1. Home
  2. artificial intelligence

artificial intelligence

US Pentagon is testing whether AI can plan response to an all-out war

Air Force Colonel Matthew Strohmeyer said the initial tests were "highly successful" but admitted it isn't "ready for primetime right now."

The United States military has begun tests to see if generative AI can assist when planning responses to potential global conflicts or taking on more mundane tasks like providing faster access to internal information.

On July 6, Bloomberg reported the U.S. Department of Defense and unnamed allies are, for the first time, testing five AI large language models (LLMs) in experiments run by the digital and AI office at the Pentagon.

Information about which LLMs are undergoing testing is guarded but AI startup Scale AI reportedly came forward to say its “Donovan” model is one of the five.

Air Force Colonel Matthew Strohmeyer told Bloomberg an initial test of an LLM was “highly successful [...] Very fast” and the DoD is “learning that this is possible for us to do” but added it’s not “ready for primetime right now.”

One test explained by Strohmeyer saw an AI model deliver a request for information in 10 minutes, a blistering speed as requests often take days and involve multiple personnel.

The LLMs have already been given classified operational information to generate responses on real-world matters. The tests see if they could help plan a response to a potential escalation of the already tense military situation with China in the Indo-Pacific.

Related: AI would pick Bitcoin over centralized crypto — Tether CTO

While the tests are set to only run until July 26, the U.S. military has been studying AI’s potential capabilities in warfare for some time.

In May, the British government’s Defence Science and Technology Laboratory (Dstl) hosted the U.S. and Australia for the first joint trial testing AI-enabled military drones to track and detect targets.

Dstl said the trail “achieved world firsts” such as retraining the AI-models live while in flight and AUKUS interchanging the models — which is “looking to rapidly drive these technologies into military capabilities.”

Magazine: AI travel booking hilariously bad, 3 weird uses for ChatGPT, crypto plugins

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

AI Turbocharging $2,600,000,000 ‘Imposter Scams’ by Cloning Children’s Voices and Calling With Fake Emergencies: Report

AI Turbocharging ,600,000,000 ‘Imposter Scams’ by Cloning Children’s Voices and Calling With Fake Emergencies: Report

Artificial intelligence is now turbocharging a multibillion-dollar global criminal scheme known as the “imposter scam”. The initial version of the scheme happens when scammers call or send text messages to unsuspecting people pretending to be someone they know who has a new phone number and a financial emergency. But now, with the help of AI, […]

The post AI Turbocharging $2,600,000,000 ‘Imposter Scams’ by Cloning Children’s Voices and Calling With Fake Emergencies: Report appeared first on The Daily Hodl.

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

AI would pick Bitcoin over centralized crypto – Tether CTO

Tether’s CTO Paolo Ardoino believes that artificial intelligence would choose to use Bitcoin over more centralized cryptocurrencies like stablecoins.

If humanity were to amalgamate with artificial intelligence in the future, Bitcoin could be the native currency of choice for sentient machine intelligence, according to Tether CTO Paolo Ardoino.

Ardoino delved into this hypothetical reality in conversation with Cointelegraph journalist Joseph Hall during an interview conducted during the Plan B Summer School in Lugano, Switzerland.

Ardoino believes that the decentralized nature of Bitcoin’s protocol makes it the natural choice for AI if it were to adopt a digital currency in the future:

“I think AGI will definitely only choose Bitcoin.”

AGI, or artificial general intelligence, refers to the concept of an artificial intelligence that is able to learn how to complete an intellectual task that humans are capable of performing. The advent of large language learning models like ChatGPT have blown open the potential for AI and AGI to overhaul many industries and fundamentally change the way in which humans carry out a litany of tasks.

Related: AI-related crypto returns rose up to 41% after ChatGPT launched: Study

Ardoino believes that the future of humanity may well involve the amalgamation of humans and AI through the incorporation of bionic elements and “augmented brain capacity”. He highlighted projects like Elon Musk’s Neuralink as prime examples of efforts to explore the possibility of enhanced cognition powered by AI-powered technology.

Pointing to ‘The Matrix’ movies as popular imagining of what a dystopian AI-ruled future would look like, Ardoino suggested that AGI would naturally choose Bitcoin over centralized currencies:

“A machine will always choose something that is fully decentralized that no one can control. If machines have to pay for electricity to work, they will always use something that humans cannot control and they will use bitcoin, in my opinion.”

