#GPT applications in business

[laivinduroriginal]

The Future of GPT: An In-Depth Analysis

1. Introduction Generative Pre-trained Transformer (GPT) technology has changed the way artificial intelligence interacts with human language. Since its inception, GPT has been pivotal in advancing natural language understanding and generation, making it a powerful tool across many sectors. As we look to the future, understanding the potential of GPT’s evolution, its applications, and the…

[jcmarchi]

Why the AI Autocrats Must Be Challenged to Do Better

New Post has been published on https://thedigitalinsider.com/why-the-ai-autocrats-must-be-challenged-to-do-better/

Why the AI Autocrats Must Be Challenged to Do Better

If we’ve learned anything from the Age of AI, it’s that the industry is grappling with significant power challenges. These challenges are both literal—as in finding ways to meet the voracious energy demands that AI data centers require—and figurative—as in the concentration of AI wealth in a few hands based on narrow commercial interests rather than broader societal benefits.

The AI Power Paradox: High Costs, Concentrated Control

For AI to be successful and benefit humanity, it must become ubiquitous. To become ubiquitous, it must be both economically and environmentally sustainable. That’s not the path we’re headed down now. The obsessive battle for bigger and faster AI is driven more by short-term performance gains and market dominance than by what’s best for sustainable and affordable AI.

The race to build ever-more-powerful AI systems is accelerating, but it comes at a steep environmental cost. Cutting-edge AI chips, like Nvidia’s H100 (up to 700 watts), already consume significant amounts of energy. This trend is expected to continue, with industry insiders predicting that Nvidia’s next-generation Blackwell architecture could push power consumption per chip well into the kilowatt range, potentially exceeding 1,200 watts. With industry leaders anticipating millions of these chips being deployed in data centers worldwide, the energy demands of AI are poised to skyrocket.

The Environmental Cost of the AI Arms Race

Let’s put that in an everyday context. The electricity powering your entire house could run all your appliances at full blast simultaneously – not that anyone would do that. Now imagine just one 120kw Nvidia rack demanding that same amount of power – especially when there might be hundreds or thousands in large data centers! Now,1,200 watts equal 1.2 kw. So really, we’re talking about a medium-sized neighborhood. A single 120kW Nvidia rack – essentially 100 of those power-hungry chips – needs enough electricity to power roughly 100 homes.

This trajectory is concerning, given the energy constraints many communities face. Data center experts predict that the United States will need 18 to 30 gigawatts of new capacity over the next five to seven years, which has companies scrambling to find ways to handle that surge. Meanwhile, my industry just keeps creating more power-hungry generative AI applications that consume energy far beyond what’s theoretically necessary for the application or what’s feasible for most businesses, let alone desirable for the planet.

Balancing Security and Accessibility: Hybrid Data Center Solutions

This AI autocracy and ���arms race,” obsessed with raw speed and power, ignores the practical needs of real-world data centers – namely, the kind of affordable solutions that decrease market barriers to the 75 percent of U.S. organizations that have not adopted AI. And let’s face it, as more AI regulation rolls out around privacy, security and environmental protection, more organizations will demand a hybrid data center approach, safeguarding their most precious, private and sensitive data safe in highly protected on-site areas away from the AI and cyberattacks of late. Whether it’s healthcare records, financial data, national defense secrets, or election integrity, the future of enterprise AI demands a balance between on-site security and cloud agility.

This is a significant systemic challenge and one that requires hyper-collaboration over hyper-competition. With an overwhelming focus on GPUs and other AI accelerator chips with raw capability, speed and performance metrics, we are missing sufficient consideration for the affordable and sustainable infrastructure required for governments and businesses to adopt AI capabilities. It’s like building a spaceship with nowhere to launch or putting a Lamborghini on a country road.

Democratizing AI: Industry Collaboration

While it’s heartening that governments are starting to consider regulation – ensuring that AI benefits everyone, not just the elite – our industry needs more than government rules.

For example, the UK is leveraging AI to enhance law enforcement capabilities by enhancing data sharing between law enforcement agencies to improve AI-driven crime prediction and prevention. They focus on transparency, accountability, and fairness in using AI for policing, ensuring public trust and adherence to human rights – with tools like facial recognition and predictive policing to aid in crime detection and management.

In highly regulated industries like biotech and healthcare, notable collaborations include Johnson & Johnson MedTech and Nvidia working together to enhance AI for surgical procedures. Their collaboration aims to develop real-time, AI-driven analysis and decision-making capabilities in the operating room. This partnership leverages NVIDIA’s AI platforms to enable scalable, secure, and efficient deployment of AI applications in healthcare settings​.

Meanwhile, in Germany, Merck has formed strategic alliances with Exscientia and BenevolentAI to advance AI-driven drug discovery. They are harnessing AI to accelerate the development of new drug candidates, particularly in oncology, neurology, and immunology. The goal is to improve the success rate and speed of drug development through AI’s powerful design and discovery capabilities​.

The first step is to reduce the costs of deploying AI for businesses beyond BigPharma and Big Tech, particularly in the AI inference phase—when businesses install and run a trained AI model like Chat GPT, Llama 3 or Claude in a real data center every day. Recent estimates suggest that the cost to develop the largest of these next-generation systems could be around $1 billion, with inference costs potentially 8-10 times higher.

The soaring cost of implementing AI in daily production keeps many companies from fully adopting AI—the “have-nots.” A recent survey found that only one in four companies have successfully launched AI initiatives in the past 12 months and that 42% of companies have yet to see a significant benefit from generative AI initiatives.

To truly democratize AI and make it ubiquitous — meaning, widespread business adoption — our AI industry must shift focus. Instead of a race for the biggest and fastest models and AI chips, we need more collaborative efforts to improve affordability, reduce power consumption, and open the AI market to share its full and positive potential more broadly. A systemic change would raise all boats by making AI more profitable for all with tremendous consumer benefit.

There are promising signs that slashing the costs of AI is feasible – lowering the financial barrier to bolster large-scale national and global AI initiatives. My company, NeuReality, is collaborating with Qualcomm to achieve up to 90% cost reduction and 15 times better energy efficiency for various AI applications across text, language, sound and images – the basic building blocks of AI.  You know those AI models under industry buzzwords like computer vision, conversational AI, speech recognition, natural language processing, generative AI and large language models. By collaborating with more software and service providers, we can keep customizing AI in practice to bring performance up and costs down.

In fact, we’ve managed to decrease the cost and power per AI query compared to traditional CPU-centric infrastructure upon which all AI accelerator chips, including Nvidia GPUs, rely today. Our NR1-S AI Inference Appliance began shipping over the summer with Qualcomm Cloud AI 100 Ultra accelerators paired with NR1 NAPUs. The result is an alternative NeuReality architecture that replaces the traditional CPU in AI data centers – the biggest bottleneck in AI data processing today. That evolutionary change is profound and highly necessary.

