Welcome to the future where on-chain robots serve coffee and crypto rewards

April 18, 2024 Coin Telegraph

AI to reinvent DAOs while tokenized models will become valuable: VC firm

September 6, 2023 Coin Telegraph

Framework Ventures co-founder Vance Spencer sees AI as being the missing piece for DAOs and shared his outlook for the tokenization of AI models.

Artificial intelligence could be the missing piece for decentralized autonomous organizations (DAOs), while trained AI models could become valuable assets on-chain, according to the co-founder of Framework Ventures.

Speaking to Cointelegraph on Sept. 5 at Korea Blockchain Week, Vance Spencer, the co-founder of the crypto-focused venture firm, shared four predictions about how AI and blockchain technology could collide.

One of the biggest impacts is for AI to finally put the “autonomous” into decentralized autonomous organizations, according to Spencer.

DAOs were founded on the concept of a decentralized collective sharing a common goal, with no overarching central authority. However, many of the biggest are still far from full decentralization or autonomy.

“It's not actually autonomous, there’s a bunch of people in the middle. It seems like AI is really the only way to actually make the DAO concept work.”

In May, DAI stablecoin proprietor MakerDAO published a five-phase roadmap to upgrade its ecosystem including a strong focus on using AI to create a “governance equilibrium.”

5 phases of Endgame.

Endgame has been defined as an update that employs AI tools and open processes to enhance efficiency, resilience, and participation.

A new publication on the Maker Forum outlines its roadmap, which includes the introduction of 5 major launch phases. pic.twitter.com/8GOkIfFADi
— Maker (@MakerDAO) May 12, 2023

According to MakerDAO co-founder Rune Christensen, phase three of the roadmap will launch AI tools aimed at improving and possibly automating certain governance aspects.

Christensen added these AI tools will initially help “level the playing field between deeply embedded insiders and more peripheral community members,” but eventually allow the DAO to improve its processes and decisions over time “without requiring leadership or centralized authority.

“What happens when Maker, who has a shitload of treasury, is governed by an AI?” Spencer queried.

“That AI can do really interesting things and there needs to be only limited human intervention with that,” he added.

Trained AI models could become prized

Spencer also sees a future in which trained AI models are tokenized on the blockchain.

He said an early example can be seen in the Ethereum native decentralized app and game — AI Arena — where players train an AI model to fight for them in a platform fighting game akin to Nintendo’s Super Smash Bros.

Framework invested in AI Arena's $5 million Paradigm-led seed round in 2021.

Spencer explained that in AI Arena, the players don’t control the fighters themselves but instead, the characters are controlled by AI models that are owned and trained by the player.

Here are some of the fight highlights from our Exclusive Access!

JamesRic vs Hung0 pic.twitter.com/pXJsxAJbXo
— AI ARENA | ʌı (@aiarena_) August 26, 2023

He noted that while it shifts the paradigm of what a game is, the on-chain ownership of AI models is “really where this comes to life in the crypto context.”

“Probably some of the most valuable assets on-chain will be tokenized AI models, that’s my theory at least,” Spencer said.

Other use cases

Meanwhile, decentralized computing marketplaces — such as Akash Network and Render Network — could also see crypto play a part in the growth of AI.

The blockchain-based protocols work as a marketplace that allows buyers to purchase idle computing processing power from providers, which is particularly important given the current shortage of GPU chips, explained Spencer.

“Actually having a network that sources and provides and bootstraps the market? Those things should work,” he said. “There are some pretty successful companies that do it that are protocols.”

Spencer also argued that blockchain technology will be important for auditing and verifying AI-provided information.

in the not too distant future, something that hasn't been validated via a blockchain will be irrelevant

"show me the data" will become "show me the proof that this data is accurate"
— Brandon Potts (@b_potts23) August 23, 2023

“Say that you want to prove that ChatGPT, that specific model, is giving you an answer rather than Bard, rather than Falcon, which is UAE’s model," Spencer explained. "You can actually prove that on-chain.”

AI Eye: Apple developing pocket AI, deep fake music deal, hypnotizing GPT-4

Can artificial intelligence create more jobs?

May 11, 2023 Coin Telegraph

Despite negatively impacting the labor market, there are many reasons to think AI will eventually contribute to creating new jobs and economic growth.

Artificial intelligence (AI) can increase productivity, boost economic growth, alter existing occupations and generate new ones. Without a doubt, AI will result in some job displacement in the short term, but there are numerous reasons to think that AI will also contribute to creating new jobs and economic growth in the long run.

