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In today’s digital age, artificial intelligence (AI) has revolutionized various aspects of our lives, including productivity tools. Notion AI and ChatGPT are two prominent AI-powered tools that aim to enhance productivity for businesses, students, and professionals. While both tools share some similarities, they also have distinct differences that set them apart. This article will delve into the key similarities and differences between Notion AI and ChatGPT, shedding light on their respective features, use cases, and more.

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Both Notion AI and ChatGPT leverage the power of AI to enhance workflow and productivity. These tools are designed to assist users in organizing their tasks, improving efficiency, and streamlining their work processes.

Both tools can be effectively utilized in customer service and educational contexts. They can assist in automating customer interactions, providing instant responses, and offering educational content to users.

Notion AI boasts a versatile range of use cases compared to ChatGPT. While ChatGPT is primarily employed for customer service, education, and research purposes, Notion AI extends its capabilities to encompass tasks such as organizing work, creating documents, and collaborating with others.

The training data sets used by Notion AI and ChatGPT differ in scale and scope. Notion AI is trained on an extensive dataset comprising text and code, enabling it to generate highly accurate and informative text. In contrast, ChatGPT is trained on a comparatively smaller dataset of text.

As of now, ChatGPT is available for free usage, providing accessibility to a wider user base. On the other hand, Notion AI requires a subscription, making it a premium tool that offers additional features and functionalities.

Notion AI and ChatGPT differ slightly in terms of stability. While both tools are generally reliable, users may find variations in performance and results due to the complexities of AI algorithms.

Notion AI offers a high level of customization, allowing users to tailor the tool according to their specific requirements. In contrast, ChatGPT has limited customization options, providing a more standardized experience.

Also Read : ChatGPT vs. Chad GPT: Avoiding the Common Typo that Unleashes Chad into the AI World

ChatGPT is often considered more user-friendly, with a simpler interface and intuitive interactions. Notion AI, on the other hand, may have a steeper learning curve due to its extensive range of features and functionalities.

In conclusion, ChatGPT and Notion AI are powerful AI-driven tools that cater to distinct needs. ChatGPT excels in generating text and answering queries, making it a versatile solution for a wide range of applications. On the other hand, Notion AI offers a streamlined experience for organizing work, creating documents, and collaborating with others. Ultimately, the choice between these two tools depends on the specific requirements and preferences of the user.

As technology continues to evolve, we can expect even more sophisticated AI tools that will redefine the way we work and interact with digital assistants. The potential for AI-powered solutions is vast, and Notion AI and ChatGPT are just the beginning of what is yet to come.

Q: Is Notion AI suitable for personal use, or is it mainly for businesses?

A: Notion AI caters to both personal and business users. It offers a versatile set of features that can be beneficial for individuals, teams, and organizations.

Q: Can ChatGPT generate code snippets or assist in programming tasks?

A: ChatGPT can generate code snippets and provide basic programming assistance. However, its capabilities in this area are more limited compared to Notion AI, which is trained on a larger dataset of text and code.

Q: Are there any additional costs associated with using Notion AI?

A: Yes, Notion AI requires a subscription for full access to its features. The subscription fee varies based on the chosen plan.

Q: Can Notion AI and ChatGPT be integrated with other applications and platforms?

A: Both tools offer integrations with various applications and platforms, allowing users to enhance their workflows and leverage the power of AI in conjunction with other tools.

Q: How often are Notion AI and ChatGPT updated with new features and improvements?

A: Notion AI and ChatGPT are continuously updated by their respective developers to introduce new features, enhancements, and bug fixes. Users can expect regular updates to improve the overall user experience.

Chatgpt Stock Symbol: Investing In The Future Of Ai

See More: How To Use ChatGPT Prompts For Writing Cover Letter?

Investing in Microsoft (NASDAQ: MSFT) is one of the indirect ways to gain exposure to ChatGPT. Microsoft has a strategic partnership with OpenAI, the organization behind ChatGPT. Microsoft has taken a massive position in OpenAI, which makes it a compelling investment option for those interested in the potential of ChatGPT. As one of the leading technology companies globally, Microsoft’s involvement with OpenAI highlights its commitment to AI research and development.

