artificial intelligence

Nym co-founder and Chelsea Manning discuss decentralization and privacy

Nym co-founder Harry Halpin and security and hardware consultant Chelsea Manning chat with The Agenda about privacy and the benefits of a decentralized VPN.

Users’ data privacy and the growing need for it to be protected is a topic that people worldwide are reminded of on a near daily basis. For example, just two days ago, on Dec. 11, Toyota warned customers about a potential data breach, stating that “sensitive personal and financial data was exposed in the attack.” 

Hacks, breaches and exploits happen so often that one could jokingly say that user data breaches rival the rugs and protocol exploits that crypto is infamous for. To name a notable few, there was the Kid Security parental control app hack, which resulted in 300 million data records being compromised.

Consumer genetics and research company 23andMe had a breach in October that put 20 million records at risk. Even MGM was hacked in September, and estimates suggest that the hack cost the production studio at least $100 million.

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Google’s ‘GPT-4 killer’ Gemini is out, here’s how you can try it

Google has deployed its newest weapon in the AI arms race, a new artificial intelligence model that it claims is smarter and more powerful than OpenAI’s GPT-4.

Tech giant Google has officially rolled out Gemini, its latest artificial intelligence (AI) model that it claims has surpassed OpenAI’s GPT-4.

On Dec. 6, Google CEO Sundar Pichai and Google DeepMind CEO and co-founder Demis Hassabis announced the launch of Gemini in a company blog post.

The AI model has been optimized for different sizes and use cases (Ultra, Pro, Nano) and built to be multimodal to understand and combine different types of information.

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How is artificial intelligence revolutionizing financial services?

This article explores how artificial intelligence is transforming the financial services industry, from fraud detection to customer service and beyond.

What is the future of AI in financial services?

The future of AI in finance is exciting, with the potential to improve efficiency, accuracy and customer experience. However, it will be essential for financial institutions to carefully manage the risks and challenges associated with the use of AI.

The use of AI in financial services has the potential to significantly improve the sector. Several facets of finance have already been transformed by AI, including fraud detection, risk management, portfolio optimization and customer service.

Automating financial decision-making is one area where AI is anticipated to have a large impact in the future. This could involve the examination of massive amounts of financial data using machine learning algorithms, followed by the formulation of investment recommendations. With AI, customized investment portfolios might be constructed for clients depending on their risk appetite and financial objectives.

In addition, AI-powered recommendation engines could also be developed to offer customers targeted products and services that meet their needs. This could improve customer experience and satisfaction while also increasing revenue for financial institutions.

However, there are also potential challenges associated with the use of AI in finance. These include data privacy concerns, regulatory compliance issues, and the potential for bias and discrimination in algorithmic decision-making. It will be important for financial institutions to ensure that AI is used in a responsible and ethical way and that appropriate safeguards, such as transparent algorithms and regular audits, are in place to mitigate these risks.

What are the benefits and potential drawbacks of AI in the financial services industry?

The financial services industry can enjoy several benefits from AI systems, such as automating mundane tasks, improving risk management and swift decision-making. Nevertheless, the drawbacks of AI, such as security risks, potential bias and absence of a human touch, should not be ignored.

Potential advantages of AI in the financial services industry include:

  • Improved efficiency: AI can automate routine processes and reduce the need for human intervention, improving efficiency and reducing costs.
  • Better risk management: AI can analyze vast amounts of data to identify potential risks and prevent losses.
  • Enhanced customer experience: AI can provide personalized services and round-the-clock assistance, improving customer satisfaction.
  • Faster decision-making: AI can analyze data and make decisions much faster than humans, enabling financial institutions to respond quickly to changing market conditions.

The possible disadvantages of using AI in the financial services industry consist of:

  • Security risks: AI systems can be vulnerable to cyberattacks, posing a security risk to financial institutions and their customers.
  • Privacy concerns: The use of AI in financial services can raise concerns about data privacy, as the technology requires access to large volumes of personal and financial data, which must be secured and protected from unauthorized access or use.
  • Bias: AI systems can be biased based on the data they are trained on, potentially leading to discriminatory outcomes.
  • Regulatory challenges: The use of AI in financial services is subject to regulatory oversight, and compliance with regulations can be challenging.
  • Lack of human touch: Customers may prefer interacting with humans for certain financial services, such as complex financial advice or emotional support during difficult financial situations.
  • Job displacement: The use of AI in financial services may lead to job displacement as certain tasks become automated.

