-trained Transformer (GPT) architecture. Transformer models utilise a mechanism known as ‘attention’ to weigh the influence of divergent words into generating a response to natural language inputs.
For example,if the transformer model had to generate a response to the sentence ‘The cat chased its tail’, it would acknowledge that the ‘cat’ is the subject and therefore, more important than the ‘tail’.
The training of Generative AI is typically a two-step procedure – pre-training and fine-tuning. During the pre-training stage, the model is filled with a broad amount of data sources with the objective to understand the statistical patterns of a language.
The fine-tuning phase refers to training the model on a more narrow dataset generated with the aid of human reviewers and following particular guidelines provided by the OpenAI company. This phase teaches the model to respond more specifically to certain inputs and maintain a useful interaction with users.
Once the model has been trained, it generates responses using the ‘autoregressive’ method; in other words, it begins with an input message, and predicts the next word until it is able to form a complete sentence.
AI has been the flavour of 2023. Additionally, it is estimated that more than 60% of trading on major stock markets around the globe is algorithmic due to a high occurrence of trading bots and algorithmic portfolio management.
Even though it is hard to precisely state the percentage of algorithmic trading, machine learning techniques are dominating the industry for risk management, as well as spotting market trends and market dynamics.
When it comes to market conditions, the use of machine learning technologies has been in the hands of experts known as ‘quant traders’, but it is becoming available to retail traders and the cryptocurrency market as well.
Two key powers delivered by AI are insights and automation; both of these play a significant role in trading. AI-based crypto trading bots entered the crypto market as being used by crypto investors to automate the buying and selling of positions based on key technical indicators, just as they are used within the regular AI stock trading space.
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VisitAI and blockchain technology can be acknowledged as two great innovation drivers over the last decade which already influenced various sectors such as the world of finance and supply management. Combining these two technologies could bring more potential benefits to the development of many sectors.
While blockchain provides a decentralised and transparent way to store data, Generative AI enables creativity and self-learning. If you combine these two innovations, technology could be at the same time adaptive, intelligent and secure.
To learn more about blockchain technology, check out this article: 'What is a Blockchain'.
It cannot be disregarded how AI has already helped in transforming ordinary systems and many wonder whether it could do the same for the crypto realm.
It has been stated that blockchain can't be spelled without AI. Let's check out why.
Crypto trading exists 24/7 without borders; it includes a wide range of digital assets and crypto markets. Algorithm-based trading bots have existed for a while now, but they are getting exponentially better. The time of doing this manually has come to its end.
There are several key benefits AI could bring to cryptocurrency trading to enhance it and potentially contribute to more investment opportunities.
Let’s start with real-time market monitoring; one of AI’s significant upper-hands is the ability to analyse and process vast amounts of data in real-time. When a market operates 24/7, prices can change within minutes.
By constantly monitoring market conditions and assessing multiple parameters, AI can spot patterns and market trends that may not be visible to the human eye. A real-time analysis can aid crypto traders in gaining in-depth knowledge and making informed decisions. For example, an AI language model is able to support market as well as technical analysis, and recognise chart patterns to generate trading signals and provide price predictions.
Another important feature of AI crypto trading bots is their ability to learn from past experiences. AI can conduct a historical data analysis of digital assets and the crypto market to improve performance over time. The capability to evolve makes AI crypto trading bots adaptable to new market dynamics
AI could improve trading in a volatile market such as the crypto space because it removes human emotion and bias from the picture. The judgement of human traders can be clouded by emotions such as greed and fear, along with being influenced by other people.
Finally, AI-based algorithms can be designed to execute trades automatically based on predetermined indicators. This eliminates the need for human involvement in every crypto trading decision as well as the negative influences of error and emotion. Crypto traders would be enabled to take advantage of trading possibilities even when they are not monitoring the crypto market actively.
ChatGPT is built on the Transformer architecture as a type of deep neural network specifically designed for handling sequential data such as text. ChatGPT has opened doors for traders, providing them with a powerful tool to revolutionise their strategies and streamline crypto trading operations. However, is it currently the ultimate tool for crypto trading?
The cutting-edge AI language model wasn’t specifically designed for trading, but it is able to analyse market news, sentiment and trends. One of its strengths is delivering a market sentiment analysis by dissecting news articles, social media and other data sources to determine the current market sentiment. The chatbot has the ability to deliver personalised insights and serve as an educational resource to crypto traders.
However, the language model comes with a few restraints. It seems that crypto trading bots cannot be replaced yet. Crypto trading bots have the ability to process massive data volumes, respond to real-time market dynamics and track trends.
For example, Chat GPT, at its current level of development, lacks context; its main strength lies in language-based tasks and it may lack the comprehension required for intricate trading strategies.