Perion Network (NASDAQ: PERI) is another company that could benefit from the ChatGPT rollout. Perion Network has a strategic partnership with Microsoft’s Bing search engine, and Microsoft is planning to launch a new version of Bing powered by ChatGPT. This collaboration positions Perion Network as a potential winner in the AI space, as ChatGPT’s language capabilities can enhance the user experience and effectiveness of Bing’s search results.

Investors looking to capitalize on the AI boom have several other options to consider. Here are some of the top AI-powered companies that have established themselves as leaders in the industry:

Google (Alphabet): Google, a subsidiary of Alphabet Inc. (NASDAQ: GOOGL), is at the forefront of AI innovation. From its search engine algorithms to autonomous vehicles and machine learning applications, Google has made significant strides in harnessing the power of AI.

Microsoft: We’ve already discussed Microsoft’s involvement with OpenAI and its commitment to AI research and development. Microsoft’s Azure cloud platform also offers various AI services, making it a well-rounded investment option.

Nvidia: Nvidia (NASDAQ: NVDA) is a leading provider of graphics processing units (GPUs) used in AI training and inference. Its GPUs have become essential components in data centers and AI systems, positioning Nvidia as a key player in the AI hardware market.

Amazon: As one of the largest e-commerce and cloud computing companies in the world, Amazon (NASDAQ: AMZN) utilizes AI extensively to improve customer experience, optimize logistics, and enhance its virtual assistant, Alexa.

Meta Platforms: Formerly known as Facebook, Meta Platforms (NASDAQ: FB) is investing heavily in AI technologies. The company’s AI initiatives include facial recognition, content filtering, and natural language processing, among others.

C3.ai: chúng tôi (NYSE: AI) is a leading enterprise AI software provider. The company offers a range of AI solutions, including predictive maintenance, fraud detection, and customer engagement, catering to various industries such as energy, healthcare, and manufacturing.

Accenture: Accenture (NYSE: ACN) is a global professional services company that has embraced AI as a core part of its offerings. The company leverages AI to enhance its consulting, technology, and outsourcing services.

Epam Systems: Epam Systems (NYSE: EPAM) is a leading global provider of digital platform engineering and software development services. The company utilizes AI technologies to deliver innovative solutions across industries such as finance, healthcare, and retail.

Adobe: Adobe (NASDAQ: ADBE) is known for its creative software suite, but it has also made significant investments in AI. Adobe Sensei, its AI and machine learning framework, powers features in its products and helps businesses make data-driven decisions.

Baidu: Baidu (NASDAQ: BIDU) is often referred to as the “Google of China” and is a major player in the AI industry. The company focuses on AI research, autonomous vehicles, and voice recognition technology.

Also Check: How to Use Adobe Podcast AI: A Comprehensive Guide

While investing in AI presents promising opportunities, it is essential to consider the associated risks. Here are some factors to keep in mind:

Regulatory Challenges: As AI technology becomes more prevalent, there may be increased scrutiny and regulatory challenges. Changes in regulations or public sentiment towards AI could impact the growth prospects of AI companies.

Competitive Landscape: The AI industry is highly competitive, with many companies vying for market share. The success of an AI company may depend on its ability to differentiate itself and stay ahead of the competition.

Ethical Considerations: AI technology raises ethical concerns related to privacy, data security, and algorithmic biases. Negative publicity or legal issues surrounding these concerns can have a significant impact on AI companies.

Technological Risks: AI is a complex field, and breakthroughs in new technologies could render existing AI solutions obsolete. Investing in AI requires understanding the technical landscape and the potential risks associated with evolving technologies.

ChatGPT is not publicly traded, and therefore, there is no stock symbol for it.

One way to gain exposure to ChatGPT is by investing in Microsoft (NASDAQ: MSFT), as Microsoft has a partnership with OpenAI and a significant position in the company.

Perion Network (NASDAQ: PERI) has a strategic partnership with Microsoft’s Bing search engine, and Microsoft is rolling out a new version of Bing powered by ChatGPT. This collaboration could benefit Perion Network by improving the effectiveness of Bing’s search results.