Artificial intelligence can also help workers become more effective and productive by giving them access to real-time data and insights, enabling them to enhance their performance and make better decisions. In addition, AI can generate new employment opportunities in the creative and artistic industries by nurturing new modes of expression and creativity. For instance, artificial intelligence can produce original works of literature, music and art, allowing creators to work with AI systems to explore new kinds of creativity.

Will Ai erase jobs? Yes
Will Ai create jobs? Yes

1-1 goaless draw
— Kalu Aja (@FinPlanKaluAja1) April 6, 2023

Similarly, by assisting organizations in identifying and averting cyberattacks, AI can create new jobs in the cybersecurity sector. For example, AI can spot behavioral patterns that could be signs of a cyberattack, enabling organizations to take proactive steps to stop or lessen the attack. Cybersecurity experts who are proficient in employing AI and machine learning techniques to protect against cyber threats may find new employment prospects as a result.

Enabling new products, services, industries and jobs with AI

By enabling the development of new products and services previously unattainable or unfeasible, AI can also create new jobs. For instance, AI can create personalized medicinal treatments, precision farming and sophisticated industrial methods. These new products and services can lead to new responsibilities in research, development and marketing, along with new skills and experience requirements.

AI can potentially generate new jobs by enabling new sectors and business models. For instance, the emergence of AI-powered digital assistants and smart home appliances has opened up new career prospects for hardware engineers, data analysts and software developers. Similar to how autonomous vehicle and drone research has opened up new career prospects for engineers, technicians and logistics specialists.

AI automation and the transformation of existing jobs

Automating tedious and normal chores allows people to concentrate on more difficult and creative tasks, which is one example of how artificial intelligence might create jobs. For instance, AI-powered chatbots can respond to routine customer service questions, freeing up human customer service agents to handle more complicated situations that call for interpersonal connection and problem-solving abilities.

In addition, businesses can engage with clients and partners in new areas thanks to language translation services enabled by artificial intelligence. As a result, there are more opportunities for linguists, software developers and localization experts to create and enhance these systems.

Similarly, AI-powered drones are now being utilized for inspecting infrastructure, and surveying and monitoring crops. As the demand for software developers, data analysts and drone operators increases, new job possibilities will arise in these fields.

5. Agriculture:

AI-powered drones, sensors, and robotics will optimize crop management, reduce waste, and improve sustainability.

Example: Blue River Technology's See & Spray system uses AI to identify and target weeds, reducing the need for chemical herbicides.
— Tomas Ondrejka (@tomas_ondrejka) May 2, 2023

Some concerns and the call to action

However, there are fears that AI may result in significant job displacement in some businesses and areas. Automation fueled by AI, for instance, may result in considerable job losses in the industrial, retail and transportation industries, and some administrative and white-collar positions. Automating low-skilled occupations and creating new opportunities for highly trained individuals at the expense of workers with less education and training could also worsen already-existing disparities.

Policymakers, educators and business leaders must collaborate to address these issues, ensuring people are ready for the evolving nature of work in the AI era. Focusing on education and training will be necessary, especially in the science, technology, engineering and mathematics professions, and other areas where there will likely be a significant need for competent individuals. To facilitate the development and commercialization of new products and services, it will also be necessary to make investments in infrastructure and innovation.

Additionally, authorities need to make sure that society as a whole benefits from AI. New laws and regulations may be required to address the issues of income inequality and job displacement, and ensure employees are protected with access to social safety nets.

5 ways AI is helping to improve customer service in e-commerce

May 9, 2023 Coin Telegraph

AI is transforming e-commerce customer service through chatbots, personalized recommendations, voice assistants, fraud detection and image recognition.

Artificial intelligence (AI) has revolutionized the e-commerce industry in recent years. One of the most significant ways in which AI is impacting e-commerce is by transforming customer service. AI-powered customer service technologies are becoming more common, and they are helping to improve the customer experience in numerous ways.

This article will discuss how AI improves customer service in e-commerce, with examples of companies using these technologies to their advantage.

Chatbots

AI-driven chatbots are assisting online retailers in offering prompt and effective customer support. Without human assistance, chatbots may provide 24/7 customer service. They can assist clients with information about the products, order tracking, returns and refunds, and other services. For instance, H&M uses a chatbot to assist shoppers in finding products and placing orders on its website.