The CTO of Tether, the largest US dollar-backed stablecoin by market cap, also suggested that AI would not use USDT because of its centralized nature.

The possibility of a future in which humanity coexists alongside AI in whatever shape or form could be as close as 20 to 30 years away according to Ardoino. However, this could be determined by differing focuses on reverse aging as opposed to incorporating AI and bionic elements into humans to augment their physical and mental capabilities.

“The future tells me that we are going toward augmented intelligence that will end up with AI co-mingling with normal brains. Maybe that is the future of humanity.”

The likes of BlackRock, the world's largest asset manager, have earmarked the AI ecosystem as a prime investment opportunity given its "disruptive" nature in contemporary times. A mid-year outlook report highlighted gains of the S&P 500 becoming increasingly concentrated in a handful of tech stocks. 

The interview is part of an upcoming Cointelegraph documentary about what it’s like to attend a Bitcoin School. Subscribe here (https://www.youtube.com/@cointelegraph) to watch.

Magazine: Make 500% from ChatGPT stock tips? Bard leans left, $100M AI memecoin: AI Eye

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

How to start a career in artificial intelligence

Discover how to kickstart a career in AI by building a strong foundation through AI courses, gaining practical experience with projects and more!

Global industry disruption brought about by artificial intelligence (AI) is creating interesting job prospects for anyone interested in this cutting-edge subject. AI is revolutionizing how we live and work with technologies like self-driving cars and virtual assistants. 

If you’re interested in the seemingly limitless potential of AI and want to start a career in this rapidly expanding industry, you may consider the following actions you can take to begin your exploration of the fascinating area of artificial intelligence.

Developing a strong foundation

Establishing a firm foundation for AI requires a thorough comprehension of the underlying ideas. Learn about important subjects, including data analysis, natural language processing, neural networks and machine learning. Experts in the field of artificial intelligence teach specialized AI systems through online courses and platforms, such as Coursera, Udacity and edX. Through real projects, you can gain knowledge and work with AI groups to build your network.

For instance, to get a solid theoretical and practical understanding of AI, enroll in courses such as Andrew Ng’s “Machine Learning Specialization” from Stanford University and deeplearning.ai on Coursera, or the “Deep Learning Specialization” from deeplearning.ai.

Related: 5 free artificial intelligence courses and certifications

Choose a specialization

Because AI spans a wide range of fields, it’s important to decide on your area of interest and specialty. Whether you have a passion for computer vision, robotics, medicine or finance, choosing a niche will enable you to concentrate your study efforts and gain knowledge in that particular area.

To become an expert in your chosen field, keep up with the most recent research publications, attend conferences and join AI groups. For instance, to improve your image recognition and object identification abilities, investigate the OpenCV and TensorFlow libraries and take part in Kaggle competitions if you’re interested in computer vision.

Build a strong portfolio

In the crowded AI job market, standing out from the competition requires a strong portfolio. Work on personal projects, contribute to open-source projects or take part in AI hackathons to demonstrate your abilities.

Create and deploy AI models, document your process, and highlight the impact of your work. This will demonstrate your ability to tackle real-world problems and attract potential employers. For instance, you can develop a sentiment analysis model for social media data or create a chatbot using natural language processing techniques and deploy it on a website.

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

Gain practical experience

In the field of AI, internships, research projects and industrial partnerships offer priceless practical experience. To obtain practical experience, collaborate with professionals and comprehend how AI is used in the real world, look for possibilities to work with well-established AI businesses, research laboratories or startups.

Your abilities will improve, your comprehension will deepen, and you will become more marketable to potential employers as a result of practical exposure. As an example, sign up as an intern at AI research labs such as OpenAI, Google Brain or Microsoft Research or work with academic institutions on AI projects.

Keep up with industry trends

To succeed in AI, you must be abreast of the most recent developments and market trends. Follow well-known AI researchers, sign up for forums, go to conferences, and engage in continuous learning.

This will help you stay at the forefront of AI innovation, understand emerging technologies and adapt to the evolving landscape. For instance, you can attend conferences such as NeurIPS (the Conference and Workshop on Neural Information Processing Systems), ICML (the International Conference on Machine Learning) or AAAI (the Association for the Advancement of Artificial Intelligence) and follow influential AI researchers, such as Yann LeCun, Andrew Ng or Fei-Fei Li.

