Рубрика: AI

Unite.AI‘s Images.ai is an AI image generator that utilizes the cutting-edge stable diffusion open-source code to create stunning visual content. With a focus on simplicity and user-friendly design, Images.ai makes it easy for anyone to generate spectacular pieces of art with just a search term.

The platform also offers meme functionality, allowing users to share their creations with their community. To get the most out of our AI image generator, we'll explore how to use Images.ai effectively, diving into text prompts, effective prompt writing, prompt recipes, and more.

What are text prompts?

Text prompts serve as the input for AI image generators like Images.ai. They are simple phrases or descriptions that guide the AI in generating a visual representation based on the user's input. By providing clear and concise text prompts, users can communicate their desired image concept to the AI, allowing it to generate an image that closely matches their vision.

Understanding the importance of text prompts in AI image generation is crucial for effective results. A well-crafted prompt can mean the difference between a captivating, accurate image and one that misses the mark. The AI interprets the user's input and draws upon its vast database of images and styles to generate a unique visual representation that aligns with the provided prompt.

How to write effective prompts for Images.ai

Writing effective prompts is essential for obtaining accurate and visually appealing results from Images.ai. Here are some tips to help you write compelling prompts:

1. Be specific: Provide clear and detailed descriptions to guide the AI in generating the image you envision. Vague or ambiguous prompts can lead to unpredictable results. For instance, instead of writing “a dog,” try “a happy golden retriever playing in a field.”
2. Use adjectives: Descriptive words can help communicate the style, mood, or atmosphere you want to achieve in the generated image. Examples of adjectives include “surreal,” “vibrant,” “mystical,” or “serene.” These terms can help guide the AI in generating an image that matches your desired aesthetic.
3. Experiment with different phrasings: If you're not satisfied with the initial results, try rephrasing your prompt or using synonyms to explore different interpretations of your concept. For example, if “a mysterious forest” doesn't yield the desired outcome, consider using “an enigmatic woodland” or “a shadowy grove.”
4. Combine concepts: Merge two or more ideas in your prompt to create a unique and interesting image. For example, instead of just “a cityscape,” try “a futuristic cityscape with floating cars and skyscrapers connected by sky bridges.”

Image generated by Images.ai

What are prompt recipes?

Prompt recipes are pre-defined templates or patterns that users can follow to create effective text prompts for AI image generators like Images.ai. These recipes usually consist of specific structures or combinations of words that have been proven to yield impressive results with AI image generation. By using prompt recipes, users can take advantage of tried-and-tested formulas to generate high-quality images more consistently.

Examples of prompt recipes might include:

1. [Subject] in a [setting] with a [mood] atmosphere: This recipe encourages users to provide a subject, setting, and mood to guide the AI in generating a detailed and evocative image.
2. A [style] interpretation of [concept]: This recipe prompts users to specify a particular artistic style (e.g., impressionist, cubist, or abstract) and a concept for the AI to generate an image inspired by that style.
3. A mashup of [concept 1] and [concept 2]: This recipe allows users to combine two seemingly unrelated concepts or ideas, resulting in a unique and intriguing image generated by the AI.

Image generated by Images.ai

Mastering Images.ai for your creative projects

Images.ai offers a powerful and user-friendly platform for generating unique visual content using AI. To make the most of this tool, remember the following tips:

1. Experiment with text prompts: Don't be afraid to try different prompts and phrasings to discover the full potential of Images.ai. With each iteration, you'll gain a better understanding of how the AI interprets your input and refine your prompts accordingly. The more you experiment, the more diverse and captivating your AI-generated images will become.
2. Use the meme functionality: Tap into the platform's meme functionality to add a touch of humor or pop culture references to your images. This feature allows users to create engaging and shareable content that resonates with a broader audience. By incorporating trending memes, catchphrases, or popular characters, you can enhance the appeal of your AI-generated images.
3. Create a personalized gallery: Sharing your gallery via a unique link will enable others to appreciate and admire your AI-generated art. To see your gallery link simply click on “Profile” in the top right navigation menu, scroll down and you'll find your gallery link.
4. Explore different artistic styles: Images.ai‘s AI algorithms are capable of generating images in various artistic styles, from impressionism to abstract art. Experiment with different styles in your text prompts to diversify your AI-generated image collection and gain a deeper understanding of the platform's capabilities.
5. Collaborate and learn from the community: Engage with other Images.ai users to learn from their experiences, share tips, and discuss the platform's features. By actively participating in the community, you can gain insights into the most effective strategies for using Images.ai and stay updated on new features and improvements.