What is the use of chatbots and virtual assistants in the financial industry?

Chatbots and virtual assistants are proving to be valuable tools for financial institutions looking to improve the customer experience, reduce costs and operate more efficiently.

Chatbots and virtual assistants are utilized to provide individualized services and assistance, which enhances the client experience. Customers can communicate with these AI-powered tools in real-time and receive details on their accounts, transactions and other financial services. They can also be used to respond to commonly asked inquiries, offer financial counsel and assist clients with challenging problems.

Suppose a bank customer wanted to check their account balance or ask a question about a recent transaction, but the bank’s customer service center was closed. The customer can make use of the bank’s chatbot or virtual assistant to receive the information they require in real-time rather than having to wait until the following day to speak with a customer support agent.

The virtual assistant or chatbot can verify the customer’s identification and give them access to their account balance or transaction details. If the customer has a more complex issue, the chatbot or virtual assistant can escalate it to a human representative for further assistance. This means that AI-powered chatbots and virtual assistants can provide immediate responses to customer inquiries, reducing wait times and improving customer satisfaction.

Because they are accessible round-the-clock, chatbots and virtual assistants are useful resources for clients who require support outside of conventional office hours. Through the automation of repetitive processes and the elimination of the need for human support, they can also assist financial organizations in cutting expenses.

How does AI help in fraud detection and risk management in financial services?

AI is proving to be a powerful tool for financial institutions looking to improve their fraud detection and risk management processes, enabling them to operate more efficiently and effectively while minimizing potential losses.

Here are the steps explaining how AI helps in fraud detection and risk management in financial services:

  • Data collection: The first step entails gathering data from multiple sources, including market, customer and transactional data. Then, machine learning models are trained using this data.
  • Data preprocessing: Once the data has been gathered, they need to be cleaned up to get rid of any errors or inconsistencies. This guarantees the reliability and accuracy of the data.
  • Machine learning modeling: To identify potential fraudulent actions or risks, machine learning algorithms are subsequently employed to examine the preprocessed data. Algorithms, for instance, can be trained to spot fraudulent behavior patterns in transaction data or to forecast possible hazards linked with investments.
  • Real-time monitoring: AI systems are then used to keep an eye on transactions and spot potential fraud. This makes it possible for financial institutions to act fast and stop losses.
  • Compliance: AI can also assist financial organizations in meeting legal standards for risk and fraud management. For instance, AI algorithms can be used to spot potential contraventions of Anti-Money Laundering (AML) laws and pinpoint areas where risk management procedures need to be improved.
  • Continuous improvement: AI models need to be updated and enhanced continuously based on fresh information and user input. This guarantees that the models will continue to be reliable and efficient in identifying fraud and controlling risks.

Machine learning approach to fraud detection

How are machine learning, deep learning and natural language processing (NLP) utilized in finance?

Machine learning, deep learning and NLP are helping financial institutions improve their operations, enhance customer experiences, and make more informed decisions. These technologies are expected to play an increasingly significant role in the finance industry in the coming years.

Financial organizations may make better decisions by using machine learning to examine massive volumes of data and find trends. For instance, machine learning can be used to forecast stock prices, credit risk and loan defaulters, among other things.

Deep learning is a subset of machine learning that utilizes neural networks to model and resolve complicated issues. For instance, deep learning is being used in finance to create models for detecting fraud, pricing securities and managing portfolios.

Natural language processing (NLP) is being used in finance to enable computers to understand human language and respond appropriately. NLP is used in financial chatbots, virtual assistants and sentiment analysis tools. It enables financial institutions to improve customer service, automate customer interactions and develop better products and services. 

What is the role of artificial intelligence in the financial services industry?

AI is proving to be a powerful tool for financial institutions looking to improve their operations, manage risks, and optimize their portfolios more effectively.