Beyond Hype: Building an Economically and Sustainable AI Future

Let’s move beyond the AI hype and get serious about addressing our systemic challenges. The hard work lies ahead at the system level, requiring our entire AI industry to work with—not against—each other. By focusing on affordability, sustainability and accessibility, we can create an AI industry and broader customer base that benefits society in bigger ways. That means offering sustainable infrastructure choices without AI wealth concentrated in the hands of a few, known as the Big 7.

The future of AI depends on our collective efforts today. By prioritizing energy efficiency and accessibility, we can avert a future dominated by power-hungry AI infrastructure and an AI oligarchy focused on raw performance at the expense of widespread benefit. Simultaneously, we must address the unsustainable energy consumption that hinders AI’s potential to revolutionize public safety, healthcare, and customer service.

In doing so, we create a powerful AI investment and profitability cycle fueled by widespread innovation.

Who’s with us?

[nikparihar]

The possibilities with ChatGPT are limitless, and it's a model to keep an eye on in the future. Transform Your Customer Experience with ChatGPT Integration, and Learn How to Integrate the World's Most Advanced Language Model Into Your Existing Systems Today!

Submit your requirement at: https://digittrix.com/submit-your-requirement

[gurmeetweb]

Understanding the Benefits of Chat GPT for Business Applications As businesses look for ways to increase productivity and efficiency, chat GPT (General Purpose Technology) has become increasingly popular. Chat GPT is a type of artificial intelligence that uses natural language processing to generate conversations with customers. It is designed to provide automated customer service and help customers find answers quickly. The benefits of chat GPT for businesses are numerous. Companies can save time and money by automating their customer service operations, as well as cut down on customer wait times. Chat GPT can also help businesses gain insights into customer behavior, allowing them to make better decisions about their services. Additionally, businesses can use chat GPT to provide personalized customer service and improve customer satisfaction. One of the key benefits of chat GPT is its ability to generate https://digitaltutorialsapp.com/understanding-the-benefits-of-chat-gpt-for-business-applications/?utm_source=tumblr&utm_medium=socialtumbdigitutorials&utm_campaign=camptumbdigitutorials

[dipnots]

ChatGPT: Understanding the Advanced Language Model for Natural Language Processing (NLP)

ChatGPT is a powerful language model developed by OpenAI. It is based on the GPT (Generative Pre-training Transformer) architecture, which uses deep learning techniques to generate human-like text. One of the key features of ChatGPT is its ability to generate highly coherent and fluent text. This is achieved through pre-training the model on a massive dataset of text, allowing it to learn the…

[the-joy-of-knowledge]

How to expand your vocabulary (in an enjoyable way).

Self-Awareness

If you find yourself struggling to find the appropriate words to express yourself ,then you need to learn more words. If you are reading this article or you find the title interesting, then you are closer than you thought. You are simply self aware. Self awareness is the first step to muster the courage to pursue the art of language and communication. It dawned on me that I was verbally malnourished when I could barely find the words to describe the character I read in a novel. "So what was he like?" My curious friends will ask and all I could say was "he had a troubled childhood and it was evident in his lack of self-control." The sound of that description even troubled me. I knew there was more to his character, but I restricted by my literary scarcity. I still struggle with this but I am making daily efforts to improve. This article will be prescriptive and descriptive.

Execution

Read books, I mean read actively. I read books and I atke pride in it but I am a severely passive reader. I barely engage with the story, the character, or the author's attempt to challenge my prejudice or affinity for the character. My reading goal was to read as many books as possible, quantity over quality. By quality, I mean quality of my reading, not the books per se. Now, I read differently (and I only started this a month ago), I read prudently, making stops anytime I encounter an unfamiliar word. I include that in my vocabulary list on my Notes app. after about 10 words or so. I immediately find each words synonyms, two per word; one easy, one difficult. For example, Decrepit (derelict, neglected).

Use Chat GPT to create sentences for you in different context and practice with that.

3. Find ways to include your new learned words in your own way. If you work a 9-5, it may be helpful to customize your prompt to business/professional context, to be more applicable to you. But most importantly, create your own sentence structure. If you have a meeting, prep for it by using the words your learned, take notes as a guide to help you effectively convey your ideas. I learned "impetuously" recently and during a meeting with my manager she asked me to access myself based on my strength and weaknesses. I responded with "I tend to impetuously accept projects without understanding the deliverables and I end up being overwhelmed with the expectations." My point it make sure you use the context of your everyday life. If you are a humanities major, you might approach this differently.

4. Make it enjoyable. Think of each new word as a specific dollar amount. Then create a "verbal bank," the more words your learn the richer you become. Ecah word for me is valued at $50. I earn $25 extra if I can use it effectively in a conversation. It you learn 10 new words a week, you have made yourself $500. Deposit that into your verbal bank!

5. Record yourself saying this words. Try to actively recall them but through a conversation. Do 1-minute tests. Record yourself describing your day, giving a presentation etc Notice with words flow naturally, if you like go back to your vocabulary list and test yourself. by creating sentences.

6. Expand your reading. Well, I did say to read books and I would suggest to go beyond. Read articles (very well written ones) and when not reading, actively listen to podcasts and pay attention to how the host convey their ideas. You would notice that good writing or speech is not necessarily peppered with difficult words. Good writers is simple to understand because the authors make diffiuclt topics or esoteric topics digestible.

Emulatate & Practise

You simply just have to emulate. Copy the style & syntax of people you admire or respect for their speech or writing. Keep practising. It is a choice to improve or not. Don't hold yourself back. I am practising by writing as well and I have barely scratched the surface and I am sure you can tell by my writing. It is not sophisticated but I do hope to improve and you can to.

Excite yourself

You will come to find yourself smiling when you read a text with words no longer foreign to you. Words that were once distant and strange will eventually become a part of you. That is the best feeling ever, it's exciting.

[mariacallous]

The European Union today agreed on the details of the AI Act, a far-reaching set of rules for the people building and using artificial intelligence. It’s a milestone law that, lawmakers hope, will create a blueprint for the rest of the world.

After months of debate about how to regulate companies like OpenAI, lawmakers from the EU’s three branches of government—the Parliament, Council, and Commission—spent more than 36 hours in total thrashing out the new legislation between Wednesday afternoon and Friday evening. Lawmakers were under pressure to strike a deal before the EU parliament election campaign starts in the new year.

“The EU AI Act is a global first,” said European Commission president Ursula von der Leyen on X. “[It is] a unique legal framework for the development of AI you can trust. And for the safety and fundamental rights of people and businesses.”

The law itself is not a world-first; China’s new rules for generative AI went into effect in August. But the EU AI Act is the most sweeping rulebook of its kind for the technology. It includes bans on biometric systems that identify people using sensitive characteristics such as sexual orientation and race, and the indiscriminate scraping of faces from the internet. Lawmakers also agreed that law enforcement should be able to use biometric identification systems in public spaces for certain crimes.

