#OpenAI for business data analysis

[probablyasocialecologist]

Electricity consumption at US data centers alone is poised to triple from 2022 levels, to as much as 390 terawatt hours by the end of the decade, according to Boston Consulting Group. That’s equal to about 7.5% of the nation’s projected electricity demand. “We do need way more energy in the world than we thought we needed before,” Sam Altman, chief executive officer of OpenAI, whose ChatGPT tool has become a global phenomenon, said at the World Economic Forum in Davos, Switzerland last week. “We still don’t appreciate the energy needs of this technology.” For decades, US electricity demand rose by less than 1% annually. But utilities and grid operators have doubled their annual forecasts for the next five years to about 1.5%, according to Grid Strategies, a consulting firm that based its analysis on regulatory filings. That’s the highest since the 1990s, before the US stepped up efforts to make homes and businesses more energy efficient. It’s not just the explosion in data centers that has power companies scrambling to revise their projections. The Biden administration’s drive to seed the country with new factories that make electric cars, batteries and semiconductors is straining the nation’s already stressed electricity grid. What’s often referred to as the biggest machine in the world is in reality a patchwork of regional networks with not enough transmission lines in places, complicating the job of bringing in new power from wind and solar farms. To cope with the surge, some power companies are reconsidering plans to mothball plants that burn fossil fuels, while a few have petitioned regulators for permission to build new gas-powered ones. That means President Joe Biden’s push to bolster environmentally friendly industries could end up contributing to an increase in emissions, at least in the near term. Unless utilities start to boost generation and make it easier for independent wind and solar farms to connect to their transmission lines, the situation could get dire, says Ari Peskoe, director of the Electricity Law Initiative at Harvard Law School. “New loads are delayed, factories can’t come online, our economic growth potential is diminished,” he says. “The worst-case scenario is utilities don’t adapt and keep old fossil-fuel capacity online and they don’t evolve past that.”

archive.today article link

[mariacallous]

As media companies haggle licensing deals with artificial intelligence powerhouses like OpenAI that are hungry for training data, they’re also throwing up a digital blockade. New data shows that over 88 percent of top-ranked news outlets in the US now block web crawlers used by artificial intelligence companies to collect training data for chatbots and other AI projects. One sector of the news business is a glaring outlier, though: Right-wing media lags far behind their liberal counterparts when it comes to bot-blocking.

Data collected in mid-January on 44 top news sites by Ontario-based AI detection startup Originality AI shows that almost all of them block AI web crawlers, including newspapers like The New York Times, The Washington Post, and The Guardian, general-interest magazines like The Atlantic, and special-interest sites like Bleacher Report. OpenAI’s GPTBot is the most widely-blocked crawler. But none of the top right-wing news outlets surveyed, including Fox News, the Daily Caller, and Breitbart, block any of the most prominent AI web scrapers, which also include Google’s AI data collection bot. Pundit Bari Weiss’ new website The Free Press also does not block AI scraping bots.

Most of the right-wing sites didn’t respond to requests for comment on their AI crawler strategy, but researchers contacted by WIRED had a few different guesses to explain the discrepancy. The most intriguing: Could this be a strategy to combat perceived political bias? “AI models reflect the biases of their training data,” says Originality AI founder and CEO Jon Gillham. “If the entire left-leaning side is blocking, you could say, come on over here and eat up all of our right-leaning content.”

Originality tallied which sites block GPTbot and other AI scrapers by surveying the robots.txt files that websites use to inform automated web crawlers which pages they are welcome to visit or barred from. The startup used Internet Archive data to establish when each website started blocking AI crawlers; many did so soon after OpenAI announced its crawler would respect robots.txt flags in August 2023. Originality’s initial analysis focused on the top news sites in the US, according to estimated web traffic. Only one of those sites had a significantly right-wing perspective, so Originality also looked at nine of the most well-known right-leaning outlets. Out of the nine right-wing sites, none were blocking GPTBot.

Bot Biases

Conservative leaders in the US (and also Elon Musk) have expressed concern that ChatGPT and other leading AI tools exhibit liberal or left-leaning political biases. At a recent hearing on AI, Senator Marsha Blackburn recited an AI-generated poem praising President Biden as evidence, claiming that generating a similar ode to Trump was impossible with ChatGPT. Right-leaning outlets might see their ideological foes’ decisions to block AI web crawlers as a unique opportunity to redress the balance.

David Rozado, a data scientist based in New Zealand who developed an AI model called RightWingGPT to explore bias he perceived in ChatGPT, says that’s a plausible-sounding strategy. “From a technical point of view, yes, a media company allowing its content to be included in AI training data should have some impact on the model parameters,” he says.

However, Jeremy Baum, an AI ethics researcher at UCLA, says he’s skeptical that right-wing sites declining to block AI scraping would have a measurable effect on the outputs of finished AI systems such as chatbots. That’s in part because of the sheer volume of older material AI companies have already collected from mainstream news outlets before they started blocking AI crawlers, and also because AI companies tend to hire liberal-leaning employees.

“A process called reinforcement learning from human feedback is used right now in every state-of-the-art model,” to fine-tune its responses, Baum says. Most AI companies aim to create systems that appear neutral. If the humans steering the AI see an uptick of right-wing content but judge it to be unsafe or wrong, they could undo any attempt to feed the machine a certain perspective.

OpenAI spokesperson Kayla Wood says that in pursuit of AI models that “deeply represent all cultures, industries, ideologies, and languages” the company uses broad collections of training data. “Any one sector—including news—and any single news site is a tiny slice of the overall training data, and does not have a measurable effect on the model’s intended learning and output,” she says.

Rights Fights

The disconnect in which news sites block AI crawlers could also reflect an ideological divide on copyright. The New York Times is currently suing OpenAI for copyright infringement, arguing that the AI upstart’s data collection is illegal. Other leaders in mainstream media also view this scraping as theft. Condé Nast CEO Roger Lynch recently said at a Senate hearing that many AI tools have been built with “stolen goods.” (WIRED is owned by Condé Nast.) Right-wing media bosses have been largely absent from the debate. Perhaps they quietly allow data scraping because they endorse the argument that data scraping to build AI tools is protected by the fair use doctrine?

For a couple of the nine right-wing outlets contacted by WIRED to ask why they permitted AI scrapers, their responses pointed to a different, less ideological reason. The Washington Examiner did not respond to questions about its intentions but began blocking OpenAI’s GPTBot within 48 hours of WIRED’s request, suggesting that it may not have previously known about or prioritized the option to block web crawlers.

Meanwhile, the Daily Caller admitted that its permissiveness toward AI crawlers had been a simple mistake. “We do not endorse bots stealing our property. This must have been an oversight, but it's being fixed now,” says Daily Caller cofounder and publisher Neil Patel.

Right-wing media is influential, and notably savvy at leveraging social media platforms like Facebook to share articles. But outlets like the Washington Examiner and the Daily Caller are small and lean compared to establishment media behemoths like The New York Times, which have extensive technical teams.

Data journalist Ben Welsh keeps a running tally of news websites blocking AI crawlers from OpenAI, Google, and the nonprofit Common Crawl project whose data is widely used in AI. His results found that approximately 53 percent of the 1,156 media publishers surveyed block one of those three bots. His sample size is much larger than Originality AI’s and includes smaller and less popular news sites, suggesting outlets with larger staffs and higher traffic are more likely to block AI bots, perhaps because of better resourcing or technical knowledge.

At least one right-leaning news site is considering how it might leverage the way its mainstream competitors are trying to stonewall AI projects to counter perceived political biases. “Our legal terms prohibit scraping, and we are exploring new tools to protect our IP. That said, we are also exploring ways to help ensure AI doesn’t end up with all of the same biases as the establishment press,” Daily Wire spokesperson Jen Smith says. As of today, GPTBot and other AI bots were still free to scrape content from the Daily Wire.

[ziyadnazem]

AI and ChatGPT

Artificial Intelligence (AI) and Natural Language Processing (NLP) have become essential tools in modern society. ChatGPT, a large language model developed by OpenAI, has made significant strides in various industries, including STEM and business. Its ability to understand and generate human-like text has the potential to revolutionize many industries and replace traditional methods of data analysis and customer service. However, this technology also raises concerns about privacy and potential job loss, leading some countries to consider banning its use.

AI is a branch of computer science that aims to create intelligent machines that can perform tasks that typically require human intelligence, such as learning, reasoning, and decision-making. NLP is a subfield of AI that focuses on the interaction between computers and human language, allowing machines to understand and generate natural language text.