Artificial intelligence (AI) is playing an increasingly vital role in the financial services industry. Predictive analytics, which can assist financial firms in better understanding and anticipating client demands, preferences and behaviors, is one of the most well-known uses of AI. They can then use this information to create goods and services that are more individually tailored.

Moreover, AI is also being utilized to enhance risk management and fraud detection in the financial services industry. AI systems can swiftly identify unusual patterns and transactions that can point to fraud by evaluating massive amounts of data in real-time. This can assist financial organizations in reducing overall financial risk and preventing fraud-related losses.

In addition, AI is being used for portfolio optimization and financial forecasting. By utilizing machine learning algorithms and predictive analytics, financial institutions can optimize their portfolios and make more accurate investment decisions.

The impact of artificial intelligence on financial services

9 examples of artificial intelligence in finance

Discover how artificial intelligence is transforming the financial sector with nine examples of AI in finance.

Artificial Intelligence (AI) is transforming the financial sector, revolutionizing how banks, financial institutions and investors operate. Here are nine examples of AI in finance, and how they are changing the industry:

Fraud detection

AI algorithms can analyze transactions in real time, detect anomalies and patterns that may indicate fraudulent activities, and alert banks to take appropriate actions. An example of fraud detection using AI is PayPal’s fraud detection system. PayPal uses machine learning algorithms and rule-based systems to monitor real-time transactions, and identify potentially fraudulent activities.

The system examines data points like the user’s location, transaction history, and device information to identify abnormalities and patterns that can hint at fraudulent behavior. The technology can notify PayPal’s fraud investigation team about a possibly fraudulent transaction so that they can look into it further or block the transaction. The amount of fraudulent transactions on the network has dramatically decreased thanks to this AI-powered solution, making using PayPal safer and more secure.

Customer service

AI-powered chatbots can provide personalized financial advice, answer customer queries, and automate routine tasks like opening new accounts or updating customer information.

The chatbot “KAI” from Mastercard, which helps clients with account queries, transaction histories and expenditure tracking, is an example of how AI is being used in customer support. KAI uses machine learning algorithms and natural language processing to offer consumers tailored help and financial insights across a variety of channels, including SMS, WhatsApp, and Messenger.

Algorithmic trading

AI can accurately assess past and present market trends, spot patterns, and predict future prices. AI algorithms can also perform transactions in real time, using pre-programmed rules and conditions, optimizing investing strategies and maximizing returns.

Financial institutions and investors benefit significantly from this technology, which enables them to make data-driven decisions and maintain an advantage in the fiercely competitive world of trading.

Related: What are artificial intelligence (AI) crypto coins, and how do they work?

Risk management

By analyzing complex financial data, artificial intelligence can identify potential risks and forecast future scenarios, providing valuable insights that enable banks and other financial institutions to make well-informed decisions. 

An example of risk management using AI is BlackRock’s Aladdin platform. To analyze enormous volumes of financial data, spot risks and opportunities, and give investment managers real-time insights, the Aladdin platform combines AI and machine learning algorithms.

By examining elements like market volatility, credit risk, and liquidity risk, the platform assists investment managers in monitoring and managing risks. Investment managers may enhance their investment strategies and make data-driven decisions thanks to Aladdin’s risk management capabilities, which lower the risk of losses and boost returns.

Portfolio management

AI can analyze vast amounts of financial data and provide insights into investment trends, risks and opportunities, helping investors make informed decisions. An example of portfolio management using AI is Wealthfront, a robo-advisor that uses AI algorithms to manage investment portfolios for clients. 

To create customized investment portfolios for clients based on their goals, risk tolerance, and financial position, Wealthfront combines classic portfolio theory and AI. As market conditions and the client’s goals change, the platform automatically rebalances the portfolio while continuously monitoring its performance. Many investors find Wealthfront an appealing alternative because of its AI-powered portfolio management, which enables customized and optimal investing plans.

Credit scoring

AI algorithms can analyze credit histories, financial statements, and other data to provide accurate credit scores, enabling lenders to make better lending decisions. For instance, ZestFinance’s Zest Automated Machine Learning (ZAML) platform uses AI to analyze credit risk factors and provide more accurate credit scores, improving lending decisions and reducing the risk of default.

