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Worldcoin co-founder Sam Altman affirmed that privacy concerns will decrease after Worldcoin open-sources its code.

The Open AI boss and Worldcoin co-founder suggested that blockchain and crypto could allay fears presented by the threat of super artificial intelligence (AI).

Speaking at the Worldcoin Seoul Meetup last weekend, Worldcoin co-founder Sam Altman said no one has answers to some of artificial intelligences toughest questions.

Meanwhile, CEO Alex Bania said the Worldcoin blockchain needs 100 million users to reach critical mass.

Worldcoins South Korean event comes in the wake of the launch of its World App in May. The company wants to build a decentralized digital identity system coupled with a new wealth distribution model using Worldcoin.

Its orb technology photographs human irises and uses AI to learn what is truly human. Bania said the new technologys importance will grow as the importance of distinguishing between human and synthetic content grows.

AGI [Artificial General Intelligence] and Worldcoin are individual ideas, but they are relevant at a time when AGI is coming, said Altman. Worldcoin is also related to universal basic income, and there will be a lot of economic progress through it.

Altman has set his sights on Korea as the next virtual currency and artificial intelligence hub. He said he would expand investments in Korean AI firms.

Worldcoin has got into hot water for gaps between its public messaging and user experiences.

A Massachusetts Institute of Technology report revealed Worldcoin contractors offered developing nations free money in exchange for biometric data. Representatives collected more data than they admitted and failed to acquire meaningful consent.

Respondents received Worldcoin and were told iris imaging was necessary to ensure equitable allocations. This is despite investors Andreessen Horowitz and Worldcoin employees receiving a 10% allocation apiece.

Worldcoins orb stores iris data encoded as an IrisHash and reportedly uses zero-knowledge proofs to check whether the information already exists on the Worldcoin network.

Crypto trading bots are becoming increasingly important in optimizing trading strategies and maximizing profits. Learn more here with our guide.

While the company says data on the orb will eventually be deleted, details on interim handling of user data are scant, except that they will be fodder for AI algorithms to learn about humans.

European regulators impose fines of up to 4% of global revenues for inadequately protecting the data of European citizens.

For BeInCryptos latestBitcoin(BTC) analysis,click here.

In adherence to the Trust Project guidelines, BeInCrypto is committed to unbiased, transparent reporting. This news article aims to provide accurate, timely information. However, readers are advised to verify facts independently and consult with a professional before making any decisions based on this content.

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OpenAI CEO Says Worldcoin AI Concerns Will Soon Diminish - BeInCrypto

ML (machine learning) is an approach to solving difficult computational problems instead of coding using a programming language you build an algorithm that learns the solution from data samples.

AI (artificial intelligence) is a field of computer science dealing with problems (e.g., image classification, working with human language) that are difficult to solve using traditional programming. ML and AI go hand in hand, with ML being a tool to solve problems formulated in AI.

AGI (artificial general intelligence) - is the correct term for what popular culture usually implies by AI the ability of computers to achieve human-like intellectual capabilities and broad reasoning. It is still the holy grail for researchers working in the AI field.

An artificial neural network (ANN) is a class of ML algorithms and data structures (or models for short) so called because it was inspired by the structure of biological neural tissue. But this doesnt completely mimic all the biological mechanisms behind it. Rather, ANNs are complicated mathematical functions that are based on ideas from living species biology.

Neural networks are layered structures consisting of uniform units interconnected with each other in a network. The way these units are interconnected is called architecture. Each connection has an associated number called weight and the weights store information the model learns from data. So, when you read the model has 2 billion parameters, it means that there are 2 billion connections (and weights) in the model, and it roughly designates the information capacity of the neural network.

Neural networks have been studied since the 1980s but made a real impact when the computer games industry introduced cheap personal supercomputers known as graphical processing units (GPUs). Researchers adapted this hardware for the neural network training process and achieved impressive results. One of the first deep learning architectures, the convolutional neural network (CNN), was able to carry out sophisticated image recognition that was difficult with classical computer vision algorithms. Since then, ML with neural networks has been rebranded as deep learning, with deep referring to the complicated NN architectures the networks are able to explore.