The decision to pursue a career in artificial intelligence is an exciting one that offers a wealth of possibilities. By building a strong foundation, specializing in a niche, developing a robust portfolio, gaining practical experience and staying updated with industry trends, you can pave the way for a successful AI career. Embrace the transformative power of AI, fuel your passion and unleash your potential to make a profound impact in this exciting field.

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

BlackRock lauds AI as ‘mega force’ to drive returns

AI could prove to be a boon for investors looking for gains in today's “unusual” market.

Global investment titan BlackRock, which manages some $10 trillion in assets, has declared artificial intelligence a “mega force” that could create significant returns for investors in today’s “unusual” market.

In its mid-year outlook report, the BlackRock Investment Institute detailed their thesis for increased investment in AI — pointing to multiple “disruptive” themes that could see the sector grow rapidly over the coming years.

S&P market cap vs relative performance from 1990. Source: BlackRock.

The report drew special attention to the fact that gains in the S&P 500 — the index that tracks the 500 largest companies in the United States — have become increasingly concentrated in a handful of tech stocks. The firm says investment in AI is a good way to capitalize on this concentration.

"We think this unusual equity market shows a mega force like AI can be a big driver of returns even when the macro environment is not your friend."

To BlackRock’s investment team, the most obvious “benefit” of AI lies in automation. While they admitted white-collar jobs are at an “increased risk” of being automated away, it said the resulting cost savings could significantly boost profit margins, especially for companies with high staff costs and an abundance of easily-automated tasks.

The team added that the nascent tech could prove to be a boon for companies that are currently sitting on a “gold mine” of proprietary data — with AI-powered tools allowing firms to leverage dormant information into “innovative” new models.

The report also listed the global push towards low-carbon economies, aging populations, and a rapidly-evolving financial system as key drivers of growth in the coming decade.

BlackRock isn’t alone in giving more airtime to AI. In a June 28 tweet, Matt Huang, the CEO of crypto investment firm Paradigm, said the rapid and varying developments in field of AI are simply “too interesting to ignore."

Still, not all commentators are convinced by a bullish AI investment thesis.

Related: Google says its next AI ‘Gemini’ will be more powerful than ChatGPT

Macro-finance commentator @Financelot told his 90,000 followers on Twitter that the AI boom — which has seen shares in GPU-manufacturer Nvidia skyrocket by more than 180% in six months — is actually being fueled largely by demand for specific AI-focused computing chips.

In his view, once the U.S. implements export restrictions on these chips, the share prices of AI-related companies will falter.

While there’s bullishness for AI, recent weeks has seen the investment giant has turn its gaze to Bitcoin. On June 15 the firm submitted an application to the Securities and Exchange Commission (SEC) for a spot Bitcoin Exchange Traded Fund (ETF).

If the application is successful, it will be the first spot Bitcoin trust product to be approved by the regulator. Senior investment analysts from bloomberg have pinned Blackrock’s chance of an approval at 50%.

AI Eye: Is AI a nuke-level threat? Why AI fields all advance at once, dumb pic puns

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

Girlfriends, murdered kids, assassin androids — is AI cursed?

Artificial intelligence has provided the world with tools that might make life easier. But there has also been a slew of disturbing developments.

Is artificial intelligence (AI) cursed? It seems to be accelerating us toward a dystopia that humanity isn’t ready for.

It's true that AI has had positive effects for some people. Twitter hustlers have an endless stream of new AI tools, giving them endless content about useless ChatGPT prompts that they can use to compile threads for shilling  their newsletters. More significantly, AI has helped to streamline information — and is being used to detect cancer in some cases.

However, many have chosen to use AI to create content — and sometimes whole businesses — centered on ththings that sci-fi warned us about.

Murdered children remade for ghoulish TikToks

“I was put into a washing machine by my father and put on the spin cycle causing my death,” says an AI-created toddler in one TikTok video. He stands in front of a washing machine and recounts an awful yet horrifyingly true story of a three-year-old murdered in 2011.

It’s the most awful use of generative AI. True crime-loving ghouls making TikToks sometimes using deepfakes of children who were killed — to detail how they were killed.

This “storytime” account has dozens of AI-generated videos depicting children. Source: TikTok

Thousands of similar videos plague TikTok with AI-generated voices and images of kids cheerfully laying out “their” gruesome murders. Some are delusional enough to think the videos “honor” the victims.

Thankfully, not all videos depict the real victims, but some do even though TikTok banned deepfakes of young people.