Personalized financial advice

AI-powered robo-advisors can provide personalized financial advice and investment strategies based on a client’s financial situation, goals and risk tolerance. For instance, Bank of America’s AI chatbot, Erica, can provide personalized financial advice, answer customer queries and automate routine tasks.

Insurance underwriting 

AI can analyze a range of data points, including demographic information, health records and driving history, to provide accurate insurance underwriting. For instance, to improve accuracy and lower fraud in the insurance market, Lemonade, an AI-powered insurtech company, employs AI algorithms to evaluate claims and underwrite insurance policies.

Related: A brief history of artificial intelligence

Regulatory compliance

AI can help financial institutions comply with complex regulations by analyzing transactions, detecting fraud, and ensuring compliance with Know Your Customer and Anti-Money Laundering regulations. 

For instance, ComplyAdvantage helps businesses comply with legal obligations and avoid fines by using AI and machine learning algorithms to monitor financial transactions and identify potential money laundering activities.

ChatGPT and AI must pay for the news it consumes: News Corp Australia CEO

Michael Miller said generative AI is a move by digital companies to take the creative content of others “without remunerating them for their original work.”

The creators of artificial intelligence (AI) fuelled applications should pay for the news and content being used to improve their products, according to the CEO of News Corp Australia.

In an April 2 editorial in The Australian, Michael Miller called for “creators of original journalism and content” to avoid the past mistakes that “decimated their industries” by allowing tech companies to profit from using their stories and information without compensation.

Chatbots are software that ingests news, data and other information to produce responses to queries that mimic written or spoken human speech, the most notable of which is the ChatGPT-4 chatbot by AI firm OpenAI.

According to Miller, the rapid rise of generative AI represents another move by powerful digital companies to develop “a new pot of gold to maximize revenues and profit by taking the creative content of others without remunerating them for their original work.”

Using OpenAI as an example, Miller claimed the company “quickly established a business” worth $30 billion by “using the others’ original content and creativity without remuneration and attribution.”

The Australian federal government implemented the News Media Bargaining Code in 2021, which obliges tech platforms in Australia to pay news publishers for the news content made available or linked on their platforms.

Miller says similar laws are needed for AI, so that all content creators are appropriately compensated for their work.

“Creators deserve to be rewarded for their original work being used by AI engines which are raiding the style and tone of not only journalists but (to name a few) musicians, authors, poets, historians, painters, filmmakers and photographers.”

More than 2,600 tech leaders and researchers recently signed an open letter urging a temporary pause on further artificial intelligence (AI) development, fearing “profound risks to society and humanity.”

Meanwhile, Italy’s watchdog in charge of data protection announced a temporary block of ChatGPT and opened an investigation over suspected breaches of data privacy rules.

Miller believes content creators and AI companies can both benefit from an agreement, rather than outright blocks or bans on the tech.

He wrote that with “appropriate guardrails,” AI has the potential to become a valuable journalistic resource. It can assist in creating content, “gather facts faster,” help to publish on multiple platforms and could accelerate video production.

Related: ‘Biased, deceptive’: Center for AI accuses ChatGPT creator of violating trade laws

The crypto industry is also starting to see more projects using AI, though it is still in the early stages.

Miller believes AI engines face a risk to their future success if they can’t convince the public that their information is trustworthy and credible, adding that “to achieve this they will have to fairly compensate those who provide the substance for their success.”

Magazine: All rise for the robot judge: AI and blockchain could transform the courtroom

A brief history of artificial intelligence

AI has evolved from the Turing machine to modern deep learning and natural language processing applications.

Multiple factors have driven the development of artificial intelligence (AI) over the years. The ability to swiftly and effectively collect and analyze enormous amounts of data has been made possible by computing technology advancements, which have been a significant contributing factor. 

Another factor is the demand for automated systems that can complete activities that are too risky, challenging or time-consuming for humans. Also, there are now more opportunities for AI to solve real-world issues, thanks to the development of the internet and the accessibility of enormous amounts of digital data.

Moreover, societal and cultural issues have influenced AI. For instance, discussions concerning the ethics and the ramifications of AI have arisen in response to worries about job losses and automation.

