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AI can transform creative jobs ranging from graphic design to music creation

Artificial Intelligence can transform creative jobs ranging from graphic design to music creation and also AI can assist in the automation of routine chores, saving up time. Artificial intelligence (AI) is a fast-developing technology that has already demonstrated the ability to transform many parts of our life. While its application in a variety of areas might be advantageous, it also raises worries about how it may influence creative jobs.

AI is still a long way from perfectly replicating human creativity and emotion, both of which are required for genuinely great works of art or literature. Furthermore, even if machines eventually become capable of doing labor on par with humans.

AI has advanced significantly in recent years, and it is now capable of doing many previously thought-impossible activities, and it is extensively used in customer service and e-commerce, such as online shops, gambling sites, and blogs. However, AI can certainly aid in certain aspects of the creative process, such as idea generation or the fact that it has already demonstrated the ability to create entire pieces of literary work with very little human input.

Artificial intelligence is already changing the way we think about creative expression. AI-generated art has progressed dramatically in recent years and has even won honors at art events in some situations. This style of work is known as generative art since it is created by a computer program rather than a human artist. Generative art may be used to produce one-of-a-kind pieces of art that would be difficult to make using traditional approaches. AI-generated music, for example, may be written in a range of styles and genres and even duplicate the voices of renowned singers, dubbed Deepfake music so similar to the actual artist that some cannot tell the difference.

AI can transform creative industries ranging from graphic design to music creation. AI can assist in the automation of monotonous jobs, freeing up time for more creative efforts. AI may also be used to evaluate data to give insights into customer behavior or market trends. This might aid in product development or marketing campaign selections. AI may be used to automate operations such as picture or video editing, freeing up creatives to focus on more complicated jobs such as color grading or special effects.

A world filled with AI-created films, movies, and songs would be intriguing. AI technology has already been utilized to generate some stunning pieces of art, such as the short film Sunspring, which was scripted by an AI algorithm. In this universe, artificial intelligence (AI) might be utilized to create complete films or songs from scratch with no human intervention. The human brain can even envision it. It may also offer up new avenues for filmmakers and artists seeking new methods to express themselves artistically.

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Is Artificial Intelligence Hazarding Creative Jobs? - Analytics Insight

The findings of a recent pilot study headed by academics from the University of Illinois Chicago suggest that artificial intelligence may be a beneficial aid in the treatment of mental illness.

The study, which was the first to test an AI voice-based virtual coach for behavioural therapy, found changes in patients brain activity along with improved depression and anxiety symptoms after using Lumen, an AI voice assistant that delivered a form of psychotherapy. The UIC team says the results, which are published in the journal Translational Psychiatry, offer encouraging evidence that virtual therapy can play a role in filling the gaps in mental health care, where waitlists and disparities in access, are often hurdles that patients, particularly from vulnerable communities, must overcome to receive treatment.

Weve had an incredible explosion of need, especially in the wake of COVID, with soaring rates of anxiety and depression and not enough practitioners, said Dr Olusola A Ajilore, UIC professor of psychiatry and co-first author of the paper. This kind of technology may serve as a bridge. Its not meant to be a replacement for traditional therapy, but it may be an important stop-gap before somebody can seek treatment.

Lumen, which operates as a skill in the Amazon Alexa application, was developed by Ajilore and study senior author Dr Jun Ma, the Beth and George Vitoux Professor of Medicine at UIC, along with collaborators at Washington University in St. Louis and Pennsylvania State University, with the support of a $2 million grant from the National Institute of Mental Health.

The UIC researchers recruited over 60 patients for the clinical study exploring the applications effect on mild-to-moderate depression and anxiety symptoms, and activity in brain areas previously shown to be associated with the benefits of problem-solving therapy.

Two-thirds of the patients used Lumen on a study-provided iPad for eight problem-solving therapy sessions, with the rest serving as a waitlist control receiving no intervention.

After the intervention, study participants using the Lumen app showed decreased scores for depression, anxiety and psychological distress compared with the control group. The Lumen group also showed improvements in problem-solving skills that correlated with increased activity in the dorsolateral prefrontal cortex, a brain area associated with cognitive control. Promising results for women and underrepresented populations also were found.