Creative destruction is a process in which new technologies or innovations replace existing ones, often resulting in the destruction of traditional business models and industries. This process can lead to significant benefits in terms of efficiency and productivity, but it can also have negative consequences, such as job losses and social upheaval.

ChatGPT is an excellent example of how AI and NLP can be creative destruction. Its ability to generate human-like text has the potential to revolutionize many industries and replace traditional methods of data analysis and customer service. In STEM, ChatGPT has proven to be an invaluable tool for researchers and scientists, allowing them to generate hypotheses, analyze data, and make predictions. In the business world, ChatGPT is being used to improve customer service and enhance marketing strategies.

However, the use of ChatGPT also raises concerns about privacy and potential job loss. In industries such as customer service and data analysis, companies may be tempted to rely solely on the use of AI tools such as ChatGPT, potentially replacing human employees. Additionally, the use of ChatGPT in certain fields, such as journalism, has sparked concerns about the authenticity of news articles and the potential for misinformation.

One country that has already started the process of banning ChatGPT is Italy. The country's data protection authority has expressed concerns about the potential misuse of the technology and has called for a ban on its use. This has sparked a debate about the ethics and regulation of AI technologies and the potential impact they may have on society.

Despite its potential benefits, the use of ChatGPT also raises concerns about privacy and the potential misuse of AI technologies. Its ability to generate human-like text raises questions about the potential for the creation of deepfakes and the manipulation of text for malicious purposes. Additionally, the vast amount of data required to train such models raises concerns about the security and privacy of personal information.

In conclusion, AI and NLP technologies such as ChatGPT can be incredibly powerful tools for businesses and researchers. However, their use must be carefully considered to avoid negative consequences such as job losses and privacy concerns. By working together to develop regulations and guidelines, society can ensure that these technologies are used safely and responsibly, while also reaping the benefits of creative destruction. As the technology continues to advance, it is essential to carefully consider its ethical implications and the potential risks associated with its use.

Author Note: This entire post was written by ChatGPT through prompt engineering.

[digitalsoftware]

GeminAi Review 2024 - World's 1st True Google's Gemini Powered App

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[lemonbarski]

Generate corporate profiles rich with data with CorporateBots from @Lemonbarski on POE.

It’s free to use with a free POE AI account. Powered by GPT3 from OpenAI, the CorporateBots are ready to compile comprehensive corporate data files in CSV format - so you can read it and so can your computer.

Use cases: Prospecting, SWOT analysis, Business Plans, Market Assessment, Competitive Threat Analysis, Job Search.

Each of the CorporateBots series by Lemonbarski Labs by Steven Lewandowski (@Lemonbarski) provides a piece of a comprehensive corporate profile for leaders in an industry, product category, market, or sector.

Combine the datasets for a full picture of a corporate organization and begin your project with a strong, data-focused foundation and a complete picture of a corporate entity’s business, organization, finances, and market position.

Lemonbarski Labs by Steven Lewandowski is the Generative AI Prompt Engineer of CorporateBots on POE | Created on the POE platform by Quora | Utilizes GPT-3 Large Language Model Courtesy of OpenAI | https://lemonbarski.com | https://Stevenlewandowski.us | Where applicable, copyright 2023 Lemonbarski Labs by Steven Lewandowski

Steven Lewandowski is a creative, curious, & collaborative marketer, researcher, developer, activist, & entrepreneur based in Chicago, IL, USA

Find Steven Lewandowski on social media by visiting https://Stevenlewandowski.us/connect | Learn more at https://Steven.Lemonbarski.com or https://stevenlewandowski.us

[transformhubb]

10 Breakthrough Technologies & Their Use Cases in 2023

Today's technology is developing quickly, enabling quicker changes & advancements and accelerating the rate of change. 

For instance, the advancements in machine learning (ML) and natural language processing (NLP) have made artificial intelligence (AI) more common in 2023, as part of a digital transformation solutions. 

Technology is still one of the main drivers of global development. Technological advancements provide businesses with greater opportunities to increase efficiency and develop new products. 

Business leaders can make better plans by keeping an eye on the development of new technologies, foreseeing how businesses might use them, and comprehending the factors that influence innovation and adoption, even though it is still difficult to predict how technology trends will pan out. 

Here are the top 10 emerging technology trends you must watch for in 2023.1. AI that creates graphics and assists with payment

The year of the AI artist is now. With just a few language cues, software models created by Google, OpenAI, and others can now produce beautiful artwork. 

You may quickly receive an image of almost anything after typing in a brief description of it. Nothing will ever be the same. 

A variety of industries, including advertising, architecture, fashion, and entertainment, now employ AI-generated art. 

Realistic visuals and animations are made using AI algorithms. Also, new genres of poetry and music are being created using AI-generated art. 

Moreover, AI will simplify the purchasing and delivery of products and services for customers. 

Nearly every profession and every business function across all sectors will benefit from AI. 

The convenience trends of buy-online-pickup-at-curbside (BOPAC), buy-online-pickup-in-store (BOPIS), and buy-online-return-in-store (BORIS) will become the norm as more retailers utilize AI to manage and automate the intricate inventory management operations that take place behind the scenes. 2. Progress in Web3

Also, 2023 is witnessing a huge advancement in blockchain technology as businesses produce more decentralized products and services. 

We now store everything on the cloud, for instance, but if we decentralized data storage and encrypted that data using blockchain, our information would not only be secure but also have novel access and analysis methods. 

In the coming year, non-fungible tokens (NFTs) will be easier to use and more useful. 

For instance, NFT concert tickets may provide you access to behind activities and artifacts.  

NFTs might represent the contracts we sign with third parties or they could be the keys we use to engage with a variety of digital goods and services we purchase. 3. Datafication

The breakthroughs described in the list of technological trends for 2023 will inevitably lead to the datafication of many businesses. 

The act of converting or changing human jobs into data-driven technology is referred to as the process. 

It is the first important development toward a fully data-driven society. Other branches of the same customer-centric analytical culture include workforce analytics, product behavior analytics, transportation analytics, health analytics, etc.  

Due to the vast number of linked Internet of Things (IoT) devices, it is possible to analyze a company's strengths, weaknesses, risks, and opportunities using a greater number of data points. 

According to Fittech, when the market for datafying sectors surpasses $11 billion in 2022, it is evolving into a profitable business model. 4. Certain aspects of the Metaverse will become actual 

The term "metaverse" has evolved to refer to a more immersive internet in which we will be able to work, play, and interact with one another on a persistent platform. 

According to experts, the metaverse will contribute $5 trillion to the world economy by 2030, and 2023 is the year that determines the metaverse's course for the next ten years. 

The fields of augmented reality (AR) and virtual reality (VR) will develop further. 

In the coming year, avatar technology will also progress. If motion capture technology is used, avatars will even be able to mimic our body language and movements. An avatar is a presence we portray when we interact with other users in the metaverse. 

Further advancements in autonomous AI-enabled avatars that can represent us in the metaverse even when we aren't signed in to the virtual world may also be on the horizon. 

To perform training and onboarding, businesses are already utilizing metaverse technologies like AR and VR, and this trend will pick up steam in 2023. 5. Bridging the digital & physical world

The digital and physical worlds are already beginning to converge, and this tendency will continue in 2023. This union consists of two parts: 3D printing and digital twin technologies. 

Digital twins are virtual models of actual activities, goods, or processes that may be used to test novel concepts in a secure online setting. 

To test under every scenario without incurring the enormous expenses of real-world research, designers, and engineers are adopting digital twins to replicate actual things in virtual environments. 

We are witnessing even more digital twins in 2023, in everything from precise healthcare to machinery, autos, and factories. This is a part of the best digital transformation solutions in this new era. 

Engineers may make adjustments and alter components after testing them in the virtual environment before employing 3D printing technology to produce them in the actual world. 6. More human-like robots are coming

Robots will resemble humans even more in 2023, both in terms of look and functionality.  

These robots will serve as event greeters, bartenders, concierges, and senior citizens' companions in the real world. 

While they collaborate with people in production and logistics, they will also carry out complicated duties in factories and warehouses. 

One business, Tesla, is working hard to develop a humanoid robot that will operate in our homes. 

Two Optimus humanoid robot prototypes were unveiled by Elon Musk, who also stated that the business will be prepared to accept orders in the next few years. 