Some of the top AI-powered companies to consider include Google (Alphabet), Nvidia, Amazon, Meta Platforms, and chúng tôi among others.

Investing in AI comes with risks such as market volatility, regulatory challenges, competition, ethical considerations, and technological risks. It’s important to assess these factors before making investment decisions.

Conduct thorough due diligence, stay updated with industry trends, diversify your investment portfolio, and consider consulting with financial professionals to mitigate the risks associated with investing in AI.

Investing in AI offers exciting opportunities for investors looking to capitalize on the transformative power of artificial intelligence. While there is no stock symbol for ChatGPT, there are indirect ways to gain exposure to AI technology. Companies like Microsoft and Perion Network have strategic partnerships and collaborations that can provide investors with indirect exposure to ChatGPT.

Moreover, there are several other AI-powered companies such as Google (Alphabet), Nvidia, Amazon, Meta Platforms, and chúng tôi that investors can consider for their AI-focused investment strategies. These companies are at the forefront of the AI industry, driving innovation and shaping the future.

Ai Vs. Machine Learning Vs. Deep Learning

Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality.

Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future.

Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market. Among vendors selling big data analytics and data science tools, two types of artificial intelligence have become particularly popular: machine learning and deep learning.

While many solutions carry the “AI,” “machine learning,” and/or “deep learning” labels, confusion about what these terms really mean persists in the market place. The diagram below provides a visual representation of the relationships among these different technologies:

As the graphic makes clear, machine learning is a subset of artificial intelligence. In other words, all machine learning is AI, but not all AI is machine learning.

Similarly, deep learning is a subset of machine learning. And again, all deep learning is machine learning, but not all machine learning is deep learning.

Also see: Top Machine Learning Companies

AI, machine learning and deep learning are each interrelated, with deep learning nested within ML, which in turn is part of the larger discipline of AI.

Computers excel at mathematics and logical reasoning, but they struggle to master other tasks that humans can perform quite naturally.

For example, human babies learn to recognize and name objects when they are only a few months old, but until recently, machines have found it very difficult to identify items in pictures. While any toddler can easily tell a cat from a dog from a goat, computers find that task much more difficult. In fact, captcha services sometimes use exactly that type of question to make sure that a particular user is a human and not a bot.

In the 1950s, scientists began discussing ways to give machines the ability to “think” like humans. The phrase “artificial intelligence” entered the lexicon in 1956, when John McCarthy organized a conference on the topic. Those who attended called for more study of “the conjecture 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.”

Critics rightly point out that there is a big difference between an AI system that can tell the difference between cats and dogs and a computer that is truly intelligent in the same way as a human being. Most researchers believe that we are years or even decades away from creating an artificial general intelligence (also called strong AI) that seems to be conscious in the same way that humans beings are — if it will ever be possible to create such a system at all.

If artificial general intelligence does one day become a reality, it seems certain that machine learning will play a major role in the system’s capabilities.

Machine learning is the particular branch of AI concerned with teaching computers to “improve themselves,” as the attendees at that first artificial intelligence conference put it. Another 1950s computer scientist named Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.”

In traditional computer programming, a developer tells a computer exactly what to do. Given a set of inputs, the system will return a set of outputs — just as its human programmers told it to.

Machine learning is different because no one tells the machine exactly what to do. Instead, they feed the machine data and allow it to learn on its own.

In general, machine learning takes three different forms:

Reinforcement learning is one of the oldest types of machine learning, and it is very useful in teaching a computer how to play a game.

For example, Arthur Samuel created one of the first programs that used reinforcement learning. It played checkers against human opponents and learned from its successes and mistakes. Over time, the software became much better at playing checkers.

Reinforcement learning is also useful for applications like autonomous vehicles, where the system can receive feedback about whether it has performed well or poorly and use that data to improve over time.

Supervised learning is particularly useful in classification applications such as teaching a system to tell the difference between pictures of dogs and pictures of cats.