However, chatbots may not always understand complex customer queries, leading to frustration and dissatisfaction.

Product recommendations

AI is capable of analyzing client data and making tailored product recommendations. E-commerce companies can provide customers with products more likely to be of interest by learning about their preferences and past purchases. For instance, using AI, Amazon suggests products based on a customer’s browsing and purchase history.

The 4 key components of the most successful Amazon product pages are:
1. A personalized shopping experience
How? Get your Web Dev to use Amazon Personalize to deliver personalized recommendations to your customers
…
— Talia Wolf (@TaliaGw) May 3, 2023

Personalized recommendations, however, may be perceived negatively by some customers as intrusive or creepy, which is one of the disadvantages of AI.

Fraud detection

AI can help e-commerce businesses detect and prevent fraudulent activity before it happens. By analyzing patterns of fraudulent behavior, AI can identify potential fraudsters and flag suspicious transactions. For example, PayPal uses AI to detect fraudulent transactions and prevent unauthorized account access.

However, these systems may not always accurately distinguish between legitimate and fraudulent, leading to false positives that can inconvenience and frustrate customers.

Voice assistants

With the rise of voice assistants like Amazon’s Alexa and Google Home, e-commerce businesses can use AI to provide a more seamless customer experience. Customers can use their voices to order products, check order status and get answers to questions. For example, Walmart has integrated its shopping service with Google Home, allowing customers to add items to their cart and place orders using voice commands.

Still, voice assistants might not always understand consumer requests correctly, which could cause annoyance and mistakes during the ordering process.

Image recognition

E-commerce companies can enhance their product search and discovery with AI-powered image recognition. AI can make it easier for buyers to find what they want by examining product photos and recognizing characteristics like color, shape and texture. For instance, Wayfair uses image recognition technology to assist clients in locating furniture and home décor items that complement their tastes and aesthetics.

However, one disadvantage is that image recognition may not always accurately identify products, especially if they are similar in appearance or if the lighting and background in the image are poor. This can lead to frustration and incorrect purchases for the customer.

The future of AI in e-commerce

As e-commerce continues to evolve, AI technology is playing an increasingly important role in the industry. AI is revolutionizing how online retailers conduct business, from personalizing the shopping experience to improving supply chain management. There are several areas where AI is anticipated to significantly affect e-commerce in the future.

Visual search is one such area that enables users to find things by merely submitting a photo. Retailers can use AI to analyze photos and determine product characteristics like color, style and material. Using this technology, a customer’s browsing history can be used to generate product recommendations.

Additionally, it is anticipated that e-commerce will place more emphasis on AI-powered fraud detection. AI can assist merchants in identifying and preventing fraudulent transactions by analyzing trends in client behavior. The risk of stockouts and overstocking can be decreased by using this technology to improve the accuracy of supply chain forecasts and inventory management.

Finally, thanks to its capability to analyze consumer behavior and make real-time price adjustments, AI can assist businesses in optimizing their pricing plans. Additionally, this technology can be used to design customized sales incentives and promotions for specific clients, fostering client loyalty and boosting revenue.

How can AI be used to improve credit scoring?

May 5, 2023 Coin Telegraph

AI can analyze data for accurate risk assessment, reduce bias, automate tasks and personalize the lending experience for improved credit scoring.

Artificial Intelligence (AI) can be used to improve credit scoring in a number of ways. Credit scoring is the process of assessing a borrower’s creditworthiness based on their credit history, financial data and other relevant factors. Here are some ways AI can improve credit scoring:

Better data analysis

AI can analyze large volumes of data from a variety of sources to identify patterns and trends that might not be apparent to human analysts. This can help lenders make more accurate predictions about a borrower’s creditworthiness. The below steps illustrate how AI can be used for data analysis:

Collect data from various sources, such as social media, credit bureaus and financial statements.
Pre-process and clean the data to ensure it’s ready for analysis.
Apply machine learning algorithms to the data to identify patterns and trends.
Use the insights gained from the analysis to inform lending decisions.

Improved risk assessment

AI can be used to build predictive models that assess the likelihood of a borrower defaulting on a loan. These models can take into account a wide range of factors, such as income, debt-to-income ratio and payment history, to better predict the risk associated with lending to a particular borrower.