Concerns have also been raised about the possibility of AI being employed for evil intent, such as malicious cyberattacks or disinformation campaigns. As a result, many researchers and decision-makers are attempting to ensure that AI is created and applied ethically and responsibly.

AI has come a long way since its inception in the mid-20th century. Here’s a brief history of artificial intelligence.

Mid-20th century

The origins of artificial intelligence may be dated to the middle of the 20th century, when computer scientists started to create algorithms and software that could carry out tasks that ordinarily need human intelligence, like problem-solving, pattern recognition and judgment.

One of the earliest pioneers of AI was Alan Turing, who proposed the concept of a machine that could simulate any human intelligence task, which is now known as the Turing Test. 

Related: Top 10 most famous computer programmers of all time

1956 Dartmouth conference

The 1956 Dartmouth conference gathered academics from various professions to examine the prospect of constructing robots that can “think.” The conference officially introduced the field of artificial intelligence. During this time, rule-based systems and symbolic thinking were the main topics of AI study.

1960s and 1970s

In the 1960s and 1970s, the focus of AI research shifted to developing expert systems designed to mimic the decisions made by human specialists in specific fields. These methods were frequently employed in industries such as engineering, finance and medicine.

1980s

However, when the drawbacks of rule-based systems became evident in the 1980s, AI research began to focus on machine learning, which is a branch of the discipline that employs statistical methods to let computers learn from data. As a result, neural networks were created and modeled after the human brain’s structure and operation.

1990s and 2000s

AI research made substantial strides in the 1990s in robotics, computer vision and natural language processing. In the early 2000s, advances in speech recognition, image recognition and natural language processing were made possible by the advent of deep learning — a branch of machine learning that uses deep neural networks.

Modern-day AI

Virtual assistants, self-driving cars, medical diagnostics and financial analysis are just a few of the modern-day uses for AI. Artificial intelligence is developing quickly, with researchers looking at novel ideas like reinforcement learning, quantum computing and neuromorphic computing.

Another important trend in modern-day AI is the shift toward more human-like interactions, with voice assistants like Siri and Alexa leading the way. Natural language processing has also made significant progress, enabling machines to understand and respond to human speech with increasing accuracy. ChatGPT — a large language model trained by OpenAI, based on the GPT-3.5 architecture — is an example of the “talk of the town” AI that can understand natural language and generate human-like responses to a wide range of queries and prompts.

Related: Biased, deceptive’: Center for AI accuses ChatGPT creator of violating trade laws

The future of AI

Looking to the future, AI is likely to play an increasingly important role in solving some of the biggest challenges facing society, such as climate change, healthcare and cybersecurity. However, there are concerns about AI’s ethical and social implications, particularly as the technology becomes more advanced and autonomous.

Moreover, as AI continues to evolve, it will likely profoundly impact virtually every aspect of our lives, from how we work and communicate, to how we learn and make decisions.

The government should fear AI, not crypto: Galaxy Digital CEO

Galaxy Digital CEO Mike Novogratz believes regulators have got it “completely upside-down” on crypto vs AI regulation.

Mike Novogratz, the CEO of digital asset investment firm Galaxy Digital told investors he is shocked over the amount of regulatory attention for crypto rather than artificial intelligence (AI), a technology he believes will trigger a “deep fake” identity crisis.

The chief executive explained at the firm’s fourth-quarter conference call on March 28 that the U.S. government has it “completely upside-down” in choosing to focus so much on crypto regulation and yet turn a blind eye to AI:

“When I think about AI, it shocks me that we’re talking so much about crypto regulation and nothing about AI regulation. I mean, I think the government’s got it completely upside-down.”

This concern appeared to stem from Novogratz’s fear that AI will trigger a “deep fake” identity crisis.

“In lots of ways, one of the best use cases for crypto is going to be identity around AI, because pretty soon you’re going to get a fake Mike Novogratz, hopefully with hair […] how do you prove identity in a world like that?” he said.

However, he believes blockchain-based applications will play a “huge role” in combating some of the issues presented by AI:

“Crypto and blockchain is going to have a huge role in that. It is dumb to think that we should cache this industry because of Sam Bankman-Fried in his Bermuda shorts, period.”