In this case, you would feed the application a whole lot of images that had been previously tagged as either dogs or cats. From that training data, the computer would draw its own conclusions about what distinguishes the two types of animals, and it would be able to apply what it learned to new pictures.

By contrast, unsupervised learning does not rely on human beings to label training data for the system. Instead, the computer uses clustering algorithms or other mathematical techniques to find similarities among groups of data.

Unsupervised machine learning is particularly useful for the type of big data analytics that interests many enterprise leaders. For example, you could use unsupervised learning to spot similarities among groups of customers and better target your marketing or tailor your pricing.

Some recommendation engines rely on unsupervised learning to tell people who like one movie or book what other movies or books they might enjoy. Unsupervised learning can also help identify characteristics that might indicate a person’s credit worthiness or likelihood of filing an insurance claim.

Various AI applications, such as computer vision, natural language processing, facial recognition, text-to-speech, speech-to-text, knowledge engines, emotion recognition, and other types of systems, often make use of machine learning capabilities. Some combine two or more of the main types of machine learning, and in some cases, are said to be “semi-supervised” because they incorporate some of the techniques of supervised learning and some of the techniques of unsupervised learning. And some machine learning techniques — such as deep learning — can be supervised, unsupervised, or both.

The phrase “deep learning” first came into use in the 1980s, making it a much newer idea than either machine learning or artificial intelligence.

Deep learning describes a particular type of architecture that both supervised and unsupervised machine learning systems sometimes use. Specifically, it is a layered architecture where one layer takes an input and generates an output. It then passes that output on to the next layer in the architecture, which uses it to create another output. That output can then become the input for the next layer in the system, and so on. The architecture is said to be “deep” because it has many layers.

To create these layered systems, many researchers have designed computing systems modeled after the human brain. In broad terms, they call these deep learning systems artificial neural networks (ANNs). ANNs come in several different varieties, including deep neural networks, convolutional neural networks, recurrent neural networks and others. These neural networks use nodes that are similar to the neurons in a human brain.

However, those GPUs also excel at the type of calculations necessary for deep learning. As GPU performance has improved and costs have decreased, people have been able to create high-performance systems that can complete deep learning tasks in much less time and for much less cost than would have been the case in the past.

Today, anyone can easily access deep learning capabilities through cloud services like Amazon Web Services, Microsoft Azure, Google Cloud and IBM Cloud.

If you are interested in learning more about AI vs machine learning vs deep learning, Datamation has several resources that can help, including the following:

Decoding The Next Generation Of Ai

The Uniqueness Behind the AI powered Robots

So what is about the robot evolution from the automation to autonomy? What started off as a quest to make routine work easy through automation has come a long way towards full robot autonomy? AI brings a game changer approach to robotics by enabling a move away from automation to true self-directed autonomy. When the robot needs to handle several tasks, or respond to humans or changes in the environment, it essentially needs certain levels of autonomy. The path from autonomy has been an uphill but a truly worthwhile change. According to a source, the evolution of robots can be explained by burrowing case studies from the autonomous car space. For an easy explanation of the process underlined below, robots are defined as the programmable machines capable of carrying out complex actions automatically. • Level 0 stage is also called as the No automation stage where people operate machines, there is no automation without any robotic involvement. • Level 1 stage is the driver assistance level, where a single function or task is automated, but the robot does not necessarily use information about the environment. Traditionally, robots are deployed in automotive or manufacturing industries programmed to repeatedly perform specific tasks with a high precision and speed. • Level 2 stands for partial automation where a machine assists with certain functions, using sensory input from the environment to automate some operational decisions. Examples include identifying and handling different objects with a robotic vision sensor. In this stage, robots lack the ability to deal with surprises, new objects or changes. • Level 3 is the Conditional autonomy where the machine controls the entire environment monitoring, but still requires a human’s intervention and attention for unpredictable events. • Level 4 is the high autonomy stage where the machine is fully autonomous in certain situations or defined areas. • Level 5 is the complete autonomy level powering the machine with full automation in all situations.