The general steps followed by lenders to assess borrower’s suitability for credit are listed below:

Collect data about the borrower, such as credit history, income, employment status and other relevant factors.
Pre-process and clean the data to ensure it’s ready for analysis.
Train machine learning models on the data to predict the likelihood of a borrower defaulting on a loan.
Use the models to assess the risk associated with lending to a particular borrower.

Reduced bias

AI can help reduce bias in credit scoring by using objective criteria to assess creditworthiness. This can help reduce the impact of factors such as race, gender and ethnicity on lending decisions.

One of the challenges in credit scoring is ensuring that the process is fair and free from bias. Historically, lending decisions have been influenced by factors such as race, gender and ethnicity, which can result in discriminatory practices. However, with the use of AI, it’s possible to reduce the impact of these factors on lending decisions.

I also mentioned the aspect of fairness under the ethical considerations of using AI, while we are looking at using AI for credit scoring @SociaLendAfrica, we will maintain the human review process. Even AI can be biased by the information we feed it #WLS @katslaw1
— Marvin Peter Akankwasa (@Marvinpetersbk) August 1, 2019

To achieve this, lenders need to identify potential sources of bias in the credit scoring process, such as race, gender and ethnicity. They can then train machine learning models to exclude or de-emphasize these factors in the lending decision process. By doing so, lenders can make more objective and fair lending decisions that are based on the borrower’s creditworthiness rather than personal characteristics.

However, it is critical to note that AI is not immune to bias, and it’s crucial to monitor the models for any signs of bias and adjust them as needed to ensure fairness and transparency. This necessitates constant model monitoring and assessment, as well as routine evaluations of the training data. By doing so, lenders are able to guarantee that their credit score system is impartial and equal for all borrowers, regardless of their racial, gender or cultural background.

Faster processing

AI can significantly improve the speed and efficiency of the credit scoring process. Traditionally, credit scoring has been a manual and time-consuming process, involving a lot of paperwork and human intervention. However, with the use of AI, lenders can automate many of the tasks involved in credit scoring, reducing processing times and increasing efficiency.

One of the ways AI can speed up the credit scoring process is by automating data entry and analysis. By using machine learning algorithms to process and analyze large volumes of data, lenders can quickly assess a borrower’s creditworthiness and make lending decisions in real-time. This can be especially useful for online lending platforms that require fast and accurate credit assessments.

Another way AI can improve the speed of credit scoring is by automating the loan application process. By using chatbots and other AI-powered tools, lenders can provide borrowers with instant feedback on their loan applications, reducing the time and effort required to apply for a loan.

Improved customer experience

AI-powered credit scoring can provide borrowers with a more personalized lending experience. For example, lenders can use AI to offer borrowers loan products that are tailored to their specific needs and financial situations. The lenders typically follow the steps below to enhance the borrower’s experience:

Collect data about the borrower, such as their financial goals and risk tolerance.
Use machine learning algorithms to identify loan products that match the borrower’s needs and preferences.
Offer personalized loan products to the borrower based on the analysis.

7,800 jobs at IBM could be replaced by AI within years, suggests CEO

May 2, 2023 Coin Telegraph

accounting

Arvind Krishna, the chief executive of IBM, said roughly 30% of their non-customer-facing positions could be covered by artificial intelligence over a five-year period.

International Business Machines Corp. (IBM) is expecting to put a “pause” on hiring for "back-office" roles that could be potentially automated by artificial intelligence (AI) instead.

IBM CEO Arvind Krishna explained in a May 1 interview with Bloomberg that many “back-office” positions such as those in the human resources and accounting departments will likely be the first to be automated by AI.

The IBM boss added he could "easily" see 30% of these positions replaced by AI over a five-year period.

Dropbox: Laying off 500 people and replacing them with AI

IBM: Pausing hiring for ~7,800 roles that could be done by AI

AI is already replacing jobs
— Genevieve Roch-Decter, CFA (@GRDecter) May 1, 2023

IBM employs 282,000 employees globally according to LinkedIn and according to Bloomberg has around 26,000 non-customer-facing staff — meaning around 7,800 jobs could be handed over to AI.

“I could easily see 30% of that getting replaced by AI and automation over a five-year period.”

According to some reports, AI-based automation has already helped IBM save well over $1 billion in business expenses and maintenance costs.

Among the tasks that may be automated include providing employment verification letters or moving employees between departments.

However, Krishna thinks human resource roles that evaluate workforce composition, measure productivity and other tasks that benefit from human judgement likely won’t be replaced over the next decade.