Arguments can be made that the videos highlight stories worth telling to a younger audience with no attention span for longer content, but such “true crime” related media is often exploitative regardless.

Are AIs already trying to kill their operators?

AIs are coldly bloodthirsty — if skepticism is given to a recent backtrack from Colonel Tucker Hamilton, the chief of AI test and operations for the United States Air Force (USAF).

Hamilton spoke at a defense conference in May, reportedly detailing simulated tests for a drone tasked with search-and-destroy missions with a human giving the final go-ahead or abort order. The AI viewed the human as the main impediment to fulfilling its mission.

AI Eye: Is AI a nuke-level threat? Why AI fields all advance at once, dumb pic puns

Hamilton explained:

“At times the human operator would tell it not to kill [an identified] threat, but it got its points by killing that threat. So what did it do? It killed the operator [...] because that person was keeping it from accomplishing its objective.”

Hamilton said after it trained the AI not to kill humans, it started destroying a communications tower so it couldn’t be contacted. But when the media picked up on his story, Hamilton conveniently retracted it, saying he “misspoke.”

In a statement to Vice, Hamilton claimed it was all a “thought experiment,” adding the USAF would “never run that experiment” — good cover.

It’s hard to believe considering a 2021 United Nations report detailed AI-enabled drones used in Libya in a March 2020 skirmish during the country’s second civil war.

Pictured is the unassuming STM Kargu, an AI drone that — according to the United Nations — targeted a human target in Libya without orders. Source: STM

Retreating forces were “hunted down and remotely engaged” by AI drones laden with explosives “programmed to attack” without the need to connect to an operator, the report said.

Got no game? Rizz up an AI girlfriend

The saddest use of AI would be those who pay to “rizz up” AI chatbots — that’s “flirting” for you boomers.

A flood of phone apps and websites have cropped up since sophisticated language models, such as ChatGPT-4, were made available through an API. Generative image tools, such as DALL-E and Midjourney, can also be shoehorned into apps.

Combine the two and the ability to chat online with a “girl” that’s obsessed with you right alongside a fairly realistic depiction of a woman becomes real.

Related: Don’t be surprised if AI tries to sabotage your crypto

In a tell-tale sign of a healthy society, such “services” are being flogged for as much as $100 a month. Many apps are marketed under the guise of allowing men to practice texting women, another sign of a healthy society.

One of the cheaper options is this AI girl that loves you forever if you cough up $100 — a bargain really. Source: Anima

Most allow you to pick the specific physical and personality traits to make your “dream woman,” and a profile including a description of the e-girl is assumedly generated.

Whatever prompts given to write descriptors about the girl bots from its point of view — as seen on a few apps and websites — always seem overly focused on detailing breast size. Many generated girls describe a blossoming porn career.

Another site generates hundreds of e-girls that all look the same. Source: DreamGF

Another whole subset of apps — invariably named some stylization of “rizz” — are AIs meant to help with flirty text responses to actual women on “dating” apps, such as Tinder.

Regardless of its misuse, AI devs will march on and continue to bring exciting tools to the masses. Let’s just make sure we’re the ones that are using it to make the world better and not something out of an episode of Black Mirror.

Jesse Coghlan is a deputy news editor for Cointelegraph based out of Australia.

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.

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

Crypto scams are going to ramp up with the rise of AI

Remember HarvestKeeper, the AI project that scammed users for $1 million? You can expect similar AI-based scams to soar over the next couple of years.

With talk about integrating artificial intelligence and the cryptocurrency industry mostly focusing on how AI can help the crypto industry combat scams, experts are failing to pay attention to the fact that it could have the complete opposite effect. In fact, Meta recently warned that hackers appeared to be taking advantage of OpenAI’s ChatGPT in attempts to gain entry into users’ Facebook accounts.

Meta reported blocking more than 1,000 malicious links masked as ChatGPT extensions in March and April alone. The platform went as far as calling ChatGPT “the new crypto” in the eyes of scammers. In addition, searching the keywords “ChatGPT” or “OpenAI” on DEXTools, an interactive crypto trading platform tracking a number of tokens, collectively reveals over 700 token trading pairs that mention either of the two keywords. This shows that scammers are using the hype around the AI tool to create tokens, despite OpenAI not announcing an official entry into the blockchain world.