The Current Stage of Automation

Today, a majority of robots deployed in factories are non-feedback controlled, or open-looped implying that their actions are independent from sensor feedback as that happens in level 1 stage as discussed above. Few robots in the business act and take commands based on sensor feedback as that happens in Level 2. A collaborative robot, or co-bot, is designed to be more versatile empowered to work with humans; however, the trade-off is less powerful and happens at lower speeds, especially when compared to industrial robots. Though a co-bot is relatively easier to program, it is not necessarily autonomous to handle. There is often a need of human workers to handhold a co-bot every whenever there is any change in the environment or the task. Pilot projects integrated with AI-enabled robots, have started to become a regular feature incorporating a Level 3 or 4 autonomy, like warehouse piece-picking. Traditional computer vision cannot handle a wide variety of objects like that in e-commerce because each robot needs to be programmed beforehand and each item needs to be registered. However reinforcement learning and deep learning has enabled robots to learn to handle different objects with minimum human assistance. In the times to come, there might be some goods that robots have never encountered before which would need a support system and a demonstration from human workers bringing the level 3 of automation. In the times to come, improvements will be seen into algorithms to get closer to full autonomy as the robots collect more data and improve through trial and error in Level 4. Taking a clue from the autonomous car industry, robotics startups are additionally taking different approaches to autonomy for their robots. Some aspects believe in a collaborative future between robots and humans, and focus on Level 3 mastery. While in a fully autonomous future, skipping Level 3 and focusing on Level 4, and eventually on Level 5 will be difficult to assess the actual level of autonomy.

The Age of AI-Enabled Robots in Industries

Taking the brighter side, robots are being used in a lot more use cases and industries than ever before. AI-enabled robots are running warehouses, in a semi-controlled environment, picking up critical pieces that are fault-tolerant tasks. On the other hand, autonomous home or surgical robots will be a reality of the future, as there are uncertainties in the operating environment, where some tasks are not recoverable. With the change in time, the human eyes will see more AI-enabled robots being used across industries and scenarios as reliability and technology precision improves. The world has seen only about 3 million robots, most of which work on welding, assembly and handling tasks. There have been very few robot arms being used in varied industries like agriculture, industries or warehouses apart from electronics and automotive units, due to the limitation of computer vision.