Image generated by Images.ai

Images.ai by Unite.AI is a powerful and accessible AI image generator that empowers users to create stunning visual content with ease. By understanding and mastering text prompts, effective prompt writing, and prompt recipes, you can harness the full potential of Images.ai for your creative projects. As the platform continues to grow and evolve, users can expect even more impressive and versatile image generation capabilities. Embrace your inner artist and start exploring the possibilities with Images.ai today. With persistence and practice, you'll be well on your way to becoming an AI-generated art connoisseur.

We can’t way to see what creations you come up with!

Click Here to visit Images.ai

Generative AI has gained significant attention in recent years, piquing interest in its potential for automation. This ongoing revolution has placed Generative AI at the forefront of human interaction with automated systems. While users traditionally engage with such systems by posing questions and issuing commands, Generative AI adds a new dimension of functionality to the equation.

Automation Anywhere is one such company driving the transformational power of generative AI through its Automation Success Platform (ASP). By integrating Open AI’s GPT models natively, the company is partnering with several GCCs in India to elevate its operations.

In an interview with Ankur Kothari, Co-founder and Chief Customer and Strategy Officer at Automation Anywhere, Analytics India Magazine learned how the company is shaping the landscape of generative AI services.

Kothari explained the impact of Language Model Masters (LLMs) on intelligent automation, serving as a turbocharger for compliance and guided system operation. Enterprises, particularly those on the consumer side, are experiencing a paradigm shift using generative AI solutions.

How GenAI is Shaping Enterprises

Kothari says that his team has been deeply immersed in the world of Generative AI, experimenting with various algorithms and working on hundreds of different use cases for clients and partners.

“We offer a comprehensive end-to-end solution, starting from identifying processes that can be automated to creating bots that can execute these actions, to leveraging our IDP product to ingest unstructured or semi-structured data, and ultimately managing and maintaining these bots while generating insightful analytics,” Kothari said.

Giving an example of how the company provides solutions, Kothari explained, “Imagine you are an airline company, and your customers frequently call or email you with requests to change their tickets. Oftentimes, these emails are not very well-structured and may not include all the necessary information to process the request efficiently. This is where natural language processing comes in.”

With the help of natural language processing, Kothari said that the company can use a machine learning algorithm to extract relevant information from the customer’s email and pass it on to the bot. The bot then takes this information, such as the customer’s ticket ID, and searches the database for all other relevant information about that passenger. Once it has all the necessary information, the bot can make the requested changes and generate a response back to the customer.

He adds that it’s important to note that there is still a human involved in this process, particularly when it comes to generating the response that is sent back to the customer. Companies are implementing guided processes that allow a human to review and modify the bot’s response before it is sent out to ensure accuracy and avoid any potential errors.

Moreover, he highlighted that the platform is the sole cloud-native RPA product available, meaning it operates efficiently on both on-premises and cloud infrastructures. “This enables us to deliver cutting-edge solutions to our customers without any limitations,” Kothari stated. Additionally, the platform boasts a powerful automation and co-pilot feature that allows users to summon bots from various applications, streamlining processes and enhancing productivity.

Fear of copyright infringement

When asked about how organisations can address the issue of intellectual property being exposed in open databases, as Samsung recently experienced, Kothari acknowledged the challenges companies face in grappling with this critical issue.

There’s discussion happening in every boardroom on how to use this technology. He noted that there are two choices: ignore it and risk falling behind the competition, or work with vendors to create policies and guidelines to use it securely.

However, Kothari acknowledged that not all problems have been solved yet, as this technology is still in its early stages. He stressed the importance of companies creating governance and policies around what can and cannot be touched, particularly in industries like automotive, where these technologies are heavily used.

Nevertheless, intelligent automation platforms like Automation Anywhere can help companies work with partners to bring these technologies into their companies in a more controlled and secure way. At present, enterprises are in the exploration phase, and many are collaborating with companies like Infosys, TCS, Accenture, or Automation Anywhere to determine how they can leverage this technology. “By combining these technologies and leveraging each other’s strengths, companies can maximise the benefits of this innovation while maintaining security and control,” says Kothari.

Do Not Fear AI

Over the past couple of months, the tech world has been seeing thousands of layoffs. There have been huge job losses in tech and IT sectors. While the common reason given is the uncertain market conditions in America, many co-relate to this automation as well.

However, Kothari believes that while some companies initially approach automation with apprehension, fearing job loss – the reality is quite different. “Automation allows companies to do more with less, enabling them to leverage new opportunities and create new roles that were not possible before,” says Kothari.

He says that the fear of job loss that is often associated with new technologies is unfounded, as history has shown that technology actually facilitates job creation and innovation, leading to a more competitive business landscape.