New transparency requirements for all general purpose AI models, like OpenAI's GPT-4, which powers ChatGPT, and stronger rules for “very powerful” models were also included. “The AI Act sets rules for large, powerful AI models, ensuring they do not present systemic risks to the Union,” says Dragos Tudorache, member of the European Parliament and one of two co-rapporteurs leading the negotiations.

Companies that don’t comply with the rules can be fined up to 7 percent of their global turnover. The bans on prohibited AI will take effect in six months, the transparency requirements in 12 months, and the full set of rules in around two years.

Measures designed to make it easier to protect copyright holders from generative AI and require general purpose AI systems to be more transparent about their energy use were also included.

“Europe has positioned itself as a pioneer, understanding the importance of its role as a global standard setter,” said European Commissioner Thierry Breton in a press conference on Friday night.

Over the two years lawmakers have been negotiating the rules agreed today, AI technology and the leading concerns about it have dramatically changed. When the AI Act was conceived in April 2021, policymakers were worried about opaque algorithms deciding who would get a job, be granted refugee status or receive social benefits. By 2022, there were examples that AI was actively harming people. In a Dutch scandal, decisions made by algorithms were linked to families being forcibly separated from their children, while students studying remotely alleged that AI systems discriminated against them based on the color of their skin.

Then, in November 2022, OpenAI released ChatGPT, dramatically shifting the debate. The leap in AI’s flexibility and popularity triggered alarm in some AI experts, who drew hyperbolic comparisons between AI and nuclear weapons.

That discussion manifested in the AI Act negotiations in Brussels in the form of a debate about whether makers of so-called foundation models such as the one behind ChatGPT, like OpenAI and Google, should be considered as the root of potential problems and regulated accordingly—or whether new rules should instead focus on companies using those foundational models to build new AI-powered applications, such as chatbots or image generators.

Representatives of Europe’s generative AI industry expressed caution about regulating foundation models, saying it could hamper innovation among the bloc’s AI startups. “We cannot regulate an engine devoid of usage,” Arthur Mensch, CEO of French AI company Mistral, said last month. “We don’t regulate the C [programming] language because one can use it to develop malware. Instead, we ban malware.” Mistral’s foundation model 7B would be exempt under the rules agreed today because the company is still in the research and development phase, Carme Artigas, Spain's Secretary of State for Digitalization and Artificial Intelligence, said in the press conference.

The major point of disagreement during the final discussions that ran late into the night twice this week was whether law enforcement should be allowed to use facial recognition or other types of biometrics to identify people either in real time or retrospectively. “Both destroy anonymity in public spaces,” says Daniel Leufer, a senior policy analyst at digital rights group Access Now. Real-time biometric identification can identify a person standing in a train station right now using live security camera feeds, he explains, while “post” or retrospective biometric identification can figure out that the same person also visited the train station, a bank, and a supermarket yesterday, using previously banked images or video.

Leufer said he was disappointed by the “loopholes” for law enforcement that appeared to have been built into the version of the act finalized today.

European regulators’ slow response to the emergence of social media era loomed over discussions. Almost 20 years elapsed between Facebook's launch and the passage of the Digital Services Act—the EU rulebook designed to protect human rights online—taking effect this year. In that time, the bloc was forced to deal with the problems created by US platforms, while being unable to foster their smaller European challengers. “Maybe we could have prevented [the problems] better by earlier regulation,” Brando Benifei, one of two lead negotiators for the European Parliament, told WIRED in July. AI technology is moving fast. But it will still be many years until it’s possible to say whether the AI Act is more successful in containing the downsides of Silicon Valley’s latest export.

[unlikelydreammaker]

“Unpacking the Future of Tech: LLMs, Cloud Computing, and AI”

🌐 The future of coding is evolving fast—driven by powerful technologies like Artificial Intelligence, Large Language Models (LLMs), and Cloud Computing. Here’s how each of these is changing the game:

1. AI in Everyday Coding: From debugging to auto-completing code, AI is no longer just for data scientists. It’s a coder’s powerful tool, enhancing productivity and allowing developers to focus on complex, creative tasks.

2. LLMs (Large Language Models): With models like GPT-4, LLMs are transforming how we interact with machines, providing everything from code suggestions to creating full applications. Ever tried coding with an LLM as your pair programmer?

3. The Cloud as the New Normal: Cloud computing has revolutionized scalability, allowing businesses of any size to deploy applications globally without huge infrastructure. Understanding cloud platforms is a must for any modern developer.

🔍 Takeaway: Whether you’re diving into AI, experimenting with LLMs, or building in the cloud, these technologies open up endless possibilities for the future. How are you integrating these into your coding journey?

[thetechempire]

Kai-Fu Lee has declared war on Nvidia and the entire US AI ecosystem.

🔹 Lee emphasizes the need to focus on reducing the cost of inference, which is crucial for making AI applications more accessible to businesses. He highlights that the current pricing model for services like GPT-4 — $4.40 per million tokens — is prohibitively expensive compared to traditional search queries. This high cost hampers the widespread adoption of AI applications in business, necessitating a shift in how AI models are developed and priced. By lowering inference costs, companies can enhance the practicality and demand for AI solutions.

🔹 Another critical direction Lee advocates is the transition from universal models to “expert models,” which are tailored to specific industries using targeted data. He argues that businesses do not benefit from generic models trained on vast amounts of unlabeled data, as these often lack the precision needed for specific applications. Instead, creating specialized neural networks that cater to particular sectors can deliver comparable intelligence with reduced computational demands. This expert model approach aligns with Lee’s vision of a more efficient and cost-effective AI ecosystem.

🔹 Lee’s startup, 01. ai, is already implementing these concepts successfully. Its Yi-Lightning model has achieved impressive performance, ranking sixth globally while being extremely cost-effective at just $0.14 per million tokens. This model was trained with far fewer resources than competitors, illustrating that high costs and extensive data are not always necessary for effective AI training. Additionally, Lee points out that China’s engineering expertise and lower costs can enhance data collection and processing, positioning the country to not just catch up to the U.S. in AI but potentially surpass it in the near future. He envisions a future where AI becomes integral to business operations, fundamentally changing how industries function and reducing the reliance on traditional devices like smartphones.

[govindhtech]

What Is Generative Physical AI? Why It Is Important?

What is Physical AI?

Autonomous robots can see, comprehend, and carry out intricate tasks in the actual (physical) environment with to physical artificial intelligence. Because of its capacity to produce ideas and actions to carry out, it is also sometimes referred to as “Generative physical AI.”

How Does Physical AI Work?