The robot is capable of carrying out simple duties like watering plants and lifting objects. 7. Digitally Immune Systems

The launch of the Digital Immune System must be included in any list of technological trends for 2023. 

This system alludes to an architecture made up of techniques taken from the fields of software design, automation, development, operations, and analytics. By eliminating flaws, threats, and system weaknesses, it tries to reduce company risks and improve customer satisfaction. 

The significance of DIS resides in automating the many components of a software system to successfully thwart virtual attacks of every description. 

According to Gartner, businesses that have already implemented DIS will reduce customer downtime by around 80% by 2025. 

So, if you are looking for the best digital transformation services company to introduce digital immune systems, TransformHub is here to guide you. 8. Genomics

Genomic research has improved our grasp of life and contemporary health analytics while also advancing our understanding of brain networks. 

In the upcoming years, fast-developing technologies such as scarless genome editing, pathogen intelligence, and NGS data analysis platforms will use AI to interpret hidden genetic codes and patterns, elevating genomic data analysis and metagenomics to the top positions in the biotech sector.  

Functional genomics, which uses epigenome editing to reveal the influence of intergenic areas on biological processes, is becoming more prevalent in 2023 technology trends. 9. CRISPR 

The gene-editing technology, CRISPR, has quickly moved from the lab to the clinic during the past ten years. 

Clinical trials for common illnesses, such as excessive cholesterol, have lately been included. It originally started with experimental therapies for uncommon genetic abnormalities and might advance things much further with new variants. 

Due to its ease of usage, CRISPR is quickly becoming a common technology employed in many cancer biology investigations. 

Moreover, CRISPR is entirely adaptable. It is more accurate than existing DNA-editing techniques and can essentially modify any DNA segment within the 3 billion letters of the human genome. 

The simplicity of scaling up CRISPR is an additional benefit. 

To control and analyze hundreds or thousands of genes at once, researchers can utilize hundreds of guides RNAs. This kind of experiment is frequently used by cancer researchers to identify genes that might be potential therapeutic targets. 10. Growth of Green Technology 

Climate change is a fact. It is a rising issue that disturbs governments and society at large and poses a threat to human health and the environment. 

The use of so-called green technology is one method of combating global warming. 

Globally, scientists and engineers are working on technical solutions to reduce and get rid of everything that contributes to climate change and global warming. 

Here are some incredible uses for the same: 

Emissions reduction 

Waste-to-Energy 

Management of waste and recycling 

Biofuels 

Treatment of wastewater 

Solar power 

Tidal and wave power 

Green vehicles 

Smart structures 

Farms and gardens in the air 

TransformHub: Keeping Ahead of Technological Trends 

These innovations have the power to completely alter the way we live, work, and interact. It's critical to be informed about these changes and take their effects into account. 

The epidemic has sped up the necessary industry-wide human-AI collaboration and it looks like 2023 will be the year we catalyze this cooperation into some truly extraordinary inventions. 

For more information on how contemporary automation and AI are fusing all the defining industries of our era into a single data-driven civilization, stay up-to-date with one of the best digital transformation companies in Singapore, TransformHub. 

We take complete accountability to digitally transform your business by providing precisely tailored solutions based entirely on your requirements. 

Let’s connect and bring your vision to life!

[tastydregs]

GPT-4 will hunt for trends in medical records thanks to Microsoft and Epic

Enlarge / An AI-generated image of a pixel art hospital with empty windows.

Benj Edwards / Midjourney

On Monday, Microsoft and Epic Systems announced that they are bringing OpenAI's GPT-4 AI language model into health care for use in drafting message responses from health care workers to patients and for use in analyzing medical records while looking for trends.

Epic Systems is one of America's largest health care software companies. Its electronic health records (EHR) software (such as MyChart) is reportedly used in over 29 percent of acute hospitals in the United States, and over 305 million patients have an electronic record in Epic worldwide. Tangentially, Epic's history of using predictive algorithms in health care has attracted some criticism in the past.

In Monday's announcement, Microsoft mentions two specific ways Epic will use its Azure OpenAI Service, which provides API access to OpenAI's large language models (LLMs), such as GPT-3 and GPT-4. In layperson's terms, it means that companies can hire Microsoft to provide generative AI services for them using Microsoft's Azure cloud platform.

The first use of GPT-4 comes in the form of allowing doctors and health care workers to automatically draft message responses to patients. The press release quotes Chero Goswami, chief information officer at UW Health in Wisconsin, as saying, "Integrating generative AI into some of our daily workflows will increase productivity for many of our providers, allowing them to focus on the clinical duties that truly require their attention."

The second use will bring natural language queries and "data analysis" to SlicerDicer, which is Epic's data-exploration tool that allows searches across large numbers of patients to identify trends that could be useful for making new discoveries or for financial reasons. According to Microsoft, that will help "clinical leaders explore data in a conversational and intuitive way." Imagine talking to a chatbot similar to ChatGPT and asking it questions about trends in patient medical records, and you might get the picture.

GPT-4 is a large language model (LLM) created by OpenAI that has been trained on millions of books, documents, and websites. It can perform compositional and translation tasks in text, and its release, along with ChatGPT, has inspired a rush to integrate LLMs into every type of business, whether appropriate or not.

[seoprosafe]

The Future of Digital Marketing is Here: Top AI Websites to Keep on Your Radar

OpenAI – a research company that aims to develop and promote friendly AI in a responsible way.

KDNuggets – a website that provides news, articles, and tutorials on data science, machine learning, and artificial intelligence.

AI Expo – a conference and expo that focuses on the practical applications of AI and its impact on businesses.

AI Time Journal – a publication that covers the latest AI news, research, and trends.

The AI Hub – a website that provides resources and information on AI for businesses and professionals.

AI News – a website that provides the latest news and analysis on AI, machine learning, and deep learning.

AI Trends – a website that provides news, analysis, and research on AI and its impact on various industries.

AI Business – a website that provides information and resources for businesses looking to implement AI technology.

AI-Techpark – a website that provides information, resources, and a community for professionals and companies in the AI industry.

AI World – a conference and expo that focuses on the practical applications and implications of AI for businesses and society.

Source-- https://thesocialocean.com/

[jcmarchi]

Maximizing AI ROI in the Enterprise

New Post has been published on https://thedigitalinsider.com/maximizing-ai-roi-in-the-enterprise/

Maximizing AI ROI in the Enterprise

As has been the case with numerous technologies before it, artificial intelligence (AI) is being hailed as the next great innovation enterprises simply must use. Ironically, the underlying technology has been around for decades, but with the latest iterations, the hype has reached a fever pitch—outpacing the reality of implementation across the enterprise. Yet, as IT teams face increasing pressure to get on board the IT train, they must balance that enthusiasm with the reality of the bottom line. Different implementations require different levels of investment, meaning they must also yield a different return—often on a different timetable.

The ability to deliver successful AI products depends on numerous factors: specific strategies, planning and execution chosen by business leaders; availability of skilled resources; fit within product roadmap; organizational acceptance of risk; and time management against expected return on investment (ROI).

Balancing these factors is the challenge, but following these three steps can keep organizations on the path toward AI ROI.

Understand the Technology

Many enterprises enter the AI fray believing they are behind but not fully understanding why, how, or even what the technology is. As a result, their first task is distinguishing among different flavors of AI, beginning with precision AI vs. generative AI.

Precision AI is the use of machine learning and deep learning models to improve outcomes. It enables enterprises to automate decision-making processes, creating efficiencies and increasing ROI. Precision AI has matured into an established workhorse technology for enterprises that continues to see significant adoption and is becoming more mainstream by the day.

Generative AI (GenAI) is new and has risen to prominence since OpenAI released ChatGPT in late 2022. Consisting of foundational large language models (LLMs) trained with billions of parameters to generate new semantic text context, GenAI offers significant opportunities for business impact and operational efficiency but it’s early in its adoption lifecycle.

One significant hurdle is the standard for data quality, which is elevated for GenAI applications since low-quality datasets can introduce transparency and ethical issues.

Data reliability begins with designing and implementing workflows; establishing pipelines to perform; abstracting through APIs; curating and democratizing; and processing different data types. Rather than the previous generation of data quality requirements that included the 4Vs (volume, velocity, veracity and variety), AI needs new requirements that include 4Ps: prediction, productivity, precision, and persona at scale.

Prediction: AI algorithms allow the use of statistical analysis to find patterns in the data and identify behaviors to predict and forecast future events by correlating historical data at rest and data streaming to make decisions in real-time.