Its about changing the way people think about problems and how to address them, and not being emotionally overwhelmed, Jun Ma said. Its a pragmatic and patient-driven behavior therapy thats well established, which makes it a good fit for delivery using voice-based technology.

A larger trial comparing the use of Lumen with both a control group on a waitlist and patients receiving human-coached problem-solving therapy is currently being conducted by the researcher. They stress that the virtual coach doesnt need to perform better than a human therapist to fill a desperate need in the mental health system.

The way we should think about digital mental health service is not for these apps to replace humans, but rather to recognise what a gap we have between supply and demand, and then find novel, effective and safe ways to deliver treatments to individuals who otherwise do not have access, to fill that gap, Jun Ma said.

(with inputs from ANI)

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Artificial Intelligence voice coach shows promise in treating depression, anxiety: Study - Organiser

The highlight of the stock market in 2023 has been The Magnificent Seven i.e. the surge in shares of the mega-cap tech stocks, Bank of America analysts write in their regular weekly column.

The Big 7 monopolistic U.S. Tech stocks - Apple (NASDAQ:AAPL), Microsoft (NASDAQ:MSFT), Google (NASDAQ:GOOGL), Amazon (NASDAQ:AMZN), Nvidia (NASDAQ:NVDA), Meta Platforms (NASDAQ:META), and Tesla (NASDAQ:TSLA) - are up 61% year-to-date. The group trades on 30x PE vs 17x for the rest of the S&P 500.

A similar situation can also be observed in Europe where a group of 7 luxury stocks trades on 36x vs the rest of Stoxx 600 trading on 12x PE.

One of the key drivers of the tech rally in 2023 has been the artificial intelligence (AI) frenzy and the popularity of generative AI tools, like OpenAIs ChatGPT. The analysts say AI is in a baby bubble so far.

Bubbles in right things (e.g. Internet) & wrong things (e.g. housing) always started by easy money, always ended by rate hikes, the analysts said in a note on Friday.

They say the Fed funds rate at 6%, not at 3%, could be the pain trade for the next 12 months as the market continues to expect rate cuts in the second half of 2023. In the near term, the S&P 500 extending its rally to 4400 could be another pain trade'.

We still fade SPX 4.2k$220 EPS + 20x PE + 200bps Fed cuts = as good as it gets; but clients so bored of bears, the analysts added.

As far as flows are concerned, $25.1 billion went to cash and $5.6B to bonds in the week to Wednesday.

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Artificial Intelligence-powered tools, such as ChatGPT, have the potential to revolutionize the efficiency, effectiveness and speed of the work humans do.

And this is true in financial markets as much as in sectors like health care, manufacturing and pretty much every other aspect of our lives.

I've been researching financial markets and algorithmic trading for 14 years. While AI offers lots of benefits, the growing use of these technologies in financial markets also points to potential perils. A look at Wall Street's past efforts to speed up trading by embracing computers and AI offers important lessons on the implications of using them for decision-making.

In the early 1980s, fueled by advancements in technology and financial innovations such as derivatives, institutional investors began using computer programs to execute trades based on predefined rules and algorithms. This helped them complete large trades quickly and efficiently.

Back then, these algorithms were relatively simple and were primarily used for so-called index arbitrage, which involves trying to profit from discrepancies between the price of a stock indexlike the S&P 500and that of the stocks it's composed of.

As technology advanced and more data became available, this kind of program trading became increasingly sophisticated, with algorithms able to analyze complex market data and execute trades based on a wide range of factors. These program traders continued to grow in number on the largely unregulated trading freewayson which over a trillion dollars worth of assets change hands every daycausing market volatility to increase dramatically.

Eventually this resulted in the massive stock market crash in 1987 known as Black Monday. The Dow Jones Industrial Average suffered what was at the time the biggest percentage drop in its history, and the pain spread throughout the globe.

In response, regulatory authorities implemented a number of measures to restrict the use of program trading, including circuit breakers that halt trading when there are significant market swings and other limits. But despite these measures, program trading continued to grow in popularity in the years following the crash.

Fast forward 15 years, to 2002, when the New York Stock Exchange introduced a fully automated trading system. As a result, program traders gave way to more sophisticated automations with much more advanced technology: High-frequency trading.