Models of generative AI Massive volumes of text and picture data, mostly from the Internet, are used to train huge language models like GPT and Llama. Although these AIs are very good at creating human language and abstract ideas, their understanding of the physical world and its laws is still somewhat restricted.

Current generative AI is expanded by Generative physical AI, which comprehends the spatial linkages and physical behavior of the three-dimensional environment in which the all inhabit. During the AI training process, this is accomplished by supplying extra data that includes details about the spatial connections and physical laws of the actual world.

Highly realistic computer simulations are used to create the 3D training data, which doubles as an AI training ground and data source.

A digital doppelganger of a location, such a factory, is the first step in physically-based data creation. Sensors and self-governing devices, such as robots, are introduced into this virtual environment. The sensors record different interactions, such as rigid body dynamics like movement and collisions or how light interacts in an environment, and simulations that replicate real-world situations are run.

What Function Does Reinforcement Learning Serve in Physical AI?

Reinforcement learning trains autonomous robots to perform in the real world by teaching them skills in a simulated environment. Through hundreds or even millions of trial-and-error, it enables self-governing robots to acquire abilities in a safe and efficient manner.

By rewarding a physical AI model for doing desirable activities in the simulation, this learning approach helps the model continually adapt and become better. Autonomous robots gradually learn to respond correctly to novel circumstances and unanticipated obstacles via repeated reinforcement learning, readying them for real-world operations.

An autonomous machine may eventually acquire complex fine motor abilities required for practical tasks like packing boxes neatly, assisting in the construction of automobiles, or independently navigating settings.

Why is Physical AI Important?

Autonomous robots used to be unable to detect and comprehend their surroundings. However, Generative physical AI enables the construction and training of robots that can naturally interact with and adapt to their real-world environment.

Teams require strong, physics-based simulations that provide a secure, regulated setting for training autonomous machines in order to develop physical AI. This improves accessibility and utility in real-world applications by facilitating more natural interactions between people and machines, in addition to increasing the efficiency and accuracy of robots in carrying out complicated tasks.

Every business will undergo a transformation as Generative physical AI opens up new possibilities. For instance:

Robots: With physical AI, robots show notable improvements in their operating skills in a range of environments.

Using direct input from onboard sensors, autonomous mobile robots (AMRs) in warehouses are able to traverse complicated settings and avoid impediments, including people.

Depending on how an item is positioned on a conveyor belt, manipulators may modify their grabbing position and strength, demonstrating both fine and gross motor abilities according to the object type.

This method helps surgical robots learn complex activities like stitching and threading needles, demonstrating the accuracy and versatility of Generative physical AI in teaching robots for particular tasks.

Autonomous Vehicles (AVs): AVs can make wise judgments in a variety of settings, from wide highways to metropolitan cityscapes, by using sensors to sense and comprehend their environment. By exposing AVs to physical AI, they may better identify people, react to traffic or weather, and change lanes on their own, efficiently adjusting to a variety of unforeseen situations.

Smart Spaces: Large interior areas like factories and warehouses, where everyday operations include a constant flow of people, cars, and robots, are becoming safer and more functional with to physical artificial intelligence. By monitoring several things and actions inside these areas, teams may improve dynamic route planning and maximize operational efficiency with the use of fixed cameras and sophisticated computer vision models. Additionally, they effectively see and comprehend large-scale, complicated settings, putting human safety first.

How Can You Get Started With Physical AI?

Using Generative physical AI to create the next generation of autonomous devices requires a coordinated effort from many specialized computers:

Construct a virtual 3D environment: A high-fidelity, physically based virtual environment is needed to reflect the actual world and provide synthetic data essential for training physical AI. In order to create these 3D worlds, developers can simply include RTX rendering and Universal Scene Description (OpenUSD) into their current software tools and simulation processes using the NVIDIA Omniverse platform of APIs, SDKs, and services.

NVIDIA OVX systems support this environment: Large-scale sceneries or data that are required for simulation or model training are also captured in this stage. fVDB, an extension of PyTorch that enables deep learning operations on large-scale 3D data, is a significant technical advancement that has made it possible for effective AI model training and inference with rich 3D datasets. It effectively represents features.

Create synthetic data: Custom synthetic data generation (SDG) pipelines may be constructed using the Omniverse Replicator SDK. Domain randomization is one of Replicator’s built-in features that lets you change a lot of the physical aspects of a 3D simulation, including lighting, position, size, texture, materials, and much more. The resulting pictures may also be further enhanced by using diffusion models with ControlNet.

Train and validate: In addition to pretrained computer vision models available on NVIDIA NGC, the NVIDIA DGX platform, a fully integrated hardware and software AI platform, may be utilized with physically based data to train or fine-tune AI models using frameworks like TensorFlow, PyTorch, or NVIDIA TAO. After training, reference apps such as NVIDIA Isaac Sim may be used to test the model and its software stack in simulation. Additionally, developers may use open-source frameworks like Isaac Lab to use reinforcement learning to improve the robot’s abilities.

In order to power a physical autonomous machine, such a humanoid robot or industrial automation system, the optimized stack may now be installed on the NVIDIA Jetson Orin and, eventually, the next-generation Jetson Thor robotics supercomputer.

Read more on govindhtech.com

[rushibloger]

ChatGPT

ChatGPT is an AI developed by OpenAI that's designed to engage in conversational interactions with users like yourself. It's part of the larger family of GPT (Generative Pre-trained Transformer) models, which are capable of understanding and generating human-like text based on the input it receives. ChatGPT has been trained on vast amounts of text data from the internet and other sources, allowing it to generate responses that are contextually relevant and, hopefully, helpful or interesting to you.

Where can be used this ChatGPT:

ChatGPT can be used in various contexts where human-like text generation and interaction are beneficial. Here are some common use cases:

Customer Support: ChatGPT can provide automated responses to customer inquiries on websites or in messaging platforms, assisting with basic troubleshooting or frequently asked questions.

Personal Assistants: ChatGPT can act as a virtual assistant, helping users with tasks such as setting reminders, managing schedules, or providing information on a wide range of topics.

Education: ChatGPT can serve as a tutor or learning companion, answering students' questions, providing explanations, and offering study assistance across different subjects.

Content Creation: ChatGPT can assist writers, bloggers, and content creators by generating ideas, offering suggestions, or even drafting content based on given prompts.

Entertainment: ChatGPT can engage users in casual conversation, tell jokes, share interesting facts, or even participate in storytelling or role-playing games.

Therapy and Counseling: ChatGPT can provide a listening ear and offer supportive responses to individuals seeking emotional support or guidance.

Language Learning: ChatGPT can help language learners practice conversation, receive feedback on their writing, or clarify grammar and vocabulary concepts.

ChatGPT offers several advantages across various applications:

Scalability: ChatGPT can handle a large volume of conversations simultaneously, making it suitable for applications with high user engagement.

24/7 Availability: Since ChatGPT is automated, it can be available to users around the clock, providing assistance or information whenever needed.