Id recommend videos by Grant Sanderson available on his animated math channel.

To work with human language using computers, language must be defined mathematically. This approach should be sufficiently generic to include the distinctive features of every language. In 2003 researchers discovered how to represent language with neural networks and called it the neural probabilistic language model or LM for short. This works like predictive text in a mobile phone given some initial sequence of words (or tokens), the model can predict the next possible words with their respective probabilities. Continuing this process using previously generated words as input (this is autoregression) the model can generate text in the language for which it was trained.

Representing sequences of items was a challenging problem for neural networks. There were several attempts to solve the problem (mostly around variations of recurrent neural networks), which yielded some important ideas (e.g., word embedding, encoder-decoder architecture, and attention mechanism). In 2017 a group of Google researchers proposed a new NN architecture that they called a transformer. It combined all these ideas with effective practical implementation. It was designed to solve the language translation problem (hence the name) but proved to be efficient for capturing the statistical properties of any sequence data.

OpenAI experimented with transformers to build a neural probabilistic language model. The results of their experiments are called GPT (generative pre-trained transformer) models. Pre-trained means they were training the transformer NN on a large body of texts mined on the Internet and then taking its decoder part for language representation and text generation. There were several generations of GPTs:

Given the enormous number of parameters GPT models have (in fact, you need a huge computational cluster with hundreds to thousands of GPUs to train and serve these models), they were called Large Language Models (LLMs).

The original GPT-3 is still a word prediction engine and thus is mostly of interest to AI researchers and computational linguists. Given some initial seed or prompt, it can generate text infinitely, which makes little practical sense. The OpenAI team continued to experiment with the model, trying to fine-tune it to treat prompts as instructions to execute. They fed in a large dataset of human-curated dialogues and invented a new approach (RLHF reinforcement learning from human feedback) to significantly speed up this process with another neural network as a validator agent (typical in AI research). They released a model called InstructGPT as an MVP based on a smaller GPT-3 version and in November 2022 released a full-featured version called ChatGPT. With its simple chatbot and web UI, it changed the IT world.

Given that LLMs are just sophisticated statistical machines, the generation process could go in an unexpected and unpleasant direction. This type of result is sometimes called an AI hallucination, but from the algorithmic perspective, it is still valid, though unexpected, by human users.

Raw LLMs require treatment and additional fine-tuning with human validators and RLHF, as previously mentioned. This is to align LLMs with human expectations, and not surprisingly the process itself is called alignment. This is a long and tedious procedure with considerable human work involved; this could be considered LLM quality assurance. The alignment of the models is what distinguishes OpenAI/Microsoft ChatGPT and GPT-4 from their open-source counterparts.

Neural networks are black boxes (a huge array of numbers with some structure on top). There are some methods to explore and debug their internals but the exceptional generalization qualities of GPTs remain unexplained. This is the main reason behind the ban movement some researchers think we are playing with fire (science fiction gives us fascinating scenarios of AGI birth and technological singularity) before we get a better understanding of the processes underlying LLMs.

The most popular include:

GPTs are the most mature models with API access provided by OpenAI and Microsoft Azure OpenAI services (if you need a private subscription). But this is the frontier of AI and many interesting things have happened since the release of ChatGPT. Google has built its PaLM-2 model; Meta open-sourced their LLaMA models for researchers, which spurred lots of tweaks and enhancements (e.g., Alpaca from Stanford) and optimization (now you can run LLMs on your laptop and even smartphone).

Huggingface provides BLOOM and StarCoder and HuggingChat which are completely open source, without the LLaMA research-only limitation. Databricks trained their own completely open-source Dolly model. Lmsys.org is offering its own Vicuna LLM. Nvidias deep learning research team is developing its Megatron-LM model. The GPT4All initiative is also worth mentioning.

However, all these open-source alternatives are still behind OpenAIs major tech (especially in the alignment perspective) but the gap is rapidly closing.

The easiest way is to use OpenAI public service or their platform API playground, which offers lower-level access to the models and more control over network inner workings (specify system context, tune generation parameters, etc). But you should carefully review their service agreements since they use user interactions for additional model improvements and training. Alternatively, you can choose Microsoft Azure OpenAI services, which provide the same API and tools but with private model instances.