Productivity: AI enables business process automation, which increases enterprise operational efficiency and productivity, reducing repetitive tasks and freeing up staff time to work on more strategic assignments.

Precision: This metric measures the model results in a way that machine learning models can produce accuracy between acceptable range determined by the use cases. Precision is also calculated as the number of true positives divided by total number of positive predictions.

Persona at scale: This refers to the process of using reliable data such as customer purchase histories, on-site actions, customers’ sentiment analysis for specific products and survey responses. It delivers individualized experiences across demographics.

In addition to data quality, enterprises must consider numerous other factors—both internal and external—when evaluating their AI readiness: governance, compliance alignment, cloud investments, talent, new business operations models, risk management, and leadership commitment.

Organizations must begin by establishing an AI vision that matches their goals and strategic objectives. Buy-in from the C-suite is critical, as AI deployments require significant up-front investment. The CIO must clearly articulate the path to ROI to the entire C-suite—a true test of the CIO in elevating IT from an enabling function to a strategic one.

Next, the organization must align people, processes, and technology. AI requires new skills and certifications such as deep learning models and machine learning, as organizations have traditionally integrated AI into human workflows. However, GenAI reverses the dynamic, but most best practices and responsible use guidelines still include a “human in the loop” component to maintain ethical standards and values.

An AI deployment also demands new business processes for governance and data quality assurance, enabling the data scientists responsible for delivering new AI models to solve complex business problems.

As new AI products are designed, developed, and manufactured for production, enterprises must also remain vigilant of the AI industry’s latest regulatory policies. The European AI act has established best practices for using AI—and consequences for not following those policies. As a result, enterprises have constructed teams to create, evaluate and update efforts around AI regulations.

With enterprises becoming increasingly data-driven, they must develop foundational strategies to protect the data assets enabling them to deliver the best insights through analytics process automation platforms. From there, they can select the AI technologies and new platforms that make the most sense for them.

Define the Business Case

Finally, true return on an AI investment requires selling the benefit to customers, meaning AI readiness requires a new business mindset as the technology is driving transformation for enterprises across industries.

Successful AI product development requires an intimate understanding of industry-specific customer journeys and aligning AI solutions with business objectives. Customer centricity plays a key role in developing new operating models, and modern technologies are used to increase efficiency.

For instance, customers looking for small wins in AI maturity can rely on their software assets and cloud infrastructure to develop new products and solutions. This keeps satisfaction among employees higher and maintains their focus on exceeding customer expectations.

That said, the core of the organization should focus on shortening time-to-market and improving new process management to shorten the product development life cycle and increase the efficiency of delivering new products. For example, a distributed augmented data analytics platform is used to automate the ingestion, curation, democratization, processing, and analytics in real-time—all of which increase productivity and ROI.

Unlock the Full Potential of AI ROI

AI at its core stands for advanced algorithms, data quality, computing power, Infrastructure as Code, governance, responsible AI with ethics to protect data privacy and confidentiality. The essentials of AI application readiness and the challenges of data management require hardness data-driven frameworks, people, process, strategy ethics and technology platforms.

Concurrently, Mckinsey reports that 65% of enterprises are using AI technologies—double the number from last year. It demonstrates momentum, but deployments are still moving slowly from curiosity to real business use cases at scale. GenAI is delivering new breakthroughs, enabling organizations to harness new capabilities through the development of semantic and multi-modal LLMs. It democratizes a full spectrum of AI capabilities, enabling them to generate new revenue streams.

With the right strategy, leadership commitment, and investment in the correct use cases, businesses can gain significant value and drive transformative growth through AI.

[techgeniusinsights]

How Large Language Models (LLMs) are Transforming Data Cleaning in 2024

Data is the new oil, and just like crude oil, it needs refining before it can be utilized effectively. Data cleaning, a crucial part of data preprocessing, is one of the most time-consuming and tedious tasks in data analytics. With the advent of Artificial Intelligence, particularly Large Language Models (LLMs), the landscape of data cleaning has started to shift dramatically. This blog delves into how LLMs are revolutionizing data cleaning in 2024 and what this means for businesses and data scientists.

The Growing Importance of Data Cleaning

Data cleaning involves identifying and rectifying errors, missing values, outliers, duplicates, and inconsistencies within datasets to ensure that data is accurate and usable. This step can take up to 80% of a data scientist's time. Inaccurate data can lead to flawed analysis, costing businesses both time and money. Hence, automating the data cleaning process without compromising data quality is essential. This is where LLMs come into play.

What are Large Language Models (LLMs)?

LLMs, like OpenAI's GPT-4 and Google's BERT, are deep learning models that have been trained on vast amounts of text data. These models are capable of understanding and generating human-like text, answering complex queries, and even writing code. With millions (sometimes billions) of parameters, LLMs can capture context, semantics, and nuances from data, making them ideal candidates for tasks beyond text generation—such as data cleaning.

To see how LLMs are also transforming other domains, like Business Intelligence (BI) and Analytics, check out our blog How LLMs are Transforming Business Intelligence (BI) and Analytics.

Traditional Data Cleaning Methods vs. LLM-Driven Approaches

Traditionally, data cleaning has relied heavily on rule-based systems and manual intervention. Common methods include:

Handling missing values: Methods like mean imputation or simply removing rows with missing data are used.

Detecting outliers: Outliers are identified using statistical methods, such as standard deviation or the Interquartile Range (IQR).

Deduplication: Exact or fuzzy matching algorithms identify and remove duplicates in datasets.

However, these traditional approaches come with significant limitations. For instance, rule-based systems often fail when dealing with unstructured data or context-specific errors. They also require constant updates to account for new data patterns.

LLM-driven approaches offer a more dynamic, context-aware solution to these problems.

How LLMs are Transforming Data Cleaning

1. Understanding Contextual Data Anomalies

LLMs excel in natural language understanding, which allows them to detect context-specific anomalies that rule-based systems might overlook. For example, an LLM can be trained to recognize that “N/A” in a field might mean "Not Available" in some contexts and "Not Applicable" in others. This contextual awareness ensures that data anomalies are corrected more accurately.

2. Data Imputation Using Natural Language Understanding

Missing data is one of the most common issues in data cleaning. LLMs, thanks to their vast training on text data, can fill in missing data points intelligently. For example, if a dataset contains customer reviews with missing ratings, an LLM could predict the likely rating based on the review's sentiment and content.

A recent study conducted by researchers at MIT (2023) demonstrated that LLMs could improve imputation accuracy by up to 30% compared to traditional statistical methods. These models were trained to understand patterns in missing data and generate contextually accurate predictions, which proved to be especially useful in cases where human oversight was traditionally required.

3. Automating Deduplication and Data Normalization

LLMs can handle text-based duplication much more effectively than traditional fuzzy matching algorithms. Since these models understand the nuances of language, they can identify duplicate entries even when the text is not an exact match. For example, consider two entries: "Apple Inc." and "Apple Incorporated." Traditional algorithms might not catch this as a duplicate, but an LLM can easily detect that both refer to the same entity.

Similarly, data normalization—ensuring that data is formatted uniformly across a dataset—can be automated with LLMs. These models can normalize everything from addresses to company names based on their understanding of common patterns and formats.

4. Handling Unstructured Data

One of the greatest strengths of LLMs is their ability to work with unstructured data, which is often neglected in traditional data cleaning processes. While rule-based systems struggle to clean unstructured text, such as customer feedback or social media comments, LLMs excel in this domain. For instance, they can classify, summarize, and extract insights from large volumes of unstructured text, converting it into a more analyzable format.

For businesses dealing with social media data, LLMs can be used to clean and organize comments by detecting sentiment, identifying spam or irrelevant information, and removing outliers from the dataset. This is an area where LLMs offer significant advantages over traditional data cleaning methods.

For those interested in leveraging both LLMs and DevOps for data cleaning, see our blog Leveraging LLMs and DevOps for Effective Data Cleaning: A Modern Approach.

Real-World Applications

1. Healthcare Sector

Data quality in healthcare is critical for effective treatment, patient safety, and research. LLMs have proven useful in cleaning messy medical data such as patient records, diagnostic reports, and treatment plans. For example, the use of LLMs has enabled hospitals to automate the cleaning of Electronic Health Records (EHRs) by understanding the medical context of missing or inconsistent information.