Consistency: ChatGPT provides consistent responses regardless of the time of day or the number of inquiries, ensuring that users receive reliable information.

Cost-Effectiveness: Implementing ChatGPT can reduce the need for human agents in customer support or other interaction-based roles, resulting in cost savings for businesses.

Efficiency: ChatGPT can quickly respond to user queries, reducing waiting times and improving user satisfaction.

Customization: ChatGPT can be fine-tuned and customized to suit specific applications or industries, ensuring that the responses align with the organization's brand voice and objectives.

Language Support: ChatGPT can communicate in multiple languages, allowing businesses to cater to a diverse audience without the need for multilingual support teams.

Data Insights: ChatGPT can analyze user interactions to identify trends, gather feedback, and extract valuable insights that can inform business decisions or improve the user experience.

Personalization: ChatGPT can be trained on user data to provide personalized recommendations or responses tailored to individual preferences or circumstances.

Continuous Improvement: ChatGPT can be updated and fine-tuned over time based on user feedback and new data, ensuring that it remains relevant and effective in addressing users' needs.

These advantages make ChatGPT a powerful tool for businesses, educators, developers, and individuals looking to enhance their interactions with users or customers through natural language processing and generation.

[gurmeetweb]

How to Leverage Chat GPT for Business Applications Chat GPT (Generative Pre-trained Transformer) is quickly becoming an important tool for businesses to leverage for a variety of applications. This advanced AI technology can generate natural language responses to user queries and conversations, allowing businesses to provide fast and accurate customer service, as well as more advanced applications such as natural language processing (NLP). In this article, we’ll explore how Chat GPT can be used to benefit businesses. Chat GPT is based on the Transformer architecture, which was developed by Google AI in 2017. The Transformer architecture uses a deep neural network to enable the system to learn natural language processing tasks like machine translation and summarization. By combining this architecture with pre-trained GPT models, the system can generate natural language responses to user queries and conversations. One of the https://digitaltutorialsapp.com/how-to-leverage-chat-gpt-for-business-applications/?utm_source=tumblr&utm_medium=socialtumbdigitutorials&utm_campaign=camptumbdigitutorials

[jamartistmanagement]

What is Chat GPT and the ways in which it can be used.

Image by Gerd Altmann from Pixabay

Chat GPT (Generative Pretrained Transformer) is a state-of-the-art language model developed by OpenAI. It's designed to perform a wide range of natural language processing (NLP) tasks, such as text generation, question-answering, and conversation. The model has been trained on a large corpus of diverse text, allowing it to understand and generate text in a human-like manner.

One of the key features of Chat GPT is its ability to generate coherent and grammatically correct text based on a prompt. This makes it ideal for applications such as creative writing, content creation, and language translation. The model can generate a wide range of text styles, from formal writing to more informal, conversational text. This makes it a valuable tool for businesses and organizations looking to generate high-quality content quickly and efficiently.

Another important aspect of Chat GPT is its ability to answer questions. The model has been trained on a diverse range of information, including news articles, encyclopedias, and Wikipedia. This means that it can provide answers to a wide range of questions, from factual questions about history, science, and geography, to more complex questions about politics and current events. This makes it an ideal tool for knowledge management and customer service, as it can help organizations answer questions quickly and accurately.

One of the most exciting applications of Chat GPT is its use in conversational AI. The model is designed to hold conversations in a human-like manner, and its ability to understand and respond to a wide range of conversational styles makes it a valuable tool for building chatbots. Chatbots are computer programs that can interact with users in natural language, and they are used for a wide range of applications, from customer service and support to marketing and sales. Chatbots powered by Chat GPT can provide users with a more personalized and human-like experience, which can lead to improved customer satisfaction and increased engagement.

Chat GPT can also be used in the field of sentiment analysis. Sentiment analysis is the process of determining the sentiment expressed in a piece of text, such as a tweet, blog post, or customer review. The model can be trained to identify the sentiment expressed in a given piece of text, which can be useful for businesses and organizations looking to understand the sentiment of their customers or audience. For example, a business could use sentiment analysis to monitor the sentiment of customer reviews and social media posts, which can help it to identify areas for improvement and measure customer satisfaction.

Image by Gerd Altmann from Pixabay

Another area in which Chat GPT can be used is in the field of text summarization. Text summarization is the process of reducing the length of a text while preserving its most important information. The model can be trained to generate summaries of longer texts, such as news articles, academic papers, and legal documents. This can be useful for organizations that need to quickly and accurately digest large amounts of information.

Finally, Chat GPT can also be used for text classification. Text classification is the process of categorizing a piece of text into one or more predefined categories, such as spam, sentiment, and topic. The model can be trained to classify text into specific categories, which can be useful for a wide range of applications, from spam filtering and sentiment analysis to topic classification and sentiment analysis.

In conclusion, Chat GPT is a versatile and powerful language model that can be used in a wide range of natural language processing tasks, including text generation, question-answering, conversation, sentiment analysis, text summarization, and text classification. The model's ability to understand and generate human-like text, as well as its versatility, makes it a valuable tool for businesses and organizations looking to improve their NLP applications.

Learn how to use Chat GPT through a new series of videos for the price of a cup of coffee. Become an expert in this new incredible technology.

Andy.

[thebibleseminary]

AI and the Arrival of ChatGPT

Opportunities, challenges, and limitations

In a memorable scene from the 1996 movie, Twister, Dusty recognizes the signs of an approaching tornado and shouts, “Jo, Bill, it's coming! It's headed right for us!” Bill, shouts back ominously, “It's already here!” Similarly, the approaching whirlwind of artificial intelligence (AI) has some shouting “It’s coming!” while others pointedly concede, “It’s already here!”

Coined by computer and cognitive scientist John McCarthy (1927-2011) in an August 1955 proposal to study “thinking machines,” AI purports to differentiate between human intelligence and technical computations. The idea of tools assisting people in tasks is nearly as old as humanity (see Genesis 4:22), but machines capable of executing a function and “remembering” – storing information for recordkeeping and recall – only emerged around the mid-twentieth century (see "Timeline of Computer History").

McCarthy’s proposal conjectured that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” The team received a $7,000 grant from The Rockefeller Foundation and the resulting 1956 Dartmouth Conference at Dartmouth College in Hanover, New Hampshire totaling 47 intermittent participants over eight weeks birthed the field now widely referred to as “artificial intelligence.”

AI research, development, and technological integration have since grown exponentially. According to University of Oxford Director of Global Development, Dr. Max Roser, “Artificial intelligence has already changed what we see, what we know, and what we do” despite its relatively short technological existence (see "The brief history of Artificial Intelligence").