If you are more adventurous, you can try LLM models hosted by HuggingFace, but youll need to be more skilled with Python and data science tooling. Denis Shipilov is experienced Solutions Architect with wide range of expertise from distributed systems design to the BigData and Data Science related projects.

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Your Ultimate Guide to Chat GPT and Other Abbreviations - KDnuggets

Generative AI will have a profound impact across industries.

Thats what Amazon Web Services (AWS) believes, according to Hussein Shel, an Energy Enterprise Technologist for the company, who said Amazon has invested heavily in the development and deployment of artificial intelligence and machine learning for more than two decades for both customer-facing services and internal operations.

We are now going to see the next wave of widespread adoption of machine learning, with the opportunity for every customer experience and application to be reinvented with generative AI, including the energy industry, Shel told Rigzone.

AWS will help drive this next wave by making it easy, practical, and cost-effective for customers to use generative AI in their business across all the three layers of the technology stack, including infrastructure, machine learning tools, and purpose-built AI services, he added.

Looking at some of the applications and benefits of generative AI in the energy industry, Shel outlined that AWS sees the technology playing a pivotal role in increasing operational efficiencies, reducing health and safety exposure, enhancing customer experience, minimizing the emissions associated with energy production, and accelerating the energy transition.

For example, generative AI could play a pivotal role in addressing operational site safety, Shel said.

Energy operations often occur in remote, and sometimes hazardous and risky environments. The industry has long-sought solutions that help to reduce trips to the field, which directly correlates to reduced worker health and safety exposure, he added.

Generative AI can help the industry make significant strides towards this goal. Images from cameras stationed at field locations can be sent to a generative AI application that could scan for potential safety risks, such as faulty valves resulting in gas leaks, he continued.

Shel said the application could generate recommendations for personal protective equipment and tools and equipment for remedial work, highlighting that this would help to eliminate an initial trip to the field to identify issues, minimize operational downtime, and also reduce health and safety exposure.

Another example is reservoir modeling, Shel noted.

Generative AI models can be used for reservoir modeling by generating synthetic reservoir models that can simulate reservoir behavior, he added.

GANs are a popular generative AI technique used to generate synthetic reservoir models. The generator network of the GAN is trained to produce synthetic reservoir models that are similar to real-world reservoirs, while the discriminator network is trained to distinguish between real and synthetic reservoir models, he went on to state.

Once the generative model is trained, it can be used to generate a large number of synthetic reservoir models that can be used for reservoir simulation and optimization, reducing uncertainty and improving hydrocarbon production forecasting, Shel stated.

These reservoir models can also be used for other energy applications where subsurface understanding is critical, such as geothermal and carbon capture and storage, Shel said.

Highlighting a third example, Shel pointed out a generative AI based digital assistant.

Data access is a continuous challenge the energy industry is looking to overcome, especially considering much of its data is decades old and sits in various systems and formats, he said.

Oil and gas companies, for example, have decades of documents created throughout the subsurface workflow in different formats, i.e., PDFs, presentations, reports, memos, well logs, word documents, and finding useful information takes a considerable amount of time, he added.

According to one of the top five operators, engineers spend 60 percent of their time searching for information. Ingesting all of those documents on a generative AI based solution augmented by an index can dramatically improve data access, which can lead to making better decisions faster, Shel continued.

When asked if the thought all oil and gas companies will use generative AI in some way in the future, Shel said he did, but added that its important to stress that its still early days when it comes to defining the potential impact of generative AI on the energy industry.

At AWS, our goal is to democratize the use of generative AI, Shel told Rigzone.

To do this, were providing our customers and partners with the flexibility to choose the way they want to build with generative AI, such as building their own foundation models with purpose-built machine learning infrastructure; leveraging pre-trained foundation models as base models to build their applications; or use services with built-in generative AI without requiring any specific expertise in foundation models, he added.

Were also providing cost-efficient infrastructure and the correct security controls to help simplify deployment, he continued.