HFT uses computer programs to analyze market data and execute trades at extremely high speeds. Unlike program traders that bought and sold baskets of securities over time to take advantage of an arbitrage opportunitya difference in price of similar securities that can be exploited for profithigh-frequency traders use powerful computers and high-speed networks to analyze market data and execute trades at lightning-fast speeds. High-frequency traders can conduct trades in approximately one 64-millionth of a second, compared with the several seconds it took traders in the 1980s.

These trades are typically very short term in nature and may involve buying and selling the same security multiple times in a matter of nanoseconds. AI algorithms analyze large amounts of data in real time and identify patterns and trends that are not immediately apparent to human traders. This helps traders make better decisions and execute trades at a faster pace than would be possible manually.

Another important application of AI in HFT is natural language processing, which involves analyzing and interpreting human language data such as news articles and social media posts. By analyzing this data, traders can gain valuable insights into market sentiment and adjust their trading strategies accordingly.

These AI-based, high-frequency traders operate very differently than people do.

The human brain is slow, inaccurate and forgetful. It is incapable of quick, high-precision, floating-point arithmetic needed for analyzing huge volumes of data for identifying trade signals. Computers are millions of times faster, with essentially infallible memory, perfect attention and limitless capability for analyzing large volumes of data in split milliseconds.

And, so, just like most technologies, HFT provides several benefits to stock markets.

These traders typically buy and sell assets at prices very close to the market price, which means they don't charge investors high fees. This helps ensure that there are always buyers and sellers in the market, which in turn helps to stabilize prices and reduce the potential for sudden price swings.

High-frequency trading can also help to reduce the impact of market inefficiencies by quickly identifying and exploiting mispricing in the market. For example, HFT algorithms can detect when a particular stock is undervalued or overvalued and execute trades to take advantage of these discrepancies. By doing so, this kind of trading can help to correct market inefficiencies and ensure that assets are priced more accurately.

But speed and efficiency can also cause harm.

HFT algorithms can react so quickly to news events and other market signals that they can cause sudden spikes or drops in asset prices.

Additionally, HFT financial firms are able to use their speed and technology to gain an unfair advantage over other traders, further distorting market signals. The volatility created by these extremely sophisticated AI-powered trading beasts led to the so-called flash crash in May 2010, when stocks plunged and then recovered in a matter of minuteserasing and then restoring about $1 trillion in market value.

Since then, volatile markets have become the new normal. In 2016 research, two co-authors and I found that volatilitya measure of how rapidly and unpredictably prices move up and downincreased significantly after the introduction of HFT.

The speed and efficiency with which high-frequency traders analyze the data mean that even a small change in market conditions can trigger a large number of trades, leading to sudden price swings and increased volatility.

In addition, research I published with several other colleagues in 2021 shows that most high-frequency traders use similar algorithms, which increases the risk of market failure. That's because as the number of these traders increases in the marketplace, the similarity in these algorithms can lead to similar trading decisions.

This means that all of the high-frequency traders might trade on the same side of the market if their algorithms release similar trading signals. That is, they all might try to sell in case of negative news or buy in case of positive news. If there is no one to take the other side of the trade, markets can fail.

That brings us to a new world of ChatGPT-powered trading algorithms and similar programs. They could take the problem of too many traders on the same side of a deal and make it even worse.

In general, humans, left to their own devices, will tend to make a diverse range of decisions. But if everyone's deriving their decisions from a similar artificial intelligence, this can limit the diversity of opinion.

Consider an extreme, nonfinancial situation in which everyone depends on ChatGPT to decide on the best computer to buy. Consumers are already very prone to herding behavior, in which they tend to buy the same products and models. For example, reviews on Yelp, Amazon and so on motivate consumers to pick among a few top choices.

Since decisions made by the generative AI-powered chatbot are based on past training data, there would be a similarity in the decisions suggested by the chatbot. It is highly likely that ChatGPT would suggest the same brand and model to everyone. This might take herding to a whole new level and could lead to shortages in certain products and service as well as severe price spikes.

This becomes more problematic when the AI making the decisions is informed by biased and incorrect information. AI algorithms can reinforce existing biases when systems are trained on biased, old or limited data sets. And ChatGPT and similar tools have been criticized for making factual errors.