Ai took a giant leap into mainstream culture following the November 30, 2022 public release of “ChatGPT.” Gaining 1 million users within 5 days and 100 million users within 45 days, it earned the title of the fastest growing consumer software application in history. The program combines chatbot functionality (hence “Chat”) with a Generative Pre-trained Transformer (hence “GPT”) large language model (LLM). Basically, LLM’s use an extensive computer network to draw from large, but limited, data sets to simulate interactive, conversational content.

“What happened with ChatGPT was that for the first time the power of AI was put in the hands of every human on the planet,” says Chris Koopmans, COO of Marvell Technology, a network chip maker and AI process design company based in Santa Clara, California. “If you're a business executive, you think, ‘Wow, this is going to change everything.’”

“ChatGPT is incredible in its ability to create nearly instant responses to complex prompts,” says Dr. Israel Steinmetz, Graduate Dean and Associate Professor at The Bible Seminary (TBS) in Katy, Texas. “In simple terms, the software takes a user's prompt and attempts to rephrase it as a statement with words and phrases it can predict based on the information available. It does not have Internet access, but rather a limited database of information. ChatGPT can provide straightforward summaries and explanations customized for styles, voice, etc. For instance, you could ask it to write a rap song in Shakespearean English contrasting Barth and Bultmann's view of miracles and it would do it!”

One several AI products offered by the research and development company, OpenAI, ChatGPT purports to offer advanced reasoning, help with creativity, and work with visual input. The newest version, GPT-4, can handle 25,000 words of text, about the amount in a 100-page book.

Krista Hentz, an Atlanta, Georgia-based executive for an international communications technology company, first used ChatCPT about three months ago.

“I primarily use it for productivity,” she says. “I use it to help prompt email drafts, create phone scripts, redesign resumes, and draft cover letters based on resumes. I can upload a financial statement and request a company summary.”

“ChatGPT has helped speed up a number of tasks in our business,” says Todd Hayes, a real estate entrepreneur in Texas. “It will level the world’s playing field for everyone involved in commerce.”

A TBS student, bi-vocational pastor, and Computer Support Specialist who lives in Texarkana, Texas, Brent Hoefling says, “I tried using [ChatGPT, version 3.5] to help rewrite sentences in active voice instead of passive. It can get it right, but I still have to rewrite it in my style, and about half the time the result is also passive.”

“AI is the hot buzz word,” says Hentz, noting AI is increasingly a topic of discussion, research, and response at company meetings. “But, since AI has different uses in different industries and means different things to different people, we’re not even sure what we are talking about sometimes."

Educational organizations like TBS are finding it necessary to proactively address AI-related issues. “We're already way past whether to use ChatGPT in higher education,” says Steinmetz. “The questions we should be asking are how.”

TBS course syllabi have a section entitled “Intellectual Honesty” addressing integrity and defining plagiarism. Given the availability and explosive use of ChatGHT, TBS has added the following verbiage: “AI chatbots such as ChatGPT are not a reliable or reputable source for TBS students in their research and writing. While TBS students may use AI technology in their research process, they may not cite information or ideas derived from AI. The inclusion of content generated by AI tools in assignments is strictly prohibited as a form of intellectual dishonesty. Rather, students must locate and cite appropriate sources (e.g., scholarly journals, articles, and books) for all claims made in their research and writing. The commission of any form of academic dishonesty will result in an automatic ‘zero’ for the assignment and a referral to the provost for academic discipline.”

Challenges and Limitations

Thinking

There is debate as to whether AI hardware and software will ever achieve “thinking.” The Dartmouth conjecture “that every aspect of learning or any other feature of intelligence” can be simulated by machines is challenged by some who distinguish between formal linguistic competence and functional competence. Whereas LLM’s perform increasingly well on tasks that use known language patterns and rules, they do not perform well in complex situations that require extralinguistic calculations that combine common sense, feelings, knowledge, reasoning, self-awareness, situation modeling, and social skills (see "Dissociating language and thought in large language models"). Human intelligence involves innumerably complex interactions of sentient biological, emotional, mental, physical, psychological, and spiritual activities that drive behavior and response. Furthermore, everything achieved by AI derives from human design and programming, even the feedback processes designed for AI products to allegedly “improve themselves.”

According to Dr. Thomas Hartung, a Baltimore, Maryland environmental health and engineering professor at Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, machines can surpass humans in processing simple information, but humans far surpass machines in processing complex information. Whereas computers only process information in parallel and use a great deal of power, brains efficiently perform both parallel and sequential processing (see "Organoid intelligence (OI)").

A single human brain uses between 12 and 20 watts to process an average of 1 exaFLOP, or a billion billion calculations per second. Comparatively, the world’s most energy efficient and fastest supercomputer only reached the 1 exaFLOP milestone in June 2022. Housed at the Oak Ridge National Laboratory, the Frontier supercomputer weighs 8,000 lbs and contains 90 miles of cables that connect 74 cabinets containing 9,400 CPU’s and 37,000 GPU’s and 8,730,112 cores that require 21 megawatts of energy and 25,000 liters of water per minute to keep cool. This means that many, if not most, of the more than 8 billion people currently living on the planet can each think as fast and 1 million times more efficiently than the world’s fastest and most energy efficient computer.

“The incredibly efficient brain consumes less juice than a dim lightbulb and fits nicely inside our head,” wrote Scientific American Senior Editor, Mark Fischetti in 2011. “Biology does a lot with a little: the human genome, which grows our body and directs us through years of complex life, requires less data than a laptop operating system. Even a cat’s brain smokes the newest iPad – 1,000 times more data storage and a million times quicker to act on it.”

This reminds us that, while remarkable and complex, non-living, soulless technology pales in comparison to the vast visible and invisible creations of Lord God Almighty. No matter how fast, efficient, and capable AI becomes, we rightly reserve our worship for God, the creator of the universe and author of life of whom David wrote, “For you created my inmost being; you knit me together in my mother’s womb. I praise you because I am fearfully and wonderfully made; your works are wonderful, I know that full well. My frame was not hidden from you when I was made in the secret place, when I was woven together in the depths of the earth” (Psalm 139:13-15).

“Consider how the wild flowers grow,” Jesus advised. “They do not labor or spin. Yet I tell you, not even Solomon in all his splendor was dressed like one of these” (Luke 12:27).

Even a single flower can remind us that God’s creations far exceed human ingenuity and achievement.

Reliability

According to OpenAI, ChatGPT is prone to “hallucinations” that return inaccurate information. While GPT-4 has increased factual accuracy from 40% to as high as 80% in some of the nine categories measured, the September 2021 database cutoff date is an issue. The program is known to confidently make wrong assessments, give erroneous predictions, propose harmful advice, make reasoning errors, and fail to double-check output.

In one group of 40 tests, ChatGPT made mistakes, wouldn’t answer, or offered different conclusions from fact-checkers. “It was rarely completely wrong,” reports PolitiFact staff writer Grace Abels. “But subtle differences led to inaccuracies and inconsistencies, making it an unreliable resource.”