It is still too early to state that AI-based bots such as ChatGPT can totally replace specialised trading tools. The novel solution lacks key features that crypto users rely on when it comes to trading. However, when the technology evolves it has a potential to enhance cryptocurrency trading.
While AI may not be ready yet to take over the crypto trading space, its worth has been recognised within the Web3 ecosystem. Primarily, AI could increase productivity since Web3 projects will need to spend less time on tasks such as copywriting and marketing.
Secondly, by embedding AI functionalities within the Web3 space, people could create applications that operate on a decentralised and secure backbone and possess the power to provide intelligence-driven functionalities and adapt accordingly.
AI has the ability to enhance the operation of decentralised autonomous organisations (DAOs) as well. For example, since AI can analyse massive amounts of data, it could enhance voting and governance strategies.
When it comes to the space of decentralised finance (DeFi), AI could provide similar benefits as in cryptocurrency markets. Intelligence-driven services could analyse market behaviour, enhance the decision-making process, provide optimised financial instruments and predictive content based on historical data and relevant information.
In the past, the crypto space was the centre of breaking news due to some negative occurrences such as crypto scams. After these events, regulators became more hands-on and demanded compliance.
Auditing has typically been a time-consuming process. However, AI tools have been revolutionising the field of legal research by being able to automate document review and compliance.
AI can do the same when it comes to regulatory compliance within the crypto space. AI-driven algorithms could analyse blockchain transactions, identify potential violations and report suspicious activities. Overall, auditing could become more efficient and less time-consuming.
AI has been acknowledged as the driving force of the Metaverse. At the moment, the Metaverse is still a work-in-process. It needs better graphics, content, and interface. It is not an easy task to build immersive worlds, but AI could make it easier.
If you're interested in the Metaverse's development, why not read this article: 'Is Metaverse dead? What would it take for a revival'.
One of its significant applications within the Metaverse could be the development of digital twins as virtual representations of physical objects or processes. For example, digital twins can be used for divergent purposes such as assembly line testing, city planning or even conducting virtual surgeries.
The integration of Generative AI and the Metaverse ecosystem can produce dynamic immersive environments, where AI-based content can respond to users’ actions and create more engaging experiences. Additionally, it can enable novel forms of digital art and communication services.
In the financial context, tokenization refers to the procedure of issuing a digital token that runs on a blockchain. It is a digital representation of a certain asset. Tokenized assets are programmable and have smart contract functionality. This is where AI jumps in.
When we combine AI with tokenization, we can spot a bunch of new utilities. Let’s start with the improvement of smart contracts’ code; coding smart contracts is often a complex process, and even minor mistakes in the code can have big consequences. To diminish such a risk, an AI tool could be useful.
In the matter of ChatGPT or any other AI natural language model, the tool could streamline the process of smart contract creation and testing the code. If developers would write the code in natural language, the AI tool could reduce errors and manage additional risks.
AI can be further used to automate the process of smart contract execution; this could be done by interpreting the data created by smart contracts. Developers would be alerted to take corrective action, and the whole process would be less time-consuming.
In the tokenization process, AI tools can help to streamline multiple tasks involved in the creation and management of digital tokens. They can be utilised to assess the value of a certain asset by taking into account different metrics such as expert opinions, historical data and trends.
We have already mentioned that AI-driven tools are useful in the light of auditing and compliance. The same could be done here; AI can be utilised to monitor and manage the risks associated with tokenized assets. Aside from managing risk, this could enhance trust of the general public in the process of tokenization and execution of transactions.
Finally, AI can assist in enhancing the transparency on tokenized platforms. For example, AI tools could help in aggregating bids and ask quotes from all platforms in real-time and identify the best bid or ask to be executed in real time.
Such examples demonstrate that the combination of AI and tokenization could supercharge financial markets in the future and deliver more transparent and effective financial services.
AI is a groundbreaking tool but it comes with certain drawbacks. Many now see the potential of blockchain technology as a foundation of support for AI and claim that it could even help in resolving AI’s black box problem by delivering a transparent ledger to monitor decision-making processes.
Blockchain can support the design of decentralised AI tools which amounts to less biases in AI models. Organisations would be enabled to audit the algorithms and data used which would enhance the trust in AI tools.
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VisitLet’s lay a practical example – you have probably heard of AI-driven deep fakes that have tricked millions of people. Many people are afraid of what AI could become in the future. That's one of AI's biggest challenges.
On the other hand, blockchain has often been praised for its ability to authenticate that digital assets are truly what they claim to be. The technology known as zero-knowledge proofs will be vital for AI moving forward and gaining more trust within the general public.