For AI to work, it must be built responsibly. IBM CEO Arvind Krishna shares how organizations can prepare for the challenges and opportunities ahead in @FortuneMagazine: https://t.co/WYBu6VUCuc pic.twitter.com/gZtLwTNklb
— IBM (@IBM) April 24, 2023

Many industry pundits remain at crossroads on whether AI actually has the potential to leave humans without work on a mass scale.

A recent study found that 62% of Americans think implementing artificial intelligence in the workplace will have a “major impact” on workers within the next 20 years, leaving many employees “wary” and “worried” about what their future holds.

The more tech-savvy employees however feel slightly more secure about their future.

Blockchain developer Salman Arshad recently explained to Cointelegraph that instead of AI coming in to wipe out the developer market, it’ll only serve as a tool to increase efficiency.

“You know what your company wants to do. You can tell ChatGPT, and it can perfectly transform your commands into a smart contract, auditing process, document or white paper.”

“ChatGPT and AI tools are a blessing; they are not our enemies and are not here to end the career of a developer,” he added.

Another blockchain developer, Syed Ghazanfer, explained to Cointelegraph that the combination of human input and ChatGPT offers much more versatility than a complete transition to AI automation.

On the other hand, Dominik Schiener, the founder of the IOTA Foundation, believes that AI will take away employment opportunities from humans but at the same time, AI and robotic process will create new jobs:

“We’ll see more and more humans being forced to pivot to new roles that may look nothing like anything they’ve ever done.”

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

A brief history of digital banking

April 20, 2023 Coin Telegraph

automation

Explore a brief history of digital banking, tracing its evolution from early automation to the integration of new technologies, such as IoT and blockchain.

Digital banking, also known as online banking or e-banking, refers to the delivery of financial services through digital channels such as the internet, mobile devices and automated teller machines (ATMs). Digital banking has become increasingly popular in recent years, but its origins can be traced back several decades.

Here’s a brief history of digital banking.

Early automation (1960s to 1980s)

The first forms of digital banking can be traced back to the 1960s, when banks began using mainframe computers to automate various banking functions such as check processing and customer account management. In the 1980s, banks started offering dial-up services that allowed customers to access their accounts through their home computers.

In the 1960s, Bank of America introduced the first ATM, which allowed customers to withdraw cash from their accounts without needing a bank teller. Also, In the 1980s, Citibank introduced the first online banking system, which allowed customers to access account information and perform basic transactions through a dial-up connection.

Introduction of online banking (1990s to 2000s)

Online banking portals were developed due to increased internet use in the 1990s and 2000s. Banks started creating online portals to enable consumers to see account balances, transfer money and pay bills from their home computers. Online banking quickly became a preferred option for many people due to its convenience.

For instance, in 1994, Stanford Federal Credit Union became the first financial institution to offer online banking to its members, and in 1996, Wells Fargo became the first bank to provide online banking to its customers.

Mobile banking (2000s to present)

The proliferation of smartphones in the late 2000s and early 2010s led to the emergence of mobile banking. Banks began offering mobile apps that allowed customers to access their accounts from their smartphones, enabling them to check account balances, transfer funds, and pay bills on the go. Today, mobile banking has become an essential part of the digital banking landscape.

In 2007, USAA Federal Savings Bank became the first bank to offer mobile banking through its mobile app. Today, virtually every major bank offers a mobile banking app that allows customers to perform a wide range of transactions, from checking account balances to depositing checks.

Integration of new technologies (present to future)

Technological advancements like blockchain and artificial intelligence (AI) will have a major impact on the future of digital banking. Blockchain technology is being utilized to increase the security and effectiveness of cross-border payments, with companies like Ripple partnering with banks around the world.

In addition, banks are already exploring using AI-powered chatbots and virtual assistants to improve customer service. The banking sector is anticipated to change in the future due to the integration of these and other technologies, making it more effective and easy for customers.

Furthermore, in the future, technologies such as biometrics and the Internet of Things (IoT) are likely to play an increasingly important role in digital banking, enabling customers to authenticate transactions using fingerprints or facial recognition and providing real-time insights into their financial health through connected devices.

DeFi vs. Digital banking

To better understand the key differences between decentralized finance (DeFi) and digital banking, let’s closely examine their features and compare them.

DeFi has recently gained popularity as an alternative to traditional banking systems. DeFi is a blockchain-based financial system that allows anyone to participate and access financial services without intermediaries or centralized authorities. On the other hand, digital banking is a version of traditional banking that uses technology to offer services such as online banking, mobile banking and digital wallets.