Kothari further explains that recent news regarding job losses has nothing to do with automation. Automation technology has been in use in India for over a decade, and job losses have not been a concern. Instead, the adoption of automation has led to the creation of new roles and offerings for clients, which would not have been possible without this technology.

The post This Company is Paving the Way for Generative AI Services appeared first on Analytics India Magazine.

Announcements

• In addition to ever-increasing volumes of data, storage needs have evolved due to increases in remote work, the use of cloud services, and cybersecurity concerns such as ransomware. In Modern Storage Management summit, learn from top industry experts and solution providers about the latest ways to effectively manage the flood of enterprise data. You’ll hear about the various forms of cloud storage and how they can benefit your storage strategy, new trends in data backup, and how to ensure security and data protection are always a priority.
• Automation helps companies keep on top of ever-growing workloads, cut costs and free workers from manual, tedious tasks. The automation market continues to advance to better address the increasing demands companies face. Tune into The Growth of Automation summit to hear leading experts discuss how AI, machine learning and other technologies can expand automation to provide further benefits, as well as the latest technologies to help successfully automate workflows.

Big tech must weigh AI’s risks vs. rewards

In an interview with the New York Times, Hinton noted the pace of AI advancement is far beyond what he and other tech experts predicted. Hinton said that Google acted very responsibly while he worked on its AI development efforts. His concerns are due to AI’s rapid advancement, and its potential nefarious use if it fell into the wrong hands.

Big consultancies predict the annual AI market could be in the trillions of dollars by the 2030s, Data Science Central contributor Alan Morrison notes in his article this week. It seems well on its way: After ChatGPT launched in late 2022, tech companies large and small deployed similar AI tools designed to engage customers, conduct research and develop business plans.

As these AI tools continue to advance (and make tons of money), it will be difficult for companies to slow AI research and advancement to offset risk. Balancing this risk vs. reward will be key, and require cooperation between lawmakers, the tech industry and the general public — no small task.

Speaking of initiatives that require cooperation from large swaths of society, we wanted to announce the launch of TechTarget’s Sustainability and ESG site. The site provides information for business leaders who are optimizing technology for sustainability purposes. As Sustainability and ESG editor Diann Daniel notes in her introductory article, AI, analytics, virtual reality, and many more technologies will factor into organizations’ sustainability efforts. Visit the Sustainability and ESG site to understand which technologies help and which end up doing more harm.

The Editors of Data Science Central

Contact The DSC Team if you are interested in contributing.

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Picture of the Week

When students are looking for a university, factors they consider typically include location, cost, school spirit, and academics, just to name a few. Now there is a new factor to consider, and it's ChatGPT.

Since ChatGPT first arrived on the scene, one of the biggest concerns people had about AI chatbots is how they will affect the education system. As a result, some school districts and professors are choosing to ban ChatGPT as a whole.

Universities may want to reconsider those policies in light of new data.

Also:ChatGPT is the newest in-demand tech skill

A study surveyed 372 students seeking admission into college for fall 2023 and found that nearly half, 39% of those students, would not consider attending a college that's banned ChatGPT or other AI tools.

The study also polled 1,000 university students to learn more about the impact ChatGPT is having in college students' lives.

Over 40% of the students polled said they use ChatGPT for their coursework with 41% saying they use it as often as a few times per week.

The topics students are using ChatGPT the most for include English (21%) and math (17%). It makes sense that English is the top subject students use it for, given that the chatbot has proven to be a proficient essay writing assistant.

Also: AI might enable us to talk to animals soon. Here's how

Most interestingly, out of the same pool of students, 36% said that their professors have threatened to fail students caught using AI technologies for coursework.

AI tool bans by professors or schools aren't enough to stop students from using the technology though they may push some students away from schools where they're enforced.

Perhaps it would be more beneficial to embrace the technology, as some professors have done, to have more control about how students use ChatGPT in their coursework and help them prepare to be able to use AI tools in their future careers.

See also

Snap announces tests of sponsored links in My AI, new ad products for Spotlight and Stories Sarah Perez @sarahintampa / 9 hours

After Snap’s stock took a hit from its weak first-quarter earnings, the company today made its pitch to advertisers at IAB’s NewFronts where it introduced new ad products and opportunities. Here, Snap’s new President of Americas Rob Wilk, previously Microsoft’s head of Advertising, and Chief Creative Officer Colleen DeCourcy, spoke about a test that allowed Snap partners to leverage its new AI feature, My AI, to place sponsored links in front of users. Snap also announced new ad slots, including the option to reserve the first video ad seen in Snapchat’s Friend Stories and the ability to advertise within its TikTok-like Spotlight feature.