Robotics brings together a wide range of different machines including Pepper partnering with soft-bank; the Boston Dynamics humanoid robot Atlas, which can do backflips in movies and television and a plethora of humanoids and Bots that leave the human mind with awe and inspiration to achieve new tech heights. Much that the technology that powers robotics continues to achieve new pinnacle; people not familiar with the developments tend to hold polarized views, ranging from unrealistically high expectations of robots with human-level intelligence, or an underestimation of the potential of new research and technologies. Over the past years, questions have been asked about what is actually going on in deep reinforcement learning and robotics industry. How are AI-enabled robots different from traditional ones and their underlying potential to revolutionize various industries, what is the new excitement the robotics industry holds for the future. These questions point towards the challenging world of robotics and how difficult it can go to understand the current technological progress and industry landscape, to enable tech giants and newbies alike to make predictions for the chúng tôi what is about the robot evolution from the automation to autonomy? What started off as a quest to make routine work easy through automation has come a long way towards full robot autonomy? AI brings a game changer approach to robotics by enabling a move away from automation to true self-directed autonomy. When the robot needs to handle several tasks, or respond to humans or changes in the environment, it essentially needs certain levels of autonomy. The path from autonomy has been an uphill but a truly worthwhile change. According to a source, the evolution of robots can be explained by burrowing case studies from the autonomous car space. For an easy explanation of the process underlined below, robots are defined as the programmable machines capable of carrying out complex actions automatically. • Level 0 stage is also called as the No automation stage where people operate machines, there is no automation without any robotic involvement. • Level 1 stage is the driver assistance level, where a single function or task is automated, but the robot does not necessarily use information about the environment. Traditionally, robots are deployed in automotive or manufacturing industries programmed to repeatedly perform specific tasks with a high precision and speed. • Level 2 stands for partial automation where a machine assists with certain functions, using sensory input from the environment to automate some operational decisions. Examples include identifying and handling different objects with a robotic vision sensor. In this stage, robots lack the ability to deal with surprises, new objects or changes. • Level 3 is the Conditional autonomy where the machine controls the entire environment monitoring, but still requires a human’s intervention and attention for unpredictable events. • Level 4 is the high autonomy stage where the machine is fully autonomous in certain situations or defined areas. • Level 5 is the complete autonomy level powering the machine with full automation in all situations.Today, a majority of robots deployed in factories are non-feedback controlled, or open-looped implying that their actions are independent from sensor feedback as that happens in level 1 stage as discussed above. Few robots in the business act and take commands based on sensor feedback as that happens in Level 2. A collaborative robot, or co-bot, is designed to be more versatile empowered to work with humans; however, the trade-off is less powerful and happens at lower speeds, especially when compared to industrial robots. Though a co-bot is relatively easier to program, it is not necessarily autonomous to handle. There is often a need of human workers to handhold a co-bot every whenever there is any change in the environment or the task. Pilot projects integrated with AI-enabled robots, have started to become a regular feature incorporating a Level 3 or 4 autonomy, like warehouse piece-picking. Traditional computer vision cannot handle a wide variety of objects like that in e-commerce because each robot needs to be programmed beforehand and each item needs to be registered. However reinforcement learning and deep learning has enabled robots to learn to handle different objects with minimum human assistance. In the times to come, there might be some goods that robots have never encountered before which would need a support system and a demonstration from human workers bringing the level 3 of automation. In the times to come, improvements will be seen into algorithms to get closer to full autonomy as the robots collect more data and improve through trial and error in Level 4. Taking a clue from the autonomous car industry, robotics startups are additionally taking different approaches to autonomy for their robots. Some aspects believe in a collaborative future between robots and humans, and focus on Level 3 mastery. While in a fully autonomous future, skipping Level 3 and focusing on Level 4, and eventually on Level 5 will be difficult to assess the actual level of autonomy.Taking the brighter side, robots are being used in a lot more use cases and industries than ever before. AI-enabled robots are running warehouses, in a semi-controlled environment, picking up critical pieces that are fault-tolerant tasks. On the other hand, autonomous home or surgical robots will be a reality of the future, as there are uncertainties in the operating environment, where some tasks are not recoverable. With the change in time, the human eyes will see more AI-enabled robots being used across industries and scenarios as reliability and technology precision improves. The world has seen only about 3 million robots, most of which work on welding, assembly and handling tasks. There have been very few robot arms being used in varied industries like agriculture, industries or warehouses apart from electronics and automotive units, due to the limitation of computer vision. Over the next 20 years, the world will witness an explosive growth and a changing industry landscape which will bought by the next-generation robots as reinforcement learning, cloud computing and deep learning unlock the robotic potential.

The Grandfather Of Ai Art, Dall

On Wednesday, chúng tôi removed the waitlist to sign up for DALL-E, allowing anyone to join after registering for a free account. (The linked blog post includes a link to sign up.)

Each signup adds 50 credits to your account, with each credit generating four 1024×1024 images from a single prompt from the OpenAI server. You’ll get 15 new credits per month, though the credits do not roll over. OpenAI also has placed content limits on the type of images you can generate, forbidding violence, sexual acts (including nudity), politicians, and public figures. On the other hand, the 1024×1024 image sizes are larger than other AI art generators, and the images render quite quickly.

DALL-E also supports outpainting, a relatively new AI art technique that allows you to create variations on a scene within certain regions. For example, if you created a prompt that generated a scene where a fairy and a giant had a picnic on a cliff, your vision of what the backdrop might look like could clash with what DALL-E generated. Outpainting allows you to simply highlight or erase the backdrop with a virtual paintbrush, and DALL-E will provide variations on that region.

Image editing, or outpainting, allows you to change just part of a scene to allow DALL-E to perform variations on the selected region.

Is DALL-E good? In certain scenarios, yes—and in others, you’ll find better success elsewhere. DALL-E feels a little bit dumb, in the sense that computers are dumb: It favors explicit prompts, and seems to take instructions rather literally. If you try a rather generic prompt—”the castle of time” has been one I’ve used before—you’ll probably receive photo-like compositions of ordinary castles.