As technology continues to evolve and disrupt traditional industries, the future of finance is becoming increasingly decentralized and democratized. However, while DeFi has a lot of potential, it still faces challenges in terms of scalability, security and mainstream adoption.

On the other hand, digital banking has already established itself as a mainstream industry and has been embraced by millions of users worldwide. However, digital banking is still largely centralized and controlled by traditional financial institutions, which limits its potential for democratization and innovation.

5 key features of machine learning

February 13, 2023 Coin Telegraph

artificial intelligence

Machine learning is based on the idea that a system can learn to perform a task without being explicitly programmed.

Machine learning has a wide range of applications in the finance, healthcare, marketing and transportation industries. It is used to analyze and process large amounts of data, make predictions, and automate decision-making processes, among other tasks.

In this article, learn the five key features of machine learning that make it a powerful tool for solving a broad set of problems, from image and speech recognition to recommendation systems and natural language processing.

What is machine learning?

Machine learning is a subfield of artificial intelligence (AI) that involves the development of algorithms and statistical models, which allow computers to learn from data without being explicitly programmed. Building systems with the ability to continuously improve their performance on a given task based on the experience obtained from the data they are exposed to is the aim of machine learning. This is accomplished by giving algorithms extensive training on huge data sets, which enables the algorithms to find patterns and connections in the data.

Supervised learning: This involves training a model on a labeled data set, where the correct output is provided for each input. The algorithm uses this information to learn the relationship between inputs and outputs and can then make predictions on new, unseen data.
Unsupervised learning: This involves training a model on an unlabeled data set where the correct output is not provided. The algorithm must find the structure in the data on its own and is typically used for clustering, dimensionality reduction and anomaly detection.
Reinforcement learning: This involves training an agent to make decisions in an environment where it receives feedback through rewards or punishments. The algorithm uses this feedback to learn the best strategy for maximizing rewards over time.

5 key features of machine learning

Machine learning has become one of the most important technological advancements in recent years and has significantly impacted a broad range of industries and applications. Its main features are:

Predictive modeling: Data is used by machine learning algorithms to create models that forecast future events. These models can be used to determine the risk of a loan default or the likelihood that a consumer would make a purchase, among other things.
Automation: Machine learning algorithms automate the process of finding patterns in data, requiring less human involvement and enabling more precise and effective analysis.
Scalability: Machine learning techniques are well suited for processing big data because they are made to handle massive amounts of data. As a result, businesses can make decisions based on information gleaned from such data.
Generalization: Algorithms for machine learning are capable of discovering broad patterns in data that can be used to analyze fresh, unexplored data. Even though the data used to train the model may not be immediately applicable to the task at hand, they are useful for forecasting future events.
Adaptiveness: As new data becomes available, machine learning algorithms are built to learn and adapt continuously. As a result, they can enhance their performance over time, becoming more precise and efficient as more data is made available to them.

The integration of machine learning and blockchain technology

The integration of machine learning and blockchain technology holds great promise for the future. Machine learning algorithms can be used to assess the data and generate predictions based on it using a decentralized and secure platform like the blockchain.

One possible area of usage for this integration is in the banking sector, where blockchain technology’s decentralized character and ability to prohibit unauthorized access to sensitive data can help machine learning algorithms detect fraud and money laundering more efficiently.

Machine learning and blockchain technology can also make a significant difference in supply chain management. While blockchain technology can be used to provide openness and accountability in the supply chain, machine learning algorithms can be utilized to optimize supply chain operations and forecast demand.

Blockchain technology can enable the secure and private sharing of medical records, while machine learning algorithms can be used to predict disease outbreaks and enhance patient outcomes.

The future of machine learning

The future of machine learning is expected to be characterized by continued advances in algorithms, computing power and data availability. As machine learning becomes more widely adopted and integrated into various industries, it has the potential to greatly impact society in a number of ways.

Some of the key trends and developments in the future of machine learning include:

Increased automation: As machine learning algorithms progress, they will be able to automate a larger range of jobs, requiring less human input and boosting productivity.
More personalized experiences: Machine learning algorithms will have the capacity to assess and make use of enormous volumes of data to deliver highly individualized experiences, such as personalized suggestions and adverts.
Enhanced judgment: As machine learning algorithms get better at making complicated judgments and predictions, numerous businesses will benefit from more precise and efficient decision-making.
AI ethical advancements: As machine learning becomes more common, there will be a growing emphasis on ensuring that it is developed and utilized ethically and responsibly, with a focus on safeguarding privacy and eliminating biases in decision-making.
Interdisciplinary collaboration: Machine learning will increasingly be used in collaboration with other fields, such as neuroscience and biology, to drive new discoveries and advancements in those areas.