The announcements come at a key time for Snap’s ad business, which saw its sales fall for the first time as a public company, despite a 15% year-over-year increase in Snapchat users to 383 million.

The tech industry’s first-quarter results made clear that the advertising rebound that had boosted Meta during Q1 was not yet benefiting the wider social app ecosystem. Snap wasn’t alone in feeling those impacts — Google’s YouTube business also saw its ad revenue decline by nearly 3% in the quarter. By opening up new ad products to marketers, Snap has the opportunity to drive revenue increases as it finds more places to insert ads into its mobile app.

While many of the ad slots Snap announced today were more traditional placements, one of its announcements stood out.

At the event, the company talked about how Snap could use its new AI feature to put sponsored links in front of users — something Snap CEO Evan Spiegel had teased during earnings.

My AI, which just rolled out to the wider Snapchat user base last month, can now suggest Lenses, Places from Snap Map and, soon, will be able to send a generative Snap back to Snapchat+ subscribers, in addition to having text-based conversations with users.

Today, the company confirmed it has begun testing sponsored links in conversations with My AI that would connect users with partners relevant to their conversation. For instance, if a Snapchat user asked where to have dinner, My AI could return a link sponsored by a local restaurant or a food delivery app. If a user was talking about a weekend trip, My AI could return a sponsored link from an airline or hotel. Or if a user was talking about a video game, users might receive a link to a similar game from a local retailer.

Image Credits: Snap

Snap says it’s still in an early experimental stage with this feature and wants to make sponsored links as useful as possible.

It also notes that the learnings from AI could introduce mobile video powered by conversational intent to Snapchat users for the first time and noted how users’ conversations with the AI would help Snap to serve more relevant content across its app, including in areas like Stories and Spotlight. In other words, it sounds like what users talk to the AI about could then change the experience of what they see elsewhere in the app.

To aid with this, Snap informs users that it’s saving their AI conversations until the user manually deletes them.

It’s worth pointing out how quickly Snap is running with the AI feature’s addition. The company just over a month ago said it was still seeking qualified AI experts to join its Safety Advisory Board. It also launched the AI product to global users before introducing parental controls — though it says those are coming. Meanwhile, Snapchat users have been panning the AI with one-star reviews amid complaints that the feature can’t be removed from their Chat tab without paying for a subscription.

Snapchat sees spike in 1-star reviews as users pan the ‘My AI’ feature, calling for its removal

Other Snap announcements today involved Spotlight, its TikTok clone, which launched in 2020. Snapchat began testing ads within the product a year ago and, during last week’s earnings, Spiegel said Spotlight now reaches 350 million monthly active users — a figure that’s up 170% year-over-year.

Now, the company is opening up Spotlight to global advertisers, it said at NewFronts.

Spotlight ads will initially be served as automatic placements with the service and advertisers will be able to manage those via the Snapchat Ads Manager. Of course, there’s a risk with buying ad slots next to any user-generated content, but Snap says it moderates Spotlight content before it reaches a wider audience, which reduces the chance that marketers’ content would appear alongside hate speech or any harmful content.

In addition, Snap introduced a new takeover ad product called “First Story,” which lets advertisers reserve the first Snap Ad (the video ad slot between Friend Stories), that users would see. The company compared the product, which it said was in high demand, to existing offerings like First Commercial and First Lens, which also let advertisers reserve the first spot in other parts of Snapchat’s app. The potential of being first in Friend Stories means a marketer is more likely to reach users before they exit. In the U.S., Snap says the potential daily reach is over 50 million.

Image Credits: Snap

The First Story ad placements are launching today globally, and Warner Bros. is the first client. It will be using the ad slot for marketing the upcoming feature, “The Flash.”

The company also today announced it’s making it easier for brands to work with Snap creators through Snap Star Collab Studio in the U.S., its service that helps brands source, partner and drive results with Snap Stars, as it calls its top creators and other public figures on the app. The new Studio will aid partnerships by connecting brands with preferred production partners — Studio71, Beeline by Brat TV, Influential and Whalar — to create and execute their sponsored Stories and creative.

Image Credits: Snap

Over the course of the year, Snap will also expand its API and introduce additional tools for paid amplification, which will help brands’ creative reach more users.

Snap also announced a range of new content partnerships at today’s event, including plans to work with the Women’s World Cup, which joins its existing partners, NFL, NBA and WNBA in the sports realm. Women’s World Cup will bring exclusive content to Snapchat’s Stories, Spotlight and Camera for its upcoming tournament, the company says.