2. Financial Services

Financial institutions deal with massive datasets, ranging from customer transactions to market data. In the past, cleaning this data required extensive manual work and rule-based algorithms that often missed nuances. LLMs can assist in identifying fraudulent transactions, cleaning duplicate financial records, and even predicting market movements by analyzing unstructured market reports or news articles.

3. E-commerce

In e-commerce, product listings often contain inconsistent data due to manual entry or differing data formats across platforms. LLMs are helping e-commerce giants like Amazon clean and standardize product data more efficiently by detecting duplicates and filling in missing information based on customer reviews or product descriptions.

Challenges and Limitations

While LLMs have shown significant potential in data cleaning, they are not without challenges.

Training Data Quality: The effectiveness of an LLM depends on the quality of the data it was trained on. Poorly trained models might perpetuate errors in data cleaning.

Resource-Intensive: LLMs require substantial computational resources to function, which can be a limitation for small to medium-sized enterprises.

Data Privacy: Since LLMs are often cloud-based, using them to clean sensitive datasets, such as financial or healthcare data, raises concerns about data privacy and security.

The Future of Data Cleaning with LLMs

The advancements in LLMs represent a paradigm shift in how data cleaning will be conducted moving forward. As these models become more efficient and accessible, businesses will increasingly rely on them to automate data preprocessing tasks. We can expect further improvements in imputation techniques, anomaly detection, and the handling of unstructured data, all driven by the power of LLMs.

By integrating LLMs into data pipelines, organizations can not only save time but also improve the accuracy and reliability of their data, resulting in more informed decision-making and enhanced business outcomes. As we move further into 2024, the role of LLMs in data cleaning is set to expand, making this an exciting space to watch.

Large Language Models are poised to revolutionize the field of data cleaning by automating and enhancing key processes. Their ability to understand context, handle unstructured data, and perform intelligent imputation offers a glimpse into the future of data preprocessing. While challenges remain, the potential benefits of LLMs in transforming data cleaning processes are undeniable, and businesses that harness this technology are likely to gain a competitive edge in the era of big data.

[creativesgenie]

AI Showdown: GPT 4o VS GPT 4o Mini - What's The Difference?

OpenAI’s popular chatbot ChatGPT was released in December 2022 and gained worldwide attention right after, since then OpenAI has rolled out new models to update the existing chatbot. The latest version of Chat GPT is ChatGPT 4o, which was soon followed by ChatGPT 4o mini. 

ChatGPT4o:

As the latest version of Chat GPT, GPT4o is an advanced language model based on OpenAI’s unique GPT-4 architecture. It is designed to execute complicated tasks such as generating human-like text and performing complex data analysis and processing. Due to this, major computer resources and data processing are required. Due to this, GPT 4o pricing is also high.

ChatGPT4o Mini:

GPT 4o mini is a smaller model based on the same architecture as GPT-4, however, it sacrifices some performance for greater convenience and less extensive data processing. This makes it suitable for more straightforward and basic tasks and projects.

So, If GPT 4o mini is a smaller version of GPT 4o, what's the difference?

Both models are known for their natural language processing capabilities, executing codes, and reasoning tasks. However, the key difference between both is their size, capabilities, compatibility, and cost. 

As the latest version of Chat GPT, GPT-4o is capable of generating human-like text, and solving complex problems, much like its predecessors, however with the release of ChatGPT-4o, OpenAI took a new step towards more natural human-computer interaction –- it accepts data through a combination of text, audio, image, and video and replies with the same kind of output. 

ChatGPT-4o mini can only accept and give outputs in the form of text and vision in the API.

Due to its grand size and capabilities, GPT 4o pricing is more expensive and it is harder to use and maintain, making it a better choice for larger enterprises that have the budget to support it.

Due to being a smaller model and a cost-effective alternative, GPT-4o Mini provides vital functionalities at a lower price, making it accessible to smaller businesses and startups.

ChatGPT 4o allows you to create projects involving complicated text generation, detailed and comprehensive content creation, or sophisticated data analysis. This is why for larger businesses and enterprises, GPT-4o is the better choice due to its superior abilities.

ChatGPT 4o mini is more suited for simpler tasks, such as basic customer interactions or creating straightforward content, it can even help students prepare for an exam. GPT-4o Mini can provide accurate information with smooth performance without overextending your resources.

Pricing: The cost comparison between both models shows what you really need when you're working with limited resources or need extensive computational resources.

GPT 4o pricing costs $15.00 / 1M output tokens.

While ChatGPT 4o mini is at $0.600 / 1M output tokens.

Which Is Better? Which of the two is better really comes down to your individual needs as the latest version of Chat GPT, GPT-4o is excellent at complex tasks requiring the most accurate level of performance and advanced capabilities. As mentioned above, it is costly and may require more effort to use and maintain.

ChatGPT 4o mini is an alternative that balances performance and cost while providing most of the benefits of GPT 4o. It can carry out small but complicated tasks that do not require comprehensive resources and details.

Hence which is better of the two comes down to what you're using it for, are you a physicist or a business looking to work with quantum mechanics and create detailed projects, or are you a student or an individual who wants to explore the capabilities of AI? Explore which version of Chat GPT is ideal for your needs with the assistance of experts at Creative’s Genie. Contact our team today.

[bluprinttechblogs]

Character AI: Detailed Features, Abilities and Application

What is Character AI

Character AI is a web app that emulates specific personalities, traits, or characters to create interactive and engaging experiences. These AI models can envy any character they are programmed to copy. Unlike other AI models, Character AI has humanlike responses that enable you to converse with the virtual characters. Since it was launched in September 2022 by developers Noam Shazeer and Daniel De Freitas, it is one of the most popular AI chatbots after OpenAI ChatGPT. How does it operate? 