Likewise, “a starship enters a warp portal against the backdrop of a binary star, sci-fi, epic, cinematic lighting” gave me something that looked a little uninspired. “Promptcraft,” where users create detailed, specific prompts to create specific outcomes, may help here. But even adding AI artists’ favorite inspiration, Greg Rutkowski, doesn’t do that much for the finished image.

Instead, DALL-E seems to work best with simple compositions. “A photo of a kraken emerging from the ocean underneath the Golden Gate Bridge” generated the image you see leading off this story, which in my opinion is quite good. “A bowl of robotic fruit” produced a rather nice conceptual image, below. Take your cues from the automated interstitial images that DALL-E generates as it’s processing prompts, and you’ll have better luck.

DALL-E generated this image using the prompt, “A bowl of robotic fruit.”

Mark Hachman / IDG via DALL-E

Remember, though, that this DALL-E AI art generator is just the first generation. In April, OpenAI moved on to the more sophisticated DALL-E 2—which is also restricted to beta access at the moment.

If you’re looking for more artistic compositions, stick with Midjourney, which also works on a credit system. For those with access to a gaming PC or GPU, however, we’d recommend you try out Stable Diffusion on your own PC, which allows you to try out as many compositions as you have time for. And remember, AI art isn’t just images; you can play virtual D&D with AI, generate artificial voices, and more, in our AI art primer.

Ai Black Box: A Demystified Guide

Understanding AI black box that refers to AI systems with core workings that are unseen to the user

Some people associate the phrase “black box” with the recording mechanisms used in aircraft that are useful for postmortem examinations in the event of the unthinkable. Others associate it with tiny, sparsely furnished theatres.

But the phrase “black box” is also significant in artificial intelligence. AI “black boxes” are systems that have unobservable internal operations. You can provide input to them and receive output, but you cannot look at the system’s code or the reasoning that led to the output.

The most common branch of artificial intelligence is machine learning. It is the foundation of ChatGPT and DALL-E 2, two generative AI systems. Machine learning consists of a model, training data, and a method or group of algorithms. An algorithm is a collection of steps. In machine learning, an algorithm is trained on a sizable collection of examples, or “training data,” and then learns to recognize patterns. A machine-learning model is produced when a machine-learning algorithm has been trained. Humans employ the model.

A machine-learning algorithm, for instance, might be created to find patterns in photos, and the training data might be pictures of dogs. A dog spotter machine learning model would be created as a result. It would take an image as input and return information on whether and where a collection of pixels in the image indicate a dog.

A machine-learning system can have any one of its three components hidden or in a “black box.” The algorithm is widely known, as is frequently the case, making using a black box less effective. Thus, AI developers often enclose the model in a black box to safeguard their intellectual property. Another strategy software developers employ is hiding the data used to train the model or placing the training data in a “black box.”

Glass boxes are occasionally used to describe the opposite of a black box. An AI glass box is a system whose training data, models, and algorithms are all publicly accessible. However, some academics refer to certain of even these as “black boxes.”

This is because deep learning algorithms, in particular, still need to be better understood by experts. Researchers in explainable AI strive to create algorithms that, while not necessarily “glass boxes,” are more accessible for people to understand.

Importance of AI Black Box

Black box machine learning techniques and models should generally be avoided. Let’s say a machine-learning algorithm has identified a health issue. Would you like a glass box or a black box model? What about the doctor who issued your treatment plan? She could be interested in learning how the model made its choice.

What happens if a machine-learning model used to verify your eligibility for a bank loan for your business rejects you? Do you want to discover the reason? If you did, you may more successfully challenge the ruling or alter your circumstances to improve your loan prospects.

Black boxes have significant effects on software system security as well. Many people in the computing industry believed for many years that placing software within a black box would prevent hackers from looking at it, making it secure. The ability of hackers to reverse-engineer software or create a copy by carefully studying how a piece of software functions and finding weaknesses to exploit has disproved this presumption.

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