“Snapchat is all about real relationships, and where over 750 million people come every month to build connections and have fun with friends, families and their favorite creators,” Wilk said. “We’re thrilled to share at NewFronts how those real relationships drive real influence for brands as we announce new innovations and features across Stories, Spotlight, Creators, My AI and more.”

Everyone is going after generative AI these days, from big tech to IT companies to tech influencers, and now online communities. Last week, Reddit announced changes to its new API that will now start restricting the content pipeline used to train AI models by big-tech companies like Microsoft, Google, and OpenAI. This thought out move will now enable Reddit to put the fuel, the content, for chatbots like ChatGPT or Bard behind paywall. But, this begs a question: why the sudden shift towards monetisation, though?

Reddit chief Steve Huffman recognises the importance and value of the corpus of the data that the community platform hosts. And interestingly, Reddit is planning an initial public offering (IPO) this year. Since most of its revenue comes from advertising, the company’s plan to monetise on the generative AI landscape with the most valuable offering it has, is a smart move. “We don’t need to give all of that value to some of the largest companies in the world for free,” Huffman told The New York Times.

The current restriction of Reddit’s data API is just for big-techs which are building AI chatbots using LLMs.

The data API has been available in a structured form for developers since 2008. Unlike unstructured data that is available on the internet through web-scraping, Reddit’s API allows developers to research and build moderation and other tools by providing “data dumps”. The company says that it will still allow free-access to the Reddit data API for developers.

Following Reddit’s footsteps, the ‘LLM-obsessed’ Stack Overflow also announced that it is planning to begin charging large AI developers for access to its programming driven community questions. Stack Overflow chief Prashanth Chandrasekar told Wired that he was very supportive of Reddit’s approach.

“Community platforms that fuel LLMs absolutely should be compensated for their contributions so that companies like us can reinvest back into our communities to continue to make them thrive,” explained Chandrasekar.

Reddit and Stack Overflow have not yet released the exact pricing details for access to their data APIs. But with the recent charge of $42,000 per month for accessing 50 million tweets by Musk, it is possible that these two platforms will also charge somewhere around that number.

Chandrasekar said that companies that are building LLMs are violating the terms of service of the platform. Even though companies can use the data to train models freely, the content posted by users on the platform falls under a Creative Commons licence, which means it needs proper attribution to where the data came from, in this case to the questions and answers of the specific users. This is not possible in the case of LLMs and is therefore clearly a violation. This is similar to how Musk accused Microsoft and OpenAI of illegally using Twitter data and stopping the access.

Sailing Against the ‘Generative AI’ Tides

In a most absurd behaviour, Stack Overflow previously had banned posting of chatbot generated answers. But later, the company announced that it is planning to integrate generative AI services within the community. Now with putting the data behind a paywall, the community is clearly trying to surf on the generative AI waves, or stopping it from rising higher.

Chandrasekhar said that for ensuring that future chatbots perform better than the current ones, it is essential that they are trained on evolving and progressing data. Fencing off valuable data might deter AI training and slow improvements in LLMs. He believes that proper licensing of the data API will accelerate the development of high-quality LLMs.

Similarly, publishers have also been wary about the usage of their website for training AI chatbots. According to the Washington Post, Google’s Bard uses data from Wikipedia, New York Times, The Guardian, and a lot more websites in its CommonCrawl Database. It is quite possible that Wikipedia might also put up some walls behind the usage of its data for AI since it has been seeking donations for the last few years. Jimmy Donal Wales, CEO of Wikipedia, believes that generative AI could actually help improve the online encyclopaedia.

On the flip side, Discord has announced no plans for modifying its API offerings, and are going to remain free. Swaleha Carlson, the spokesperson of the company said the API is provided under the terms that forbid AI training anyway.

When it comes to Reddit, the situation might be tricky. The company mostly has a very healthy relationship with Google and Microsoft. The search engines “crawl” the community platform’s pages for indexing information in the search results. This has been boding well for Reddit as its pages appear higher in the search results.

The dynamics is clearly a little different when it comes to data gobbling LLMs. Now that the company is putting them up behind a paywall, that too for big AI makers like Google and Microsoft, it might run into a situation where the search engines stop crawling the community platform’s pages for search results. This might result in platforms like Reddit and Stack Overflow losing on the revenue they currently generate through visitors and advertisers.

Everyone is chasing the generative AI’s fool’s gold (If data is the gold, data API’s are the fool’s gold). When it comes to community platforms like Stack Overflow and Reddit, the move of monetising on data API’s has a high possibility of backfiring. At the same time, this could be best bet that they can make.

The post Reddit, Stack Overflow Chase Fool’s Gold in Generative AI Rush appeared first on Analytics India Magazine.