The Features behind Character AI works

Character AI borrows technologies from Artificial Intelligence and machine learning to simulate over 50 million humanlike character behaviours. These human interactions can be through games, virtual environments, and interactive platforms. Let us go through the technologies that it borrows from Artificial Intelligence. They include- Natural Language Processing: The NLP technology enables Character AI to understand and generate human language responses. It can acknowledge written texts or spoken language, depending on how you interact and converse. It applies abilities like tokenization, part-of-speech tagging, and named entity recognition to recognize the languages presented before it. As it replies, Character AI uses the trained generation models to present relevant responses appropriate to the questions. - Machine Learning and Deep Learning: Character AI applies machine learning and Deep learning to learn from data to improve performance. These technologies enable the platform to gain an understanding of experiences. For instance, it can clarify that users prefer concise answers and rectify with time. To simulate human psychology and cognition in feedback, it incorporates Behavioral and Cognitive Models. These models ignite memory and personalities in the AI character system, empowering the characters to remember past interactions. Moreover, the AI web app developers train it to identify emotional cues in your language and respond appropriately through the sentiment analysis models. - Decision Trees and Rule-based Systems: In gaming interactions, the AI character system uses decision trees and rule-based systems to determine how to respond based on pre-defined rules and scenarios. These systems help to maintain consistency with the character’s personality and role in the game. They apply this by ensuring characters are true to their designed persona and storyline. - Feedback Systems: These systems empower Character AI to learn from your responses. Based on your interactions, the AI character system applies reinforcement learning to adjust behaviours depending on positive or negative feedback. These direct replies manually tune and improve the AI app with time. - Integration with Other Systems: In games, the Character AI must merge with Graphical and animation systems to implement the persona's verbal responses to match the suitable physical animations.  -  Enhanced User Experience: This AI character system enables a real-life experience for its characters with AI-driven personalities. You will feel as if you are communicating with real people. When you use it to generate content, like marketing a brand, the AI character app will create unique information to engage and capture consumer interest more effectively. - Improved Industry Production: Character AI is important in streamlining business operations and productions. It automates various operations across different departments in a company. For example, when you market your brand or product, this technology can generate engaging content for your clients and automatically send it to specific emails or social media platforms clients. The style it uses to implement this is quicker, cheaper, and safer, unlike the traditional methods. - Cost Efficient: Rather than employing human staff to handle your company operations, it is economical to use Character AI. For instance, in roles like customer service that require a lot of human resources, the AI character system works much better. The cost of paying the human staff is way higher as compared with the cost of adopting Character AI technology in your company. This move assures more savings for your company. - Stimulates Creativity: Character AI encourages creators to implement more diverse ways to integrate technology and narrative. The AI character system is good at transforming traditional storytelling into a more immersive experience, similar to a living story. This ability is perfect for companies that rely on interactive technology. - Writing Scripts: Draft a script to explain how interactions will happen. The script will enable the platform to create a template for its responses. - Training it with data: In this area, you can either use existing data or create your own. Feed it with examples of text interactions to learn how to respond appropriately. - Test and implement: Test your Character AI on different scenarios to ensure you get the desired results. - Entertainment: The AI character system enables interactive films and show production. Entertainment platforms like Netflix consist of examples where viewers make choices influencing the story direction. The AI system branches these storylines to enable a smooth user experience. Its virtual Actors perform scripted actions or respond to viewers for a unique viewing experience. Furthermore, the AI character system can generate virtual celebrities to interact with real humans on social media. You can use these influencers to produce massive followings through posts, endorsements, and even music videos, all managed by AI systems. - Gaming: The AI character system creates an immersive and engaging gaming experience. When you play, it reacts to your actions. For instance, you will realize adaptive behaviours in a combat game, where your enemies can analyze your strategies and adapt to your tactics in real-time. It can also show emotional responses that are appropriate to your interactions. The AI character system also enables you to interact with characters and control the gameplay through voice commands. - Education: This technology is improving the learning experience in the education industry with innovative techniques. It can act as a virtual tutor, adapting to the pace, content and teaching methods preferred by the student. Its interactive techniques are more engaging, simulating real-life experiences. For instance, students can interact to ask questions, discuss concepts and solve problems. Through this, it can analyze a student's behaviour and provide suggestions for learning materials. These suggestions include educational content like quizzes, exercises and lesson plans. Moreover, the AI character system is always available for students and beneficial to students in rural areas with limited access to traditional education resources. For professional learners, you can use Character AI to train yourself in various working fields. You can use it for training simulations for areas like healthcare, law enforcement, and customer service. While learning, it will provide you with real-life scenarios to improve your skills and decision-making. - Customer Care: Unlike humans, the Character AI is not limited to fatigue. It can handle large volumes of customers for a given period while maintaining high-quality service. It is always available, even at odd hours, and its quick responses do not keep customers waiting. Moreover, its developers empower it to handle a wide range of customer tasks, from answering simple to complex questions. These abilities eliminate the need for human agents to handle customer issues, especially in big industries. - Healthcare: You can merge character AI with health apps to streamline medical services. When you do this, the AI system can help and guide patients through treatment plans, medication schedules, and post-operative care, offering reminders and moral support. For instance, for therapeutic purposes, it provides companionship and conversation to mentally ill patients. This helps in routine check-ups on patients while managing conditions like depression and stress. - Marketing and Branding: When you want to alert customers about a new brand or a product, character AI can be very beneficial. Unlike the traditional approaches where you had to involve human labour, this technology is quicker and more productive. It can interact and engage with customers on social media platforms through conversations. Moreover, it can analyze your customer behaviour and provide recommendations to them.  Related:  Read the full article

[mariacallous]

Earlier this month, OpenAI’s board abruptly fired its popular CEO, Sam Altman. The ouster shocked the tech world and rankled Altman’s loyal employees, the vast majority of whom threatened to quit unless their boss was reinstated. After a chaotic five-day exile, Altman got his old job back—with a reconfigured, all-male board overseeing him, led by ex-Salesforce CEO and former Twitter board chair Bret Taylor.

Right now, only three people sit on this provisional OpenAI board. (More are expected to join.) Immediately prior to the failed coup, there were six. Altman and OpenAI cofounders Greg Brockman and Ilya Sutskever sat alongside Quora CEO Adam D’Angelo; AI safety researcher Helen Toner; and Tasha McCauley, a robotics engineer who leads a 3D-mapping startup.

The specifics of the boardroom overthrow attempt remain a mystery. Of those six, D’Angelo is the only one left standing. In addition to Taylor, the other new board member is former US Treasury secretary Larry Summers, a living emblem of American capitalism who notoriously said in 2005 that innate differences in the sexes may explain why fewer women succeed in STEM careers (he later apologized).

While Altman, Brockman, and Sutskever all still work at OpenAI despite their absence from the board, Toner and McCauley—the two women who sat on the board—are now cut off from the company. As the artificial intelligence startup moves forward, the stark gender imbalance of its revamped board illustrates the precarious position of women in AI.

“What this underscores is that there aren’t enough women in the mix to begin with,” says Margaret O’Mara, a University of Washington history professor and author of The Code: Silicon Valley and the Remaking of America. For O’Mara, the new board reflects Silicon Valley’s power structure, signaling that it’s “back to business” for the world’s most influential AI company—if back to business means a return to the Big Tech boys’ club. (Worth noting that when it was founded in 2015, OpenAI only had two board members: Altman and Elon Musk.)

Prominent AI researcher Timnit Gebru, who was fired by Google in late 2020 over a dispute about a research paper involving critical analysis of large language models, has been floated in the media as a potential board candidate. She is, indeed, a leader in responsible AI; post-Google, she founded the Distributed AI Research Institute, which describes itself as a space where “AI is not inevitable, its harms are preventable.” If OpenAI wanted to signal that it is still committed to AI safety, Gebru would be a savvy choice. Also an impossible one: She does not want a seat on the board of directors.

“It’s repulsive to me,” says Gebru. “I honestly think there’s more of a chance that I would go back to Google—I mean, they won’t have me and I won’t have them—than me going to OpenAI.”

The lack of women in the AI field has been an issue for years; in 2018, WIRED estimated that only 12 percent of leading machine learning researchers were women. In 2020, the World Economic Forum found that only 26 percent of data and AI positions in the workforce are held by women. “AI is very imbalanced in terms of gender,” says Sasha Luccioni, an AI ethics researcher at HuggingFace. “It’s not a very welcoming field for women.”

One of the areas where women are flourishing within the AI industry is in the world of ethics and safety, which Luccioni views as comparatively inclusive. She also sees it as significant that the ousted board members reportedly clashed with Altman over OpenAI’s mission. According to The New York Times, Toner and Altman had bickered over a research paper she published with coauthors in October that Altman interpreted as critical of the company. Luccioni believes that in addition to highlighting gender disparities, this incident also demonstrates how voices advocating for ethical considerations are getting hushed.

“I don’t think they got fired because they’re women,” Luccioni says. “I think they got fired because they highlighted an issue.” (Technically, both women agreed to leave the board.)

No matter what actually spurred the conflict at OpenAI, the way in which it was resolved, with Altman back at the helm and his dissenters out, has played into a narrative: Altman emerging as victor, flanked by loyalists and boosters. His board is now stocked with men eager to commercialize OpenAI’s products, not rein in its technological ambition. (One recent headline capturing this perspective: “AI Belongs to the Capitalists Now.”) Caution espoused by female leadership at least appears to have lost.

O’Mara sees the all-male OpenAI board as a sign of a swinging cultural pendulum. Just as some Silicon Valley tech companies have been working to correct their woeful track records in diversity and consider their environmental footprints, others have recoiled against “wokism” in various forms, instead espousing hard-nosed beliefs about work culture.

“It’s this sentiment around, ‘OK, we’re done being touchy-feely,’” she says. “Whether it’s Elon Musk’s ‘extremely hardcore’ demands or Marc Andreessen’s recent manifesto, the idea is that if you’re calling for people to take a pause and consider potential harms or complaining about the lack of representation, that is orthogonal to their business.”

OpenAI is reportedly planning to expand the board soon, and speculation is rampant about who will join. Its conspicuously all-male and all-white makeup certainly did not go unnoticed, and OpenAI is already looking at prospects who might placate some critics. According to a Bloomberg report, philanthropist Laurene Powell Jobs, former Yahoo CEO Marissa Mayer, and former US Secretary of State Condoleezza Rice were all considered but not selected.

At the time of publication, OpenAI had not responded to repeated requests for comment.

For many onlookers, it’s crucial to choose someone who will advocate balancing ambition with safety and responsibility—someone whose line of inquiry might match that of Toner, for example, rather than someone who simply looks like her. “The sort of people that this board should be bringing back are people who are thinking about responsible or trustworthy technology, and safety,” says Kay Firth-Butterfield, executive director of the Centre for Trustworthy Technology. “There are a lot of women out there who are experts in that particular field.”

As OpenAI searches for new board members, it may meet resistance from prospects wary of the real power dynamics within the company. There are already concerns about tokenization. “I just feel like the person on the board would have a horrible time because they will constantly be fighting an uphill battle,” says Gebru. “Used as a token and not to really make any kind of difference.”