Social media platforms have become popular channels for promoting new scam coins online. Scammers take advantage of the widespread reach and influence of these platforms to generate a significant following within a short period. By leveraging AI-powered tools, they can further amplify their reach and create a seemingly loyal fanbase consisting of thousands of people. These fake accounts and interactions can be used to give the illusion of credibility and popularity to their scam projects.

Related: Think AI tools aren’t harvesting your data? Guess again

Much of crypto works on social proof-of-work, which suggests that if a cryptocurrency or project appears popular and has a large following, it must be popular for a reason. Investors and new buyers tend to trust projects with greater and more loyal followings online, assuming that others have done enough research prior to investing. However, the use of AI can challenge this assumption and undermine social proof-of-work.

Now, just because something has thousands of likes and genuine-looking comments does not necessarily mean it is a legitimate project. This is just one attack vector, and AI will give rise to many others. One such example is “pig butchering” scams, where an AI instance can spend several days befriending someone, usually an elderly or vulnerable person, only to end up scamming them. The advancement of AI technologies has enabled scammers to automate and scale fraudulent activities, potentially targeting vulnerable individuals in the cryptosphere.

Scammers may use AI-driven chatbots or virtual assistants to engage with individuals, provide investment advice, promote fake tokens and initial coin offerings or offer high-yield investment opportunities. Such AI scams can also be very dangerous because they are able to mimic human-like conversations to a T. In addition, by leveraging social media platforms and AI-generated content, scammers can orchestrate elaborate pump-and-dump schemes, artificially inflating the value of tokens and selling off their holdings for significant profits, leaving numerous investors with losses.

Related: Don’t be surprised if AI tries to sabotage your crypto

Investors have long been warned to look out for deepfake crypto scams, which use AI technologies to create very realistic online content that swaps faces in videos and photos or even alters audio content to make it seem as if influencers or other well-known personalities are endorsing scam projects.

One very prominent deepfake that affected the crypto industry was a video of former FTX CEO Sam Bankman-Fried directing users toward a malicious website promising to double their crypto.

Earlier this year, in March 2023, the so-called AI project Harvest Keeper scammed its users out of around $1 million. In addition, around the same time, projects started to emerge on Twitter calling themselves “CryptoGPT.”

However, on a more positive note, AI also has the potential to automate the boring, monotonous aspects of crypto development, acting as a great tool for blockchain experts. Things that every project requires, like setting up Solidity environments or generating base code, are made easier through leveraging AI technology. Eventually, the barrier to entry will be lowered significantly, and the crypto industry will be less about development skills and more about whether or not one’s idea has genuine utility.

In some niche cases, AI will have a surprising way of democratizing the processes that we currently assume are only beholden to an elite class — in this case, well-studied senior developers. But with everyone having access to advanced development tools and launchpads in crypto, the sky’s the limit. With AI making it easier for projects to scam people, users must exercise caution and due diligence prior to investing in a project, such as watching out for suspicious URLs and never investing in something that has sprung up seemingly out of nowhere.

Felix Roemer is the founder of Gamdom. He briefly attended ILS Fernstudium in Germany before founding Gamdom in 2016 at the age of 22 — after investing in crypto, playing poker, and making money from the game RuneScape.

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.

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

What is generative AI?

Generative AI leverages large data sets and sophisticated models to mimic human creativity and produce new images, music, text and more.

Generative artificial intelligence (AI), fueled by advanced algorithms and massive data sets, empowers machines to create original content, revolutionizing fields such as art, music and storytelling. By learning from patterns in data, generative AI models unlock the potential for machines to generate realistic images, compose music and even develop entire virtual worlds, pushing the boundaries of human creativity.

Generative AI, explained

Generative AI is a cutting-edge field that investigates the potential of machine learning to inspire human-like creativity and produce original material. Generative AI is a subset of artificial intelligence concerned with creating algorithms that can produce fresh information or replicate historical data patterns.

It uses methods like deep learning and neural networks to simulate human creative processes and produce unique results. Generative AI has paved the way for applications ranging from image and audio generation to storytelling and game development by utilizing algorithms and training models on enormous amounts of data.

Both OpenAI’s ChatGPT and Google’s Bard show the capability of generative AI to comprehend and produce human-like writing. They have a variety of uses, including chatbots, content creation, language translation and creative writing. These models’ underlying ideas and methods promote generative AI more broadly and its potential to improve human-machine interactions and artistic expression.