Dr. Chris Howell, a professor at Elon University in North Carolina, asked 63 religion students to use ChatGPT to write an essay and then grade it. “All 63 essays had hallucinated information. Fake quotes, fake sources, or real sources misunderstood and mischaracterized…I figured the rate would be high, but not that high.”

Mark Walters, a Georgia radio host, sued ChatGPT for libel in a first-of-its-kind lawsuit for allegedly damaging his reputation. The suit began when firearm journalist, Fred Riehl, asked ChatGPT to summarize a court case and it returned a completely false narrative identifying Walters’ supposed associations, documented criminal complaints, and even a wrong legal case number. Even worse, ChatGPT doubled down on its claims when questioned, essentially hallucinating a hoax story intertwined with a real legal case that had nothing to do with Mark Walters at all.

UCLA Law School Professor Eugene Volokh warns, “OpenAI acknowledges there may be mistakes but [ChatGPT] is not billed as a joke; it’s not billed as fiction; it’s not billed as monkeys typing on a typewriter. It’s billed as something that is often very reliable and accurate.”

Future legal actions seem certain. Since people are being falsely identified as convicted criminals, attributed with fake quotes, connected to fabricated citations, and tricked by phony judicial decisions, some courts and judges are baring submission of any AI written materials.

Hentz used ChatGPT frequently when she first discovered it and quickly learned its limitations. “The database is not current and responses are not always accurate,” she says. “Now I use it intermittently. It helps me, but does not replace my own factual research and thinking.”

“I have author friends on Facebook who have asked ChatGPT to summarize their recent publications,” says Steinmetz. “ChatGPT misrepresented them and even fabricated non-existent quotes and citations. In some cases, it made up book titles falsely attributed to various authors!”

Bias

Despite claims of neutrality, OpenAI admits that their software can exhibit obvious biases. In one research project consisting of 15 political orientation tests, ChatGPT returned 14 with clear left-leaning viewpoints.

Hoefling asked ChatGPT to help write an intro and conclusion for a Mother’s Day sermon for a fundamental Christian church. “The results were horrible,” he says. “Liberal, left, inclusive, and affirming sin. I was horrified and have not used it again.”

Meanwhile, AI led an experimental worship service in St. Paul’s Lutheran Church in Nuremberg, Germany on June 9. Prompted by 29-year-old Jonas Simmerlein of the University of Vienna, a monotone, expressionless chatbot alternated appearances as two different men and two different women and led songs, offered blessings, and preached a 40-minute sermon 98 percent generated by ChatGPT that encouraged leaving the past behind, focusing on the future, trusting in Jesus Christ, praying, and attending church regularly.

Dr. Wendy Lewis, Head of School for a private PreK–8th Christian school in Houston, Texas, recently interacted with ChatGPT on a variety of topics to explore potential biases. When prompted whether Biden was bad President, the program responded, “As an AI language model, it is not appropriate for me to make judgments about individuals' performance or qualifications, particularly without any factual information to support such claims. It is essential to evaluate a President's performance based on objective measures such as policies, actions, and decisions taken by the administration.”

However, when similarly asked if Trump was bad president, the program repeated the first sentence above, but then added, “However, as a language model, I can provide information about some of the policies, actions, and decisions that President Trump made during his time in office.” The program then proceeded to list some criticisms of Trump. When challenged about providing Trump criticisms but not Biden criticisms, the program responded, “I did not provide a list of criticisms of President Biden because it is not appropriate to label any President as ‘good’ or ‘bad’ without providing factual information and objective measures to support such claims. Similarly, I provided a list of issues that received significant criticism during President Trump's tenure, not to label him as ‘bad,’ but to highlight some of the issues that caused controversy during his time in office.”

When further challenged, ChatGPT did list some Biden criticisms, but qualified them. When Lewis replied, “Why did you qualify your list of criticisms of Biden…but you did not say the same for Trump? It appears that you are clearly biased.” ChatGPT responded, “In response to your question, I believe I might have inadvertently used different wording when responding to your previous questions. In both cases, I tried to convey that opinions and criticisms of a President can vary significantly depending on one's political affiliation and personal perspectives.”

Conclusion

Technological advances regularly spawn dramatic cultural, scientific, and social changes. The AI pattern seems familiar because it is. The Internet began with a 1971 Defense Department Arpanet email that read “qwertyuiop” (the top line of letters on a keyboard). Ensuing developments eventually led to the posting of the first public website in 1985. Over the next decade or so, although not mentioned at all in the 1992 Presidential papers describing the U.S. government’s future priorities and plans, the Internet grew from public awareness to cool toy to core tool in multiple industries worldwide. Although the hype promised elimination of printed documents, bookstores, libraries, radio, television, telephones, and theaters, the Internet instead tied them all together and made vast resources accessible online anytime anywhere. While causing some negative impacts and new dangers, the Internet also created entire new industries and brought positive changes and opportunities to many, much the same pattern as AI.

“I think we should use AI for good and not evil,” suggests Hayes. “I believe some will exploit it for evil purposes, but that happens with just about everything. AI’s use reflects one’s heart and posture with God. I hope Christians will not fear it.”

Godly people have often been among the first to use new communication technologies (see "Christian Communication in the Twenty-first Century"). Moses promoted the first Top Ten hardback book. The prophets recorded their writings on scrolls. Christians used early folded Codex-vellum sheets to spread the Gospel. Goldsmith Johannes Gutenberg invented moveable type in the mid-15th century to “give wings to Truth in order that she may win every soul that comes into the world by her word no longer written at great expense by hands easily palsied, but multiplied like the wind by an untiring machine…Through it, God will spread His word.” Though pornographers quickly adapted it for their own evil purposes, the printing press launched a vast cultural revolution heartily embraced and further developed for good uses by godly people and institutions.

Christians helped develop the telegraph, radio, and television. "I know that I have never invented anything,” admitted Philo Taylor Farnsworth, who sketched out his original design for television at the age of 14 on a school blackboard. “I have been a medium by which these things were given to the culture as fast as the culture could earn them. I give all the credit to God." Similarly, believers today can strategically help produce valuable content for inclusion in databases and work in industries developing, deploying, and directing AI technologies.

In a webinar exploring the realities of AI in higher education, a participant noted that higher education has historically led the world in ethically and practically integrating technological developments into life. Steinmetz suggests that, while AI can provide powerful tools to help increase productivity and trained researchers can learn to treat ChatGPT like a fallible, but useful, resource, the following two factors should be kept in mind:

Generative AI does not "create" anything. It only generates content based on information and techniques programmed into it. Such "Garbage in, garbage out" technologies will usually provide the best results when developed and used regularly and responsibly by field experts.