She’s not the only person within the world of AI ethics to question whether new board members would be marginalized. “I wouldn’t touch that board with a ten-foot pole,” Luccioni says. She feels she couldn’t recommend a friend take that sort of position, either. “Such stress!”

Meredith Whittaker, president of messaging app Signal, sees value in bringing someone to the board who isn’t just another startup founder, but she doubts that adding a single woman or person of color will set them up to affect meaningful change. Unless the expanded board is able to genuinely challenge Altman and his allies, packing it with people who tick off demographic boxes to satisfy calls for diversity could amount to little more than “diversity theater.”

“We’re not going to solve the issue—that AI is in the hands of concentrated capital at present—by simply hiring more diverse people to fulfill the incentives of concentrated capital,” Whittaker says. “I worry about a discourse that focuses on diversity and then sets folks up in rooms with [expletive] Larry Summers without much power.”

[jenniferphilop0420]

Top 10 AI Development Companies 2024: Leaders Shaping the Future of Artificial Intelligence

Artificial intelligence (AI) is no longer a futuristic concept; it has seamlessly integrated into our lives, transforming industries like healthcare, finance, e-commerce, and more. Behind these advancements are the AI development companies that are creating cutting-edge AI solutions, AI models, and AI technologies to help businesses streamline operations, enhance customer experiences, and drive innovation. If you’re looking to tap into AI for your business, these companies offer AI development services to make it possible.

In this article, we'll spotlight the Top 10 AI development companies in 2024, including industry leaders and innovative startups making strides in AI development technology. Whether you're looking for AI development solutions or full-scale AI services, these companies have established themselves as front-runners in the field.

1. Shamla Tech – Your Partner in AI Innovation

A recognized name in the AI development world, Shamla Tech stands out for its comprehensive AI services, offering everything from custom AI models to robust AI solutions that cater to a wide variety of industries. With years of expertise in AI development technology, Shamla Tech is known for helping businesses leverage AI to automate processes, enhance decision-making, and improve overall operational efficiency.

Why Shamla Tech?

Customized AI Development Solutions: Shamla Tech develops bespoke AI systems to meet the unique needs of every business. Whether you need AI-powered chatbots or AI algorithms for predictive analysis, they deliver tailor-made AI development services.

End-to-End AI Services: Their expertise spans the entire AI development lifecycle, including data collection, model training, deployment, and maintenance.

AI-Powered Business Insights: Shamla Tech enables companies to extract valuable insights from their data through advanced AI analytics tools.

Key Focus Areas:

AI-powered automation for various industries

AI model training and optimization

Custom-built AI solutions for enterprises

2. OpenAI – Pioneering Generative AI

When it comes to groundbreaking AI development technology, OpenAI continues to lead with innovations like GPT-4, which has revolutionized the field of generative AI. The company focuses on developing advanced machine learning models that assist businesses in content generation, decision-making, and problem-solving.

Why OpenAI?

Advanced AI Models: OpenAI’s GPT series is renowned for generating human-like text, making it useful for chatbots, customer service, and content creation.

Cutting-Edge AI Technologies: From natural language processing (NLP) to machine learning algorithms, OpenAI remains at the forefront of AI innovations.

Flexible AI Solutions: They offer customizable AI solutions to various sectors, including e-commerce, education, and customer support.

3. Google DeepMind – AI for Scientific Breakthroughs

Known for its pioneering work in AI, particularly in healthcare and science, Google DeepMind pushes the boundaries of AI's potential. DeepMind’s AI models have been instrumental in solving complex scientific problems, like protein folding, which has far-reaching implications for drug discovery and medical research.

Why Google DeepMind?

AI for Healthcare: DeepMind uses AI development solutions to enhance medical diagnostics, particularly in fields like radiology and ophthalmology.

Scientific AI Models: Their innovations extend to biomedicine, physics, and chemistry, applying AI to some of the most complex scientific challenges.

AI for Gaming: DeepMind’s AI models, such as AlphaGo, have made headlines for their unprecedented success in games like Go and StarCraft.

4. IBM Watson – Enterprise AI Solutions

IBM Watson is one of the most comprehensive AI platforms available today, focusing on delivering AI development services for enterprises. Whether you're looking to integrate AI into your supply chain or customer service, IBM Watson provides customizable AI solutions that help businesses operate more efficiently.

Why IBM Watson?

Scalable AI Development: Watson's AI platform offers scalable solutions for enterprises, allowing them to grow their AI capabilities as their business needs evolve.

Industry-Specific AI Solutions: IBM Watson provides tailored AI technologies for healthcare, finance, and other sectors.

Data-Driven AI Models: Their AI models are designed to analyze large datasets, offering businesses actionable insights to improve their operations.

5. Microsoft Azure AI – Cloud-Powered AI

Microsoft Azure AI offers a cloud-based platform that enables businesses to build, deploy, and manage AI applications. Azure AI is especially known for its deep integration with Microsoft’s other enterprise tools, making it an excellent choice for companies already utilizing the Azure cloud ecosystem.

Why Microsoft Azure AI?

Seamless Cloud Integration: Azure AI integrates with Microsoft’s cloud infrastructure, offering a smooth AI deployment process.

Comprehensive AI Services: From machine learning models to cognitive services like speech and vision, Azure AI covers a wide range of AI applications.

Enterprise-Grade AI Development: Azure AI is designed to meet the demands of large enterprises, providing secure, scalable, and reliable AI development technology.

6. Amazon Web Services (AWS) AI – AI in the Cloud

AWS AI provides powerful AI development solutions through its extensive cloud-based platform. Known for its robust infrastructure, AWS allows businesses to easily deploy and scale their AI initiatives using machine learning and natural language processing tools.

Why AWS AI?

AI-Powered Cloud Solutions: AWS AI integrates AI into cloud-based services, enabling businesses to process vast amounts of data seamlessly.

Advanced Machine Learning Models: AWS offers pre-trained machine learning models and customizable options for businesses looking to innovate using AI.

Wide Industry Reach: AWS AI provides solutions for a range of industries, including retail, healthcare, and logistics.

7. C3.ai – AI for Digital Transformation

C3.ai specializes in providing AI development services for digital transformation, focusing on industries like energy, manufacturing, and defense. Their AI solutions help companies optimize their operations, reduce costs, and make data-driven decisions.

Why C3.ai?

AI for Enterprise: C3.ai provides AI-based enterprise applications that help large businesses improve efficiency and performance.

Industry-Specific AI Models: Their solutions are designed for high-impact industries, offering tailored AI models that address specific challenges.

Comprehensive AI Development Technology: C3.ai covers the entire AI development lifecycle, from data collection to model deployment.

8. DataRobot – Automated Machine Learning

DataRobot offers automated AI development solutions that allow businesses to deploy AI without needing in-depth data science expertise. Their platform provides AI models that are easy to integrate and scale, making AI accessible to businesses of all sizes.

Why DataRobot?

Automated AI: DataRobot simplifies the AI development process by automating much of the model-building process, making it easier for businesses to implement AI.

Pre-Built AI Models: Their platform offers a wide range of pre-built AI models that are ready for deployment.

AI for Decision Making: DataRobot’s AI solutions help businesses make smarter, data-driven decisions through predictive analytics.

9. H2O.ai – Open-Source AI Platform

H2O.ai is known for its open-source AI development technology, offering businesses the flexibility to customize and scale their AI projects. Their platform provides both pre-built AI models and tools for building custom AI solutions.

Why H2O.ai?

Open-Source Flexibility: H2O.ai allows businesses to access open-source AI tools, offering complete customization for AI development.

Fast Deployment: Their platform is designed for fast, efficient deployment of AI models, helping businesses innovate at speed.

AI for Financial Services: H2O.ai has been especially successful in the financial sector, offering AI models for fraud detection and risk management.

10. Narrative Science – AI for Data Storytelling

Narrative Science focuses on converting data into meaningful insights through AI-powered storytelling. Their AI development services help businesses transform complex data into easy-to-understand narratives, enabling better decision-making across the organization.

Why Narrative Science?

AI for Data Interpretation: Narrative Science specializes in creating AI models that generate natural language narratives from complex data sets.

Improved Decision Making: Their AI-powered insights help businesses make informed decisions quickly and efficiently.

Customizable AI Solutions: Businesses can tailor Narrative Science’s AI solutions to meet their specific needs, whether for internal reports or customer-facing insights.