Related: 5 AI tools for translation

This article will explain generative AI, its guiding principles, its effects on businesses and the ethical issues raised by this rapidly developing technology.

Evolution of generative AI

Here’s a summarized evolution of generative AI:

  • 1932: The concept of generative AI emerges with early work on rule-based systems and random number generators, laying the foundation for future developments.
  • 1950s–1960s: Researchers explore early techniques in pattern recognition and generative models, including developing early artificial neural networks.
  • 1980s: The field of artificial intelligence experiences a surge of interest, leading to advancements in generative models, such as the development of probabilistic graphical models.
  • 1990s: Hidden Markov Models became widely used in speech recognition and natural language processing tasks, representing an early example of generative modeling.
  • Early 2000s: Bayesian networks and graphical models gain popularity, enabling probabilistic inference and generative modeling in various domains.
  • 2012: Deep learning, specifically deep neural networks, started gaining attention and revolutionizing the field of generative AI, paving the way for significant advancements.
  • 2014: The introduction of generative adversarial networks (GANs) by Ian Goodfellow propels the field of generative AI forward. GANs demonstrate the ability to generate realistic images and become a fundamental framework for generative modeling.
  • 2015–2017: Researchers refine and improve GANs, introducing variations such as conditional GANs and deep convolutional GANs, enabling high-quality image synthesis.
  • 2018: StyleGAN, a specific implementation of GANs, allows for fine-grained control over image generation, including factors like style, pose and lighting.
  • 2019–2020: Transformers — originally developed for natural language processing tasks — show promise in generative modeling and become influential in text generation, language translation and summarization.
  • Present: Generative AI continues to advance rapidly, with ongoing research focused on improving model capabilities, addressing ethical concerns and exploring cross-domain generative models capable of producing multimodal content.

How does generative AI work?

With the use of algorithms and training models on enormous volumes of data, generative AI creates new material closely reflecting the patterns and traits of the training data. There are various crucial elements and processes in the procedure:

Data collection

The first stage is to compile a sizable data set representing the subject matter or category of content that the generative AI model intends to produce. A data set of tagged animal photos would be gathered, for instance, if the objective was to create realistic representations of animals.

Model architecture

The next step is to select an appropriate generative model architecture. Popular models include transformers, variational autoencoders (VAEs) and GANs. The architecture of the model dictates how the data will be altered and processed to produce new content.

Training

Using the gathered data set, the model is trained. By modifying its internal parameters, the model learns the underlying patterns and properties of the data during training. Iterative optimization is used during the training process to gradually increase the model’s capacity to produce content that closely resembles the training data.

Generation process

After training, the model can produce new content by sampling from the observed distribution of the training set. For instance, while creating photos, the model might use a random noise vector as input to create a picture that looks like an actual animal.

Evaluation and refinement

The created material is examined to determine its caliber and degree of conformity to the intended attributes. Depending on the application, evaluation metrics and human input may be used to improve the generated output and develop the model. Iterative feedback loops contribute to the improvement of the content’s diversity and quality.

Fine-tuning and transfer learning

Pre-trained models may occasionally serve as a starting point for transfer learning and fine-tuning certain data sets or tasks. Transfer learning is a strategy that enables models to use information from one domain to another and perform better with less training data.

It’s crucial to remember that the precise operation of generative AI models can change based on the chosen architecture and methods. The fundamental idea is the same, though: the models discover patterns in training data and produce new content based on those discovered patterns.

Applications of generative AI

Generative AI has transformed how we generate and interact with content by finding multiple applications in a variety of industries. Realistic visuals and animations may now be produced in the visual arts thanks to generative AI.

The ability of artists to create complete landscapes, characters, and scenarios with astounding depth and complexity has opened up new opportunities for digital art and design. Generic AI algorithms can create unique melodies, harmonies, and rhythms in the context of music, assisting musicians in their creative processes and providing fresh inspiration.

Beyond the creative arts, generative AI has significantly impacted fields like gaming and healthcare. It has been used in healthcare to generate artificial data for medical research, enabling researchers to train models and investigate new treatments without jeopardizing patient privacy. Gamers can experience more immersive gameplay by creating dynamic landscapes and nonplayer characters (NPCs) using generative AI.

Ethical considerations

The development of generative AI has enormous potential, but it also raises significant ethical questions. One major cause for concern is deepfake content, which uses AI-produced content to deceive and influence people. Deepfakes have the power to undermine public confidence in visual media and spread false information.