That said, the U.S. Commodity Futures Trading Commission recently engaged in talks about AI and its impacts with the Technology Advisory committee last week.

Seller exhaustion, China easing

As for the current state of the market, Novogratz said “seller exhaustion” and the reopening of China has helped the crypto industry recover remarkably thus far in 2023.

“All the selling that needed to get done got done, right? There was so much bad news, if you had to sell, panic selling and just the nervousness of “Oh my God! This thing could go to zero,” and people were in sheer panic, you had seller’s exhaustion,” he said.

Following a tough zero-COVID approach by the Chinese government, Novogratz said he has since noticed more crypto activity coming out of China.

“China took the regulatory boot off the necks of their tech companies, and that includes crypto, [so] you’re seeing more activity from Asia.”

Related: Could Hong Kong really become China’s proxy in crypto?

From a more technical lens, Novogratz was confident that the crypto market will continue in an upwards trajectory throughout the remainder of 2023:

“The market feels strong, and when I look at it technically on charts, we’ve had big weekly closes. I’m surprised to hear myself say this, given where my mindset was in late December, but it would not surprise if we were substantially higher three months, six months, nine months from now.”

The strong rebound in the crypto market reflected well on Galaxy’s balance sheet too with the firm today revealing in its quarterly results that it finally swung back into profit after a tough loss of $1 billion in 2022.

Magazine: Crypto winter can take a toll on hodlers’ mental health

Cointelegraph Markets Pro delivers trading alerts good for 65% gains in a choppy market

Using proprietary indicators, Cointelegraph Markets Pro crunches real-time data to inform traders before the market moves.

Navigating the ever-volatile terrain of the crypto market remains one of the most difficult jobs for traders — but much less so for members of the Cointelegraph Markets Pro community. 

With an institutional-grade crypto intelligence platform at their service, Cointelegraph Markets Pro subscribers have been able to spot significant price movements for crypto assets before the market moves on a regular basis.

This prescient ability is actually the working of Cointelegraph Market Pro’s algorithmic tools, which are designed to spot coins showing historically similar signs to coins that have moved significantly in the past.

Last week, Cointelegraph Markets Pro alerts by the NewsQuakes™, Twitter Volume and On-Chain Activity indicators led Markets Pro members to opportunities to make 65% gains with just three trades!

OAX (OAX) — 39% increase

Most Active On-Chain activity table from Friday, March 24. Source: Cointelegraph Markets Pro

On-Chain Activity on OAX skyrocketed 405% on March 24, hinting at a massive growth spurt in the potential users of the platform. While this increase does not mean a price increase is inevitable (as the other examples show), it demonstrates how On-Chain Activity growth could be a precursor to massive price spikes.

In this example, OAX’s price increases 39% soon after the increase in on-chain activity.

OAX is the native coin of OAX Foundation, which strives to advance decentralized finance through tools, technology, applications and community support.

Arbitrum (ARB) — 14%

Arbitrum NewsQuakes™ listing from Friday, March 24. Source: Cointelegraph Markets Pro

Arbitrum jumped 14% on news of its listing on the Crypto.com platform.

NewsQuakes™ have been the Cointelegraph Markets Pro community’s most lucrative and trustworthy indicator. Historically, had one bought and held every NewsQuakes™ listing alert for one hour, one could have yielded as much as $120,000 from a starting stake of just $1,000 — that’s 120x profit!

ARB is the native coin of Arbitrum, a layer-2 scaling solution built on the Ethereum network.

OmiseGo (OMG) — 12%

Price chart of OmiseGo’s surge in Tweet volume March 24. Source: Cointelegraph Markets Pro

OmiseGo jumped 12% in market capitalization soon after its Tweet Volume increased by 158% compared with its 30-day average. The Tweet Volume indicator measures public sentiment about a coin, which can precede a significant price change as seen in the example above.

OMG is the native coin of OmiseGo, an interoperable decentralized exchange and payment platform.