In addition, since market crashes are relatively rare, there isn't much data on them. Since generative AIs depend on data training to learn, their lack of knowledge about them could make them more likely to happen.

For now, at least, it seems most banks won't be allowing their employees to take advantage of ChatGPT and similar tools. Citigroup, Bank of America, Goldman Sachs and several other lenders have already banned their use on trading-room floors, citing privacy concerns.

But I strongly believe banks will eventually embrace generative AI, once they resolve concerns they have with it. The potential gains are too significant to pass upand there's a risk of being left behind by rivals.

But the risks to financial markets, the global economy and everyone are also great, so I hope they tread carefully.

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The benefits and perils of using artificial intelligence to trade stocks and other financial instruments - Tech Xplore

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A company's use of AI needs to align with its vision, mission and values and be based on a set of transparent and ethical principles and policies.DADO RUVIC/Reuters

Ian Robertson is the chief executive officer of strategic shareholder advisory and governance firm Kingsdale Advisors Inc.

Artificial Intelligence is bound to be the central engine of a fourth industrial revolution and is on the verge of playing a crucial role in the management and oversight of companies.

Some may be surprised to learn the use of artificial governance intelligence is already actively applied in boardrooms and corporate decision-making processes, such as due diligence of mergers and acquisitions, profiling investors, auditing annual reports, validating new business opportunities, analyzing and optimizing procurement, sales, marketing, and other corporate matters.

Most businesses are already utilizing some form of AI, algorithms and various platforms, such as ChatGPT. International organizations, governments, businesses, scientific and legal communities are racing to establish new regulations, laws, policies, ethical codes and privacy requirements as AI continues to evolve at a rapid pace while current legal and regulatory frameworks are lagging and becoming obsolete.

Against this backdrop it is important shareholders and boards start considering these issues, too, especially as it relates to augmenting or supplanting the role of corporate directors. Is your company ready for the rise of the robo-director?

In 2014, Hong Kong-based venture capital group Deep Knowledge Ventures appointed an algorithm named VITAL (Validating Investment Tool for Advancing Life Sciences) to its board of directors. VITAL was given the same right as human directors of the corporation to vote on whether the firm should invest in a specific company or not. Since then, VITAL has been widely acknowledged as the worlds first robo-director and other companies, such as software provider Tietoevry and Salesforce, have followed suit in employing AI in the boardroom.

The World Economic Forum has reported that by 2026, corporate governance will have undergone a robotization process on a massive scale. Momentum in computational power, breakthroughs in AI technology and advanced digitalization will inevitably lead to more established support for corporate directors using AI in their roles, if not their full replacement by autonomous systems. The result being that human directors sharing their decision-making powers with robo-directors will have become the new normal.

As the legal and regulatory landscape races to keep pace, companies need to forecast their compliance obligations that govern AI systems and boards will need to adjust to new corporate laws. In Canada, several coming federal and provincial privacy law reforms will affect the use of AI in business operations. The proposed federal Bill C- 27, if passed, would implement Canadas first artificial intelligence legislation, the Artificial Intelligence and Data Act (AIDA), which could come into effect in 2025. Current corporate law is not adapted to artificial governance intelligence and will have to cope with new and complex legal questions once the use of AI as a support tool or replacement of human directors increases.

There are some key questions directors and shareholders alike should be considering: How do current legal strategies apply to robo-directors? How and who will be responsible for the execution of fiduciary duties? Financial compensation and pay-for-performance will be of no use to robo-directors, so who is being compensated and being held accountable behind the scenes for programming and controlling the robo-director? What are the needs and limitations of a robo-director and what roles of a traditional director should be ring-fenced from them?

The use of AI provides opportunities and potential threats, both requiring strong risk and governance frameworks. The board is accountable legally and ethically for the use of AI within the company and its impact on employees, customers and shareholders, including third-party products which may embed AI technologies.

The use of AI needs to align with the companys vision, mission and values; be based on a set of safe, transparent and ethical principles and policies; and be rigorously monitored to ensure compliance with data privacy rules. Codes of conduct and ethics need to be updated to include an AI governance framework and ensure no bias in data-setting and decision-making. Companies should consider appointing an executive who will be responsible for AI governance and provide strategic insights to the board.

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Opinion: Soon, artificial intelligence will be running companies rise ... - The Globe and Mail