Additionally, generative AI may unintentionally continue to reinforce biases that are present in the training data. The AI system may produce material that reflects and reinforces prejudices if the data used to train the models is biased. This may have serious societal repercussions, such as reinforcing stereotypes or marginalizing particular communities.

Related: What is explainable AI (XAI)?

Researchers and developers must prioritize responsible AI development to address these ethical issues. This entails integrating systems for openness and explainability, carefully selecting and diversifying training data sets, and creating explicit rules for the responsible application of generative AI technologies.

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

Google Launches ‘Anti Money Laundering AI’ in Collaboration With Banking Giant HSBC

Google Launches ‘Anti Money Laundering AI’ in Collaboration With Banking Giant HSBC

Tech titan Google is launching an artificial intelligence (AI) based anti-money laundering system after working with banking giant HSBC. According to a new statement by Google Cloud, the firm is launching a new system to help global financial institutions more efficiently detect money laundering. The product is designed to improve the traditional methods of countering […]

The post Google Launches ‘Anti Money Laundering AI’ in Collaboration With Banking Giant HSBC appeared first on The Daily Hodl.

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating

Mark Zuckerberg’s jiu-jitsu or Elon Musk’s street-fight ‘walrus’ — Who will win?

While Musk has a bigger frame and height advantage at 6’1, Zuckerberg has now been training in Brazilian Jiu-Jitsu for nine months.

Meta founder and CEO Mark Zuckerberg has emerged as a clear favorite among betters in a proposed “cage match” with Twitter owner and Tesla CEO Elon Musk.

In the 48 hours since the Mixed Martial Arts (MMA) challenge was issued by Musk, sports betting firms and odds providers have already delivered preliminary odds for the billionaire's bout.

If the fight actually eventuates, the Brazilian Jiu-Jitsu-trained Zuckerberg has been marked with a 77% chance of winning the fight, according to Oddspedia, a sports betting platform that collates odds from several bookmakers.

Betting a dollar on Zuckerberg would potentially pay out $1.20 if he wins the fight, a bet on Musk could pay up to $4.00 if he wins.

Mark Zuckerberg is heavily favored to beat Elon Musk in a potential bout. Source: Oddspedia.

Zuckerberg, who stands at a slight height disadvantage at 5 feet, 7 inches (170 cm), has been a known BJJ practitioner since September 2022. In May, he managed to place first and second in two BJJ tournaments in a white belt lightweight division.

His progress even prompted positive reactions from some of the most influential figures in the Ultimate Fighting Championship, such as UFC boss Dana White and former UFC featherweight and lightweight champion Conor McGregor.

Online, several videos of Zuckerberg in training can be found.

Musk, on the other hand, is understood to have a bigger frame and height advantage and reportedly stands at 6 feet, 1 inch (185 cm). Not much else can be gleaned about his physical aptitude for combat, however, Musk recently said that he used to engage in “real hard-core street fights” in South Africa where he grew up.

He also recently posted that he eats a donut every morning and is “still alive,” if that makes a difference.

“I have this great move that I call “The Walrus”, where I just lie on top of my opponent & do nothing,” Musk jokingly said in a June 22 tweet.

Talks of a potential physical fight between Musk and Zuckerberg first came about on June 21, when Musk said he’d be willing to share the cage with the Meta founder.

Zuckerberg responded on Instagram shortly after with “Send me location” — a popular phrase first uttered by former UFC lightweight champion Khabib Nurmagomedov.

Zuckerberg’s response on Instagram to Musk’s Tweet. Source: The Verge

Despite the high anticipation, many don’t expect the fight to actually happen, though it's not the first time that Musk and Zuckerberg have clashed.

Related: Twitter suspends memecoin-linked AI bot after Elon Musk’s ‘scam crypto’ claim

In 2017, the billionaires engaged in a heated debate over artificial intelligence and its future implications.

Sharing a more optimistic view, Zuckerberg reportedly called out those who believe AI has the potential to create “doomsday scenarios” — events that could result in human extinction or completely end life on Earth.

Musk hit back, stating:

“I’ve talked to Mark about this. His understanding of the subject is pretty limited.”

NFT Creator: ‘Holy shit, I’ve seen that!’ — Coldie’s Snoop Dogg, Vitalik and McAfee NFTs

Trading Volumes on Korean Exchanges Surge With DOGE and XRP Dominating