Irene Politkoff, Founder and Chief Product Evangelist at semantic modeling tools provider TopQuadrant, posted this description of the large language model (LLM) ChatGPT:

“ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data. So ChatGPT is not always trustworthy.”

Georgetown University computer science professor Cal Newport put his own assessment this way during an interview with David Epstein, author of Range:

“Unlike the human brain, these large language models don’t start with conceptual models that they then describe with language. They are instead autoregressive word guessers. You give it some text and it outputs guesses at what word comes next.”

Newport underscores the absence of conceptual models as one reason why LLMs don’t reliably provide good answers to questions. Politkoff points out, “Representing mental models and knowledge in general is what knowledge graphs (KGs) excel at.” She argues that KGs and LLMs can work well together.

Not only can KGs provide the mental models and the facts, but LLMs, she points out, can help generate KGs in a particular format you specify based on the text you feed it. An example of a prompt she mentioned: “Generate an RDF (Resource Description Format, or semantic standard-based subject/verb/object triples) rendition of the following using Turtle notation,” followed by the text you need to be in .ttl format.

Using knowledge graphs with LLMs: Some representative research findings

Researchers in recent years have been evaluating how knowledge graphs might work best with LLMs. Some projects have used knowledge graphs to inform or augment LLMs. Others have used LLMs to generate input for knowledge graphs.

Google

Google is of course known for coining the term knowledge graph in 2012 after it acquired Freebase in 2010. The company has been a leader in encouraging the use of standard schemas and specific kinds of structured data on the web in order to facilitate data curation, reuse, and discoverability.

Research Tech Leader and Manager Enrique Alfonseca presented his team’s findings on Using Knowledge Graph Data in Large Language Models at the Swiss Analytics Text Conference in 2022. The team evaluated two types of approaches to using knowledge graphs with LLMs: Internal and external.

Alfonseca referred to the internal approach as “knowledge infusion.” After trying several approaches to infuse knowledge into the learning of the LLM, Alfonseca, and team decided to try just stringing together and “dumping in” the same kinds of RDF triples Irene Politkoff referred to above for training purposes. That approach achieved infusion results that were just as good.

An external approach the team tried was querying and retrieving from the knowledge graph directly. “Structured representation works on par with natural language,” Alfonseca mentioned the need for multi-hop or chain reasoning, in which an accurate answer hinges on bringing together facts from several different places and deriving meaning that’s larger than the sum of those facts.

Optum

Optum, a US subsidiary of UnitedHealth Group, is an integrated payer/provider healthcare company that generated over $182 billion in revenues in 2022. Optum accounted for 50 percent of UHG’s 2022 earnings, up from 44 percent in 2017.

Kunal Suri and team of Optum in India intended to demonstrate that large language models’ “ability to learn relationships among different entities makes knowledge graphs redundant in many applications.” Their 2023 paper “Language Models Sound the Death Knell of Knowledge Graphs” describes the use of high dimensional vector representation (BioBERT word embeddings) to identify and extract synonyms for terms in the SNOWMED medical classification system.

After identification and synonym extraction, then the team examined cosine distance similarity of the word embeddings using KMeans clustering. The clusters each demonstrated effective centering around the same core concept listed in the SNOWMED system.

Thoughts on the research reviewed

It’s not clear to me that the Optum research summarized above actually proves what the authors of the paper claimed it did. Specifically, knowledge graphs aren’t just about standalone concepts and how they’re associated with a domain. KGs enable articulation at various tiers of abstraction–not just the domain of interest. And there’s a stateful, continual record of how connections originate, evolve, and proliferate.

Clearly, vector representations and databases have significant utility. But does that mean larger data management environments should be oriented around vector representations? Vectorization seems to me to have additive value. It’s not a replacement for semantic graph KGs.

As for the Google research, I felt it more or less demonstrated what I’d understood intuitively before I read the research. But I am surprised there is not more research along these lines than what I was able to uncover in my spare time on a weekend.

The EU has announced draft measures for the AI act. As with GDPR, the AI act also has implications for businesses worldwide.

To put this in context, Italy has now withdrawn its ban on chatGPT and in the UK, the government has pledged an initial £100 million to establish a Foundation Model Taskforce.