The AWS representative outlined that AI applied through machine learning will be one of the most transformational technologies of our generation, tackling some of humanitys most challenging problems, augmenting human performance, and maximizing productivity.

As such, responsible use of these technologies is key to fostering continued innovation, Shel outlined.

AWS took part in the Society of Petroleum Engineers (SPE) International Gulf Coast Sections recent Data Science Convention event in Houston, Texas, which was attended by Rigzones President. The event, which is described as the annual flagship event of the SPE-GCS Data Analytics Study Group, hosted representatives from the energy and technology sectors.

Last month, in a statement sent to Rigzone, GlobalData noted that machine learning has the potential to transform the oil and gas industry.

Machine learning is a rapidly growing field in the oil and gas industry, GlobalData said in the statement.

Overall, machine learning has the potential to improve efficiency, increase production, and reduce costs in the oil and gas industry, the company added.

In a report on machine learning in oil and gas published back in May, GlobalData highlighted several key players, including BP, ExxonMobil, Gazprom, Petronas, Rosneft, Saudi Aramco, Shell, and TotalEnergies.

Speaking to Rigzone earlier this month, Andy Wang, the Founder and Chief Executive Officer of data solutions company Prescient, said data science is the future of oil and gas.

Wang highlighted that data sciences includes many data tools, including machine learning, which he noted will be an important part of the future of the sector. When asked if he thought more and more oil companies would adopt data science, and machine learning, Wang responded positively on both counts.

Back in November 2022, OpenAI, which describes itself as an AI research and deployment company whose mission is to ensure that artificial general intelligence benefits all of humanity, introduced ChatGPT. In a statement posted on its website on November 30 last year, OpenAI said ChatGPT is a sibling model toInstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.

In April this year, Rigzone looked at how ChatGPT will affect oil and gas jobs. To view that article, click here.

To contact the author, emailandreas.exarheas@rigzone.com

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Generative AI Will Have Profound Impact Across Sectors - Rigzone News

Generative artificial intelligence (AI) has put AI in the hands of people, and those who dont use it could struggle to keep their jobs in future, Jaspreet Bindra, Founder and MD, Tech Whisperer Lt. UK, surmised at the Mint Digital Innovation Summit on June 9.

We never think about electricity until its not there. Thats how AI used to be. It was always in the background and we never thought about it. With generative AI it has come into our hands, and 200-300 million of us are like, wow! said Bindra.

He noted that while AI wont replace humans at their jobs, someone using AI very well could. He urged working professionals to recalibrate and embrace generative AI as a powerful tool created by humans, instead of looking at it as a threat.

There is a new kid in town, who can do a bunch of things that we can too, he said, adding that humans will just be able to do tasks better and will hence have to take advantage of their own ingenuity. 60% of jobs will be impacted, not as jobs themselves but as tasks, he said.

To be sure, Bindra said that he believes generative AI to be a transformative technology, just like Search or the Internet were. He said that the technology will also reshape big tech firms themselves. The reshaping of big tech has already started, and theres a new trillion-dollar boy in town called Nvidia. Youre going to see some shaping and reshaping of the apex of technology as we go forward.

However, he also acknowledged that Generative AI (GAI) is not the same as Artificial General Intelligence (AGI) a fear that many have expressed ever since ChatGPT became popular last year.

I believe that one day AI will become more intelligent than human beings in certain aspects. What I dont believe is that itll ever get conscious or sentient. We dont understand our own brain, or our own consciousness its the hard problem in philosophy - how can we build something that will be conscious?

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Mint DIS 2023 | AI won't replace you, someone using AI will ... - TechCircle

Artificial General Intelligence (AGI) is still a concept or, at most, at a nascent stage. Yet, there is already a lot of debate around it.

AGI and artificial intelligence (AI) are different. The latter performs specific activities, such as the Alexa assistant. But you know that Alexa is limited in its abilities.

AGI, in the meantime, can replace human beings with robots. It enables AI to emulate the cognitive powers of a human being. Think of a robot judge in a court presiding over a complex case.