The Cointelegraph Markets Pro advantage

While most traders are left to fend for themselves in the highly competitive crypto trading markets, Cointelegraph Markets Pro subscribers find themselves in a community of like-minded individuals, fueled by algorithmic tools and institutional-grade data.

As such, members have had the opportunity to catch several winning trades each week and to actively learn from the trading experience. The Cointelegraph Markets Pro platform provides members with alerts like these on a nearly daily basis, based on real-time data, transforming any market environment into one capable of yielding gains.

Tired of coming in second to institutions and missing out on trading opportunities? If so, there’s only one place to go.

See how Cointelegraph Markets Pro delivers market-moving data before this information becomes public knowledge.

Cointelegraph is a publisher of financial information, not an investment adviser. We do not provide personalized or individualized investment advice. Cryptocurrencies are volatile investments and carry significant risk including the risk of permanent and total loss. Past performance is not indicative of future results. Figures and charts are correct at the time of writing or as otherwise specified. Live-tested strategies are not recommendations. Consult your financial adviser before making financial decisions.

All ROIs quoted are accurate as of March 28, 2023.

7 artificial intelligence examples in everyday life

AI impacts everyday life: personal assistants, social media, healthcare, autonomous vehicles, smart homes and more!

Artificial intelligence (AI) is becoming increasingly important in our daily lives. AI can automate routine and time-consuming tasks, allowing us to focus on more important activities. In addition, AI algorithms can analyze vast amounts of data to personalize products, services and experiences. Moreover, AI is driving innovation in various industries, such as finance, retail and education.

Here are seven artificial intelligence examples in everyday life.

Personal Assistants

AI-powered personal assistants, such as Siri, Google Assistant and Amazon Alexa, are integrated into smartphones, smart speakers and other devices and can perform a wide range of tasks, from setting reminders and sending messages to playing music and controlling smart home devices.

Social media

Social media sites utilize AI to examine user preferences and behavior, suggest pertinent material, and customize the user experience. Moreover, bogus news, hate speech and other harmful content are found and eliminated thanks to AI systems.

For instance, Meta uses AI to detect and remove fake news and other harmful content. Instagram uses AI to recommend posts and stories based on user behavior. TikTok uses AI to personalize the user experience and recommend videos.

Customer service

Businesses are increasingly using virtual assistants and chatbots powered by AI to offer 24/7 customer service. Natural language processing is used by these chatbots to comprehend consumer questions and deliver relevant responses.

For instance, many companies, such as H&M, use AI-powered chatbots to provide customer support. These chatbots can handle a wide range of queries, such as tracking orders and processing returns.

Related: 10 emerging technologies in computer science that will shape the future

Healthcare

Applications of artificial intelligence in healthcare include patient monitoring, medication research and medical imaging. Medical picture analysis, anomaly detection and diagnosis support are all capabilities of AI algorithms.

For instance, Merative (formally IBM Watson Health) uses AI to analyze medical images and assist doctors in making diagnoses. The app Ada uses AI to help users identify symptoms and connect with healthcare professionals.

Related: 9 promising blockchain use cases in healthcare industry

E-commerce

Customers are given product recommendations by e-commerce sites, such as Amazon, using AI algorithms based on their search queries, browsing histories and other information. Sales are boosted as a result, and customer satisfaction is enhanced.

Autonomous vehicles

AI is used in self-driving cars, trucks and buses to perceive their environment, map out routes and make judgments while driving. It is anticipated that this technology will lessen collisions, gridlock in the streets and pollutants.

For instance, Tesla uses AI to power its self-driving cars, which can navigate roads, highways and parking lots without human intervention.

Smart home devices

Smart home devices such as thermostats, lighting systems and security systems use AI to learn user preferences and adjust settings accordingly. These devices can also be controlled remotely using smartphones or voice commands.

For instance, Philips Hue uses AI to adjust lighting based on user preferences and ambient light levels.

NFT Creator: Creating ‘organic’ generative art from robotic algorithms: Emily Xie

Cryptocurrency miners are leading the next stage of AI

A globally distributed AI network that relies on mining rigs will be difficult for governments to control, according to Dr. Ben Goertzel.

As artificial intelligence (AI) rapidly works its complex magic on one sector of the economy after another, there is an increasingly pressing need for compute resources to power all this machine intelligence. 