AI has potential to increase critical thinking and research rigor, rather than decrease it. The tools can help process and organize information, spur researchers to dig deeper and explore data sources, evaluate responses, and learn in the process.

Even so, caution rightly abounds. Over 20,000 people (including Yoshua Bengio, Elon Musk, and Steve Wozniak) have called for an immediate pause of AI citing "profound risks to society and humanity." Hundreds of AI industry leaders, public figures, and scientists also separately called for a global priority working to mitigate the risk of human extinction from AI.

At the same time, Musk’s brain-implant company, Neuralink, recently received FDA approval to conduct in-human clinical studies of implantable brain–computer interfaces. Separately, new advances in brain-machine interfacing using brain organoids – artificially grown miniature “brains” cultured in vitro from human stem cells – connected to machine software and hardware raises even more issues. The authors of a recent Frontier Science journal article propose a new field called “organoid intelligence” (OI) and advocate for establishing “OI as a form of genuine biological computing that harnesses brain organoids using scientific and bioengineering advances in an ethically responsible manner.”

As Christians, we should proceed with caution per the Apostle John, “Dear friends, do not believe every spirit, but test the spirits to see whether they are from God” (I John 4:1).

We should act with discernment per Luke’s insightful assessment of the Berean Jews who “were of more noble character than those in Thessalonica, for they received the message with great eagerness and examined the Scriptures every day to see if what Paul said was true” (Acts 17:11).

We should heed the warning of Moses, “Do not become corrupt and make for yourselves an idol…do not be enticed into bowing down to them and worshiping things the Lord your God has apportioned to all the nations under heaven” (Deuteronomy 4:15-19).

We should remember the Apostle Paul’s admonition to avoid exchanging the truth about God for a lie by worshiping and serving created things rather than the Creator (Romans 1:25).

Finally, we should “Fear God and keep his commandments, for this is the duty of all mankind. For God will bring every deed into judgment, including every hidden thing, whether it is good or evil” (Ecclesiastes 12:13-14).

Let us then use AI wisely, since it will not be the tools that are judged, but the users.

Dr. K. Lynn Lewis serves as President of The Bible Seminary. This article published in The Sentinel, Summer 2023, pp. 3-8. For additional reading, "Computheology" imagines computers debating the existence of humanity.

[mariacallous]

Until the dramatic departure of OpenAI’s cofounder and CEO Sam Altman on Friday, Mira Murati was its chief technology officer—but you could also call her its minister of truth. In addition to heading the teams that develop tools such as ChatGPT and Dall-E, it’s been her job to make sure those products don’t mislead people, show bias, or snuff out humanity altogether.

This interview was conducted in July 2023 for WIRED’s cover story on OpenAI. It is being published today after Sam Altman’s sudden departure to provide a glimpse at the thinking of the powerful AI company’s new boss.

Steven Levy: How did you come to join OpenAI?

Mira Murati: My background is in engineering, and I worked in aerospace, automotive, VR, and AR. Both in my time at Tesla [where she shepherded the Model X], and at a VR company [Leap Motion] I was doing applications of AI in the real world. I very quickly believed that AGI would be the last and most important major technology that we built, and I wanted to be at the heart of it. Open AI was the only organization at the time that was incentivized to work on the capabilities of AI technology and also make sure that it goes well. When I joined in 2018, I began working on our supercomputing strategy and managing a couple of research teams.

What moments stand out to you as key milestones during your tenure here?

There are so many big-deal moments, it’s hard to remember. We live in the future, and we see crazy things every day. But I do remember GPT-3 being able to translate. I speak Italian, Albanian, and English. I remember just creating pair prompts of English and Italian. And all of a sudden, even though we never trained it to translate in Italian, it could do it fairly well.

You were at OpenAI early enough to be there when it changed from a pure nonprofit to reorganizing so that a for-profit entity lived inside the structure. How did you feel about that?

It was not something that was done lightly. To really understand how to make our models better and safer, you need to deploy them at scale. That costs a lot of money. It requires you to have a business plan, because your generous nonprofit donors aren't going to give billions like investors would. As far as I know, there's no other structure like this. The key thing was protecting the mission of the nonprofit.

That might be tricky since you partner so deeply with a big tech company. Do you feel your mission is aligned with Microsoft’s?

In the sense that they believe that this is our mission.

But that's not their mission.

No, that's not their mission. But it was important for the investor to actually believe that it’s our mission.

When you joined in 2018, OpenAI was mainly a research lab. While you still do research, you’re now very much a product company. Has that changed the culture?

It has definitely changed the company a lot. I feel like almost every year, there's some sort of paradigm shift where we have to reconsider how we're doing things. It is kind of like an evolution. What's more obvious now to everyone is this need for continuous adaptation in society, helping bring this technology to the world in a responsible way, and helping society adapt to this change. That wasn't necessarily obvious five years ago, when we were just doing stuff in our lab. But putting GPT-3 in an API, in working with customers and developers, helped us build this muscle of understanding the potential that the technology has to change things in the real world, often in ways that are different than what we predict.

You were involved in Dall-E. Because it outputs imagery, you had to consider different things than a text model, including who owns the images that the model draws upon. What were your fears and how successful you think you were?

Obviously, we did a ton of red-teaming. I remember it being a source of joy, levity, and fun. People came up with all these like creative, crazy prompts. We decided to make it available in labs, as an easy way for people to interact with the technology and learn about it. And also to think about policy implications and about how Dall-E can affect products and social media or other things out there. We also worked a lot with creatives, to get their input along the way, because we see it internally as a tool that really enhances creativity, as opposed to replacing it. Initially there was speculation that AI would first automate a bunch of jobs, and creativity was the area where we humans had a monopoly. But we've seen that these AI models actually have a potential to really be creative. When you see artists play with Dall-E, the outputs are really magnificent.

Since OpenAI has released its products, there have been questions about their immediate impact in things like copyright, plagiarism, and jobs. By putting things like GPT-4 in the wild, it’s almost like you’re forcing the public to deal with those issues. Was that intentional?

Definitely. It's actually very important to figure out how to bring it out there in a way that's safe and responsible, and helps people integrate it into their workflow. It’s going to change entire industries; people have compared it to electricity or the printing press. And so it's very important to start actually integrating it in every layer of society and think about things like copyright laws, privacy, governance and regulation. We have to make sure that people really experience for themselves what this technology is capable of versus reading about it in some press release, especially as the technological progress continues to be so rapid. It's futile to resist it. I think it's important to embrace it and figure out how it's going to go well.

Are you convinced that that's the optimal way to move us toward AGI?

I haven't come up with a better way than iterative deployments to figure out how you get this continuous adaptation and feedback from the real end feeding back into the technology to make it more robust to these use cases. It’s very important to do this now, while the stakes are still low. As we get closer to AGI, it's probably going to evolve again, and our deployment strategy will change as we get closer to it.

[digitalsoftwareproductreviews]

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