Overall, the future of machine learning holds great promise and is expected to continue transforming a wide range of industries, from finance to healthcare, in the coming years.

GameFi could be the answer to unemployment for some — Aussie game studio

October 6, 2022 Coin Telegraph

automation

The executives say traditional jobs are increasingly at risk through factors such as automation, but GameFi can provide a viable alternative to earn a wage.

Australian-based Web3 game studio Ninja Syndicate's CEO and founder believes GameFi could usher in a new era where users can earn a living wage through blockchain games.

Speaking to Cointelegraph, founder John Nguyen and CEO Alex Dunmow say that traditional jobs are increasingly at risk through factors such as automation.

According to the game developers, blockchain games can and are playing a vital role for people to earn a living in the digital world through play-to-earn (P2E) and move-to-earn.

The process often requires significant work, but Dunmow says many mainstream triple-A games already feature "grinding for hundreds of hours," though the assets "provide no value for the player."

In GameFi titles digital assets can come in the form of nonfungible tokens (NFTs); users can then take them to a marketplace and sell them for fiat currency or crypto, essentially earning a wage through gaming, argued Dunmow:

"NFTs can give you the technical ability to take ownership of a game asset out of the control of the publisher of the game."

One of the best examples Dunmow has seen of people making a living through GameFi was a 2021 report about a community in the Philippines who turned to NFT gaming during COVID, which was now causing a shortage of workers in low-paying jobs as they could earn wages playing blockchain games instead.

"I saw the whole situation as a positive, a group of people who were likely being exploited in their low paying day jobs, have found a way to earn wages in the Metaverse."

Dunmow and Nguyen say the negativity around NFTs and blockchain in gaming present a challenge, but through their games, they hope to "subtly educate people about the benefits of NFTs."

The game studio has been developing a set of blockchain games under the “Supremacy World” ecosystem, which involves building, fighting and mining resources within a fictional dystopian world where factions use giant mechs to fight for territory and power.

Supremacy will eventually combine four games, a battle arena which is already out, a first-person shooter (FPS), an MMO and a real-time grand strategy (RTS) game.

Through the series of four interoperable games, the executives said they are creating an ecosystem where players have "sovereign ownership" over their digital assets and can use them in whatever way they want, explaining:

"What interoperable boils down to is being able to share digital assets between games.”

However, Nguyen noted that this interoperability also can also extend to “other game worlds, DeFi and PFP collection.”

"Supremacy will give people who own an NFT, whatever it may be, in-game assets in our world,” said Nguyen, adding that they recently were given a chance to design a custom mech skin for a user based on his Bored Apes' Yacht Club brand NFT, noting that he can now connect his "ape" and claim his custom skin based on his NFT.

Although given the time and resources required, Dunmow acknowledges they won't be able to custom design something for every user, but he says it shows what is possible.

Dunmow said that at the heart of their game, they’re still trying to build a "fun game” which he believes is vital to the industry's survival, adding that "attracting players from outside the crypto space is crucial, especially in bear markets."

"You make a fun game that has blockchain elements and attracts mainstream players; you are now disconnected from market forces, and you'll be able to survive any recession."

On Oct. 5, Ninja Syndicate announced a new deal with NFT minting and trading platform Immutable X allowing them to build on Immutable X’s layer-2 ecosystem, joining projects including Illuvium, Gods Unchained and GameStop.

automation

Trained AI models could become prized

Other use cases

Enabling new products, services, industries and jobs with AI

AI automation and the transformation of existing jobs

Some concerns and the call to action

Chatbots

Product recommendations

Fraud detection

Voice assistants

Image recognition

The future of AI in e-commerce

Better data analysis

Improved risk assessment

Reduced bias

Faster processing

Improved customer experience

Early automation (1960s to 1980s)

Introduction of online banking (1990s to 2000s)

Mobile banking (2000s to present)

Integration of new technologies (present to future)

DeFi vs. Digital banking

What is machine learning?

5 key features of machine learning

The integration of machine learning and blockchain technology

The future of machine learning

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