Training a model like ChatGPT costs more than $5 million, and running the early ChatGPT demo, even before usage increased to its current level, costs OpenAI around $100,000 per day. And AI is more than just text generation; applying AI to practical problems across multiple industries requires similar large neural models trained on a diversity of data types — medical, financial, customer information, geospatial and so forth. Moving beyond the limitations of current neural net AI toward systems with higher levels of artificial general intelligence will almost surely be even more compute intensive.

It’s only natural that a small but increasing number of crypto miners are now looking at how to leverage their own compute infrastructures to help push forward the AI revolution.

Related: From Bernie Madoff to Bankman-Fried, Bitcoin maximalists have been validated

Bitcoin (BTC) mining remains a lucrative business. Mining other cryptocurrencies can still make money as well, but it is a rapidly shifting landscape. Ether (ETH) miners, for instance, took a major hit late last year when the Ethereum network shifted from proof-of-work to proof-of-stake.

The economic and technical situation in the crypto space over the last two years has driven an increasing number of crypto mining organizations to explore the potential of leveraging their facilities for other purposes, such as high-performance computing and, in particular, AI.

The specific computing hardware needed for high-performance computing (HPC) or AI processing is often different from what’s optimal for crypto mining. But buying servers is generally not the most difficult part of setting up a mining farm. Getting the electrical power and cooling and security and other physical infrastructure in place is a major cost and effort, and all this remains roughly the same whether one is hosting RAM-light GPUs appropriate for ETH mining or RAM-heavy GPUS appropriate for AI model learning.

Mining firm Hut 8 has led the way, leveraging its formerly mining-dedicated compute facilities for machine learning and other HPC applications. Hive Blockchain has been doing the same thing for some time, filling its servers with processor cards that “can be used for cloud computing and AI applications, and rendering for engineering applications, in addition to scientific modelling of fluid dynamics.”

Mining firm Hut 8’s stock price, Feb. 2022-Feb 2023. Source: TradingView

Perhaps most interesting is the potential for miners to shift their compute resources to AI in a way that remains fully within the blockchain space — by using them to run AI processes that are hosted in decentralized blockchain-based networks. This opportunity is provided by a number of AI projects associated with their own altcoins, such as Fetch.ai (FET), Ocean (OCEAN) Matrix AI Network (MAN), Cortex (CTXC) and my own project, SingularityNET (AGIX), and its various ecosystem projects, such as NuNet (NTX) and the new ledgerless blockchain HyperCycle. AI-related altcoins have done well in the first part of 2023, as the market has come to understand the potential for decentralized AI software.

Related: Should Bored Ape buyers be legally entitled to refunds?

It’s been clear since before Bitcoin’s white paper that the fusion of distributed computing, strong encryption and decentralized control has broad applications beyond the financial. This is why we have blockchain projects in areas spanning nearly all vertical markets — medicine, supply chain, gaming, robotics and so on. As each of these business domains becomes dominated by AI, decentralizing the software and hardware underlying AI will be a critical aspect of decentralizing the global economy. Repurposing of a portion of crypto mining hardware to running AI processing, some of which is wrapped in AI-oriented crypto networks, will increasingly form part of the story.

If a non-trivial portion of global AI processing ends up being done on crypto mining facilities, this could have implications beyond finance. Crypto mining rigs are based in diverse legal jurisdictions and owned by a variety of different parties. A globally distributed AI network spread across crypto mining rigs would be dramatically more difficult for governments or other parties to centrally control than an AI network centered in Big Tech-owned server farms (the current default for AI). Whether this is good or bad AI ethics-wise depends on your estimate of the character of Big Tech and big government.

Ben Goertzel is the CEO and founder of SingularityNET and chairman of the Artificial General Intelligence Society. He has worked as a research scientist at a number of organizations, most notably as the chief scientist at Hanson Robotics, where he co-developed Sophia. He served previously as a director of research at the Machine Intelligence Research Institute, as the chief scientist and chairman of AI software company Novamente and as chairman of the OpenCog Foundation. He graduated from Temple University with a Ph.D. in mathematics.

This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.