So, we will see all countries positioning themselves in a world dominated by AGI

There are two key points in this draft legislation:

1. AI systems will be classified based on their perceived risk (minimal, limited, high and unacceptable).
2. The proposed AI act potentially could force companies to disclose copyrighted source information on which the model is trained. Final discussions are still pending and the act is supposed to be applicable only in 2025

Here are my thoughts on the implications of the draft EU act

1. There is no ban even for high-risk tools where extra transparency will be needed. That means the idea of an actual ban proposed by the 6-month ban in the recent letter is a nonstarter.
2. The current scope is broad enough to cover future applications (which can be simply classed into a category)
3. The copyrighted material clause is interesting. Does it mean anyone who crawls the web should declare the source? Could it affect search engines also?
4. Many of the companies that claim copyright payments are aggregators. How do Getty, Reddit and stack overflow plan to share the revenue with the contributors on their site
5. The copyright clause will actually make it harder for new LLMs to be trained in my view – thereby reducing competition in the LLM market which is bad in my view

But this is just the beginning. I think the discussion will continue for years to come. i also suspect that there are some technical solutions possible which I discussed some time ago presenting probably the only real use case for blockchain

image source: Reuters

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are three buzzwords that have taken the tech world by storm in recent years. Although often used interchangeably, these terms are not synonymous. In this blog, we will delve into the differences between AI, ML, and DL, and provide some real-world examples of how each is used.
What is Artificial Intelligence?
Artificial Intelligence is a broad term used to describe the ability of machines to simulate human intelligence. In other words, AI involves the development of algorithms that enable machines to perform tasks that typically require human-like intelligence, such as problem-solving, reasoning, and learning.
AI is a broad field that encompasses any machine or system that can perform tasks that normally require human intelligence, such as reasoning, problem-solving, and learning. AI can be further classified into two categories:
1. Narrow or Weak AI: These are systems that are designed to perform specific tasks, such as speech recognition or image classification. These systems are trained on a specific dataset and can only perform the task they were designed for.
2. General or Strong AI: These are systems that can perform any intellectual task that a human can perform. This type of AI does not yet exist and is the subject of ongoing research.
AI has numerous real-world applications, such as in the healthcare industry, where it can be used to analyze medical records and diagnose diseases, and in the automotive industry, where it can be used to develop self-driving cars.
What is Machine Learning?
Machine Learning is a subset of AI that involves the development of algorithms that enable machines to learn from data. In other words, ML involves training machines to recognize patterns in data, and then using those patterns to make predictions about new data.
ML is a subset of AI that involves the development of algorithms that enable machines to learn from data. ML algorithms are designed to improve their performance over time by learning from new data. ML can be further classified into three categories:
1. Supervised Learning: This involves training an ML model on a labeled dataset, where the correct output is known, in order to make predictions on new, unseen data.
2. Unsupervised Learning: This involves training an ML model on an unlabeled dataset, where the correct output is not known, in order to discover patterns and relationships in the data.
3. Reinforcement Learning: This involves training an ML model to learn through trial and error by receiving feedback in the form of rewards or penalties.
ML has numerous real-world applications, such as in the financial industry, where it can be used to detect fraud, and in the marketing industry, where it can be used to personalize advertising.
What is Deep Learning?
Deep Learning is a subset of ML that involves the development of neural networks. Neural networks are algorithms that are designed to mimic the structure of the human brain, with multiple layers of interconnected nodes.
Deep Learning involves training these neural networks on large amounts of data, allowing them to learn complex patterns and make accurate predictions. Deep Learning is particularly useful in areas such as image and speech recognition, where the data is highly complex and difficult to analyze using traditional machine learning algorithms.
DL algorithms are designed to simulate the way the human brain works by using multiple layers of interconnected nodes to learn from data. DL is particularly well-suited for tasks such as image recognition, speech recognition, and natural language processing.
DL has been instrumental in the development of AI systems that can perform tasks that were previously thought to be impossible for machines, such as beating human players in games like Go and Chess or identifying objects in images with near-human levels of accuracy.
In conclusion, AI, ML, and DL are related but distinct technologies that are transforming the way we live and work. AI is the broadest term, encompassing any machine that can simulate human intelligence, while ML is a subset of AI that involves the development of algorithms that enable machines to learn from data. DL is a subset of ML that involves the use of neural networks to learn complex patterns and make accurate predictions. By understanding the differences between these technologies, we can better appreciate their real-world applications and the impact they are having on society. Data science technical interview questions can help you to understand more about this broad topic.
Deep Learning has numerous real-world applications, such as in the automotive industry, where it can be used to develop autonomous vehicles, and in the healthcare industry, where it can be used to analyze medical images.
Differences Between AI, ML, and DL