Example of how AGI can be used in real life

Imagine a scenario where a patient with a tumor undergoes surgery. It is later revealed that a robot performed the operation. While the outcome may be successful, the patients family and friends are surprised and have reservations about trusting a robot with such a complex task. Surgery requires improvisation and decision-making, qualities we trust in human doctors.

The concept is both a scary and radical idea. The fears emanate from various ethical, social, and moral issues. A school of thought is against AGI because robots can be controlled to perform undesirable and unethical actions.

AGI is still in its infancy, and disagreements notwithstanding, it will be a long time before we see its manifestations. The base of AGI is the same as that of AI and Machine Learning (ML). Work is still in progress around the world, with the main focus remaining on a few areas discussed below.

Big data has significantly lowered the cost of data storage. Both AI and ML require large volumes of data. Big data and cloud storage have made data storage affordable, contributing to the development of AGI.

Scientists have made significant progress in both ML and Deep Learning (DL) technologies. Major developments have occurred in neural networks, reinforcement learning, and generative models.

Transfer learning hastens ML by applying existing knowledge to recognize similar objects. For example, a learning model learns to identify small birds based on their features, such as small wings, beaks, and eyes. Now, another learning model must identify various species of small birds in the Amazon rainforest. The latter model doesnt begin from scratch but inherits the learning from the earlier model, so the learning is expedited.

Its not that you will see or experience AGI in a new avatar that is unleashing changes in society from a point in time. The changes will be gradual and slowly yet steadily manifest in our day-to-day lives.

ChatGPT models have been developing at a breakneck speed with impressive capabilities. However, not everyone is fully convinced of the potential of AGI. Various countries and experts emphasize the importance of guiding ChatGPTs development within specific rules and regulations to ensure responsible progress toward AGI.

Response from Italy

In April 2023, Italy became the first nation to ban the development of ChatGPT over a breach of data and payment information. The government has also been probing whether the ChatGPT complies with the European Unions General Data Protection Regulation (GDPR) rules that protect confidential data inside and outside the EU.

Experts point out that there is no transparency in how ChatGPT is being developed. No information is publicly available about its development models, data, parameters, and version release plans.

OpenAIs brainchild continues to develop at a great speed, and we cant probably imagine the powers it has been accumulating. All without checks and balances. Some believe that ChatGPT 5 will mark the arrival of the AGI.

According to Anthony Aguirre, a Professor of Physics at UC Santa Cruz and the executive vice president of the Future of Life, said:The largest-scale computations are increasing the size by about 2.5 times per year. GPT-4s parameters were not disclosed by OpenAI, but there is no reason to think this trend has stopped or even slowed.

Aguirre, who was behind the famous open letter, added: Only the labs themselves know what computations they are running, but the trend is unmistakable.

The open letter signed by many industry stalwarts reflected the fear and apprehensions towards the uncontrolled development of ChatGPT.

The letter urges strongly to stop all developments of ChatGPT until a robust framework is established to control misinformation, hallucination, and bias in the system. Indeed, the so-called hallucination, inaccurate responses, and the bias exhibited by ChatGPT on many occasions are too glaring to ignore.

The open letter is signed by Steve Wozniak, among many other stalwarts, and already has 3,100 signatories that comprise software developers and engineers, CEOs, CFOs, technologists, psychologists, doctoral students, professors, medical doctors, and public school teachers.

The government has also been probing whether the ChatGPT complies with the European Unions General Data Protection Regulation (GDPR) rules that protect confidential data inside and outside the EU.

Its scary to think if a few wealthy and powerful nations can develop and concentrate AGI in their hands and use that to serve their benefits.

For example, they can control all the personal and sensitive data of other countries and communities, wreaking havoc.

AGI can become a veritable tool for biased actions and judgments. And, in the worst case, lead to sophisticated information warfare.

AGI is still in the conceptual stage, but given the lack of transparency and the perceived speed at which AI and ML have been progressing, it might not be too far when AGI is realized.

Its imperative that countries and corporates put their heads together and develop a robust framework that has enough checks and balances and guardrails.

The main goal of the framework would be to protect mankind and prevent unethical intrusions in their lives.

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Unleashing the Unknown: Fears Behind Artificial General ... - Techopedia