Although AI, ML, and DL are related, there are some key differences between them.
1. Scope
AI is the broadest term of the three, encompassing any machine that can simulate human intelligence. ML is a subset of AI, focused specifically on machines that can learn from data. DL is a subset of ML, focused specifically on neural networks.
2. Learning
AI and ML can both involve various types of learning, such as supervised, unsupervised, and reinforcement learning. However, DL is specifically focused on the use of neural networks, which can learn through a process called backpropagation.
3. Complexity
AI can be either simple or complex, depending on the task it is designed to perform. ML algorithms can be more complex than traditional algorithms, but they are generally less complex than DL algorithms. DL algorithms can be extremely complex, with many layers of interconnected nodes, making them well-suited for tasks that involve highly complex data, such as image and speech recognition.
4. Performance
AI and ML can both be used to solve a wide range of problems, but their performance is often limited by the quality of the data and the algorithm being used. DL, on the other hand, has shown to be extremely effective in solving complex problems, often outperforming traditional machine learning algorithms.
5. Data requirements
ML algorithms require a large amount of data to learn from and make predictions accurately. DL algorithms require even larger amounts of data, and the data must be highly structured to work effectively.
6. Computing power requirements
DL algorithms require massive amounts of computing power to train, making them computationally expensive. ML algorithms require less computing power than DL but can still be computationally demanding.
7. Interpretability
ML algorithms are generally more interpretable than DL algorithms, meaning that it’s easier to understand how they arrived at their predictions or decisions. DL algorithms can be more opaque, making it challenging to understand how they arrived at their conclusions.
8. Applications
AI has many applications, including speech recognition, natural language processing, computer vision, and robotics. ML is used in many applications, including fraud detection, recommendation systems, and image recognition. DL is used in applications like autonomous driving, speech recognition, and image and video recognition.
9. Training time:

DL algorithms require more time to train than ML algorithms due to the large amount of data and computing power required. ML algorithms can be trained relatively quickly.
Real-World Examples
Let’s look at some real-world examples of how AI, ML, and DL are being used today.
Artificial intelligence:
1. Siri and other voice assistants, which use natural language processing and machine learning to understand and respond to user queries.
2. Chatbots, which use AI to simulate human conversation and provide customer support or assistance.
3. Tesla’s Autopilot, which uses a combination of sensors, computer vision, and deep learning algorithms to enable semi-autonomous driving.
Machine Learning:
1. Fraud detection systems, which use machine learning algorithms to analyze transaction data and identify potentially fraudulent activity.
2. Product recommendation systems used by e-commerce sites, which use machine learning to analyze user data and provide personalized recommendations.
3. Spam filters used by email providers, which use machine learning to analyze email content and identify and filter out spam messages.
Deep Learning:
1. Facial recognition systems, which use deep learning algorithms to analyze facial features and identify individuals.
2. Image recognition systems used in autonomous vehicles, which use deep learning to analyze camera feeds and identify objects and obstacles in the vehicle’s environment.
3. Natural language processing systems, which use deep learning to analyze and understand human language and perform tasks such as language translation or sentiment analysis.
These examples demonstrate the diverse range of applications for AI, ML, and DL in various industries, including transportation, e-commerce, security, and customer service. They also illustrate how these technologies are being used to automate and optimize complex processes and tasks that were once performed exclusively by humans.
Conclusion
AI, ML, and DL are three related but distinct technologies that are transforming the way we live and work. AI is the broadest term, encompassing any machine that can simulate human intelligence, while ML is a subset of AI that involves the development of algorithms that enable machines to learn from data. DL is a subset of ML that involves the use of neural networks to learn complex patterns and make accurate predictions.
They have distinct differences in terms of data requirements, complexity, interpretability, processing power, and application areas. Understanding these differences can help organizations choose the right technology for their specific needs and optimize the performance of their AI systems.

When I was first setting up my resume, I was constantly advised that Microsoft Office was the best skill I could list. A new study shows that job applicants may want to add ChatGPT instead.

Udemy, an online learning platform, compiled a Global Workplace Learning Index in which the company analyzes its course consumption to see what skills businesses are the most interested in.

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The index showed that the top global tech skill for businesses in the first quarter of 2023 was ChatGPT, which experienced a 4,419% increase in global topic consumption from the fourth quarter of 2022.

Other AI-focused skills such as Azure Machine Learning (281%) and AI art generation (239%) also saw significant increases, landing them spots within the top 10 global tech skills.

The company compiled this data by comparing the consumption of courses in the Udemy Business collection from the fourth quarter of 2022 to the first quarter of 2023.

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"Having a comprehensive understanding of ChatGPT and other emerging AI technologies will be imperative to quickly pivot in today's era of rapid digital transformation," said Diego Davila, Udemy Instructor, Entrepreneur & Social Media Innovator.

The U.S. saw an overall 5,226% increase in ChatGPT topic consumption, highlighting a growing interest businesses are showing in learning more about the topic.

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