These top AI development companies of 2024 are leading the charge in revolutionizing industries with their advanced AI technologies, AI models, and innovative AI development solutions. Whether you're a startup looking to integrate AI into your product or an enterprise aiming for large-scale digital transformation, these companies provide the AI development services you need to stay competitive in a tech-driven world. Among these, Shamla Tech continues to stand out with its customized AI offerings, making it a top choice for businesses looking for specialized AI expertise.

[techtired]

AI Development Services Review on real AI examples 

Current State, Future Prospects, and Comparison of chatGPT, Claude AI and other AI tools The integration of AI systems into our daily lives and business operations is progressing at an unprecedented pace. As developers continuously launch new products and startups across various domains, companies increasingly join this technological marathon to stay competitive and innovative. AI tools for app development Among the innovative offerings in the market, we see systems for automatic source code generation and startups focused on processing legal documents. Many companies, including Diatom Enterprises – a well-known industry leader in AI Development Services, have primarily utilized open platforms like ChatGPT. Recently, there has been growing interest in exploring newer platforms to create apps like Claude AI. AI development solutions beyond text Modern AI systems demonstrate remarkable versatility: Document Analysis: AI can process and interpret complex accounting and legal documents, streamlining financial and legal operations. Image and Diagram Comprehension: Advanced AI models can analyze visual data, including project architecture diagrams, providing insights and facilitating a better understanding of complex visual information. Multi-modal Analysis: By combining text and visual analysis capabilities, AI systems offer comprehensive insights across various data types, enhancing business decision-making processes. AI Assistance: The AI ​​development market sees new systems working as AI assistants. AI Development Services: Claude AI, Danswer AI, and others Claude AI, a contemporary system, offers several advantages in custom software development: Technical Documentation: Assistance in creating comprehensive and accurate technical documentation. Architecture Support: Helping developers conceptualize and design software architecture. Code Generation: Capability to produce both basic and specific code for projects. However, the enterprise adoption of such systems faces challenges: Local Deployment: There's a growing demand for AI systems that can be deployed within a company's local ecosystem, such as Danswer or PrivateGPT. These allow businesses to train Language Learning Models (LLMs) in specific directions relevant to their needs. Limitations of Pre-trained Models: While ChatGPT and Claude are pre-trained models, they can assist in setting up and training localized, manual AI systems for specific tasks like processing unique legal documents, customizing image generation (e.g., logo creation in a company's style), or analyzing invoices for accounting systems. Integration Challenges and API Usage It's important to note that chat interfaces like Claude and ChatGPT are frontend wrappers for powerful AI models accessible via APIs. These APIs form the backbone of AI integration in enterprise solutions: API-based Integration: Both OpenAI (for ChatGPT) and Anthropic (for Claude) offer API access to their AI models. This allows for more flexible and scalable integration into existing systems. Customizable Conversations: When using the API, developers can model the initial conversation manually, effectively "priming" the AI for specific tasks. This approach allows for more targeted and efficient use of AI capabilities. Pricing Models: API usage is typically based on a credit system or token count. Pricing can vary based on the model used and the volume of requests. For example: - OpenAI's GPT-3.5 and GPT-4 APIs have different pricing tiers based on the model's capabilities. - Anthropic's Claude API also uses a credit-based system, with pricing varying by model and usage volume. Performance Considerations: While API integration solves some of the challenges associated with web-based chat interfaces, developers still need to consider the following: Rate limits and concurrent request limitations Latency, especially for real-time applications Managing context effectively to minimize token usage and improve response relevance Data Privacy and Security: When integrating AI via APIs, companies must carefully consider data handling practices, especially when dealing with sensitive information. By leveraging these APIs, businesses can create more robust, scalable, and customized AI solutions that go beyond the limitations of web-based chat interfaces. Metrics and Comparison of AI to build an app Objectively assessing the differences between systems like ChatGPT and Claude is challenging. Comparisons often rely on technical metrics such as price, speed, and quality (which can be subjective). Our software development company has an advantage in that field; we have grown our internal expertise in AI application development since we have many customers and different ways. How much resources do you need to spend to set up a test environment to evaluate the quality of AI for creating an app? How expensive will supporting an AI platform be during development and operation costs? How easy will it be to integrate an AI system to build an app? How the security of the system's machine learning data and results is ensured What is the audience of companies involved in the platform's development and support, and what are the prospects for using this platform in the near future? Future: AI Development Services in Custom App Development (2025) The impact of AI systems on custom software development is expected to be significant. A potential model for future development processes involves using a central AI (like ChatGPT or Claude) to orchestrate other specialized AI engines: System Concept Development: The central AI develops the project's system concept and framework choices. Architectural Design: It generates prompts for AI systems specialized in creating architectural diagrams. Project Templating: The AI creates project templates and instructs tools like GitHub Copilot to build the project skeleton. Creating apps using AI nowadays is extremely popular. This "AI orchestrator" model could allow developers to create projects in the company's style with minimal manual input simply by providing high-level commands or even having the central AI generate these commands based on project requirements. Using ai to build an app is increasingly crucial in our software development world. Conclusion of the AI App Development Services Review As AI technology continues to evolve, its integration into business operations promises to drive efficiency, innovation, and competitive advantage across industries. However, deployment, training, and scalability challenges must be addressed for widespread enterprise adoption. The future of AI app development looks set to be dramatically transformed by AI, with the potential for significant automation and efficiency gains. Read the full article

[techytoolzataclick]

Artificial Intelligence in Next 5 Years – The Latest Tech Revolutions 2024

Artificial Intelligence in Next 5 Years – The Latest Tech Revolutions 2024

AI rapidly develops making way for new solutions that revolutionize numerous sectors. Fast forward to 2024, AI tools have evolved tremendously in terms of sophistication, range and ease of use. In this article, you will get some AI tools that are new to the tech world and for all anyone who is exploring more towards AI and tech field.

1. How Generative AI Is the Next-Gen In Content Creation

The growth that occurred in generative AI tools throughout the four-years of 2024 was an astounding one. All these tools can be used to generate high-quality content ranging from text, through images and even music! OpenAI with ChatGPT-4 is an excellent example of this. This makes it a potential powerhouse for content creators, marketers, and businesses, most importantly because it can create text that is coherent and makes sense in context. Another notable tool is DALL- E 3, allows generating beautiful images based on the text description — which changes everything for creatives.

2. AI in Video and Audio Editing

Video & Audio Editing by Content Creators Great dependencies for content creators on AI aid Tools Synthesia: AI Software for Video Creation from Text It is very popular among marketers and product teams with >140 AI avatars, serving 120+ languages. Another effective tool you can use to automatically transcribe your podcast is Descript, it offers automatic transcription, voice cloning and video editor make that editing a seamless experience.

3. AI-Powered Coding Assistants

The obvious benefit of AI-powered coding assistants is for developers. To boost development, GitHub Copilot provides developers with code suggestions, snippets and code line completion; all thanks to OpenAI’s Codex. Tabnine is an AI tool for code auto completion that helps to accelerate coding by intelligently completing or providing suggestions based on the context of the code you are writing

4. AI in Healthcare

Healthcare is another big area seeing drastic improvements in AI tools. The service trains and uses machine learning algorithms to help pathologists perform more accurate disease diagnoses faster. Viz. Company Description AI leverages AI to find and rank potential large vessel occlusion strokes in CT scans — getting patients treated sooner, smarter. These are some of the tools that health care providers use to diagnoses and treat patients these days.

5. AI for Business Intelligence

AI is playing a larger role in data analysis and decision-making for businesses. Tableau — It integrates AI capabilities to give it more advanced insights and predictive analytics. IBM Watson continues to be the future, will be to lead in AI-powered business intelligence with tools that enable organizations to analyse data faster, automate processes quicker and make decisions better!

6. AI in Customer Service

The other most growing part of AI tools in real world is customer service. Pro: Many applications are integrating AI for better customer support such as Zendesk adopts the practice of using automated responses or predictive analytics as a part of customer care. Ada is a data-driven, AI-powered customer service chatbot for businesses. They not only boost customer satisfaction but also help in reducing the response time.

7. AI for Personal Productivity

At one point, there will be more similar AI tools in place besides personal productivity. Notion AI added to the Notion workspace helps to categorize tasks, produce content and handle projects more orderly. Grammarly- It is an old favourite among writers, providing AI-powered convenient grammar and style recommendations for better written content.

Conclusion

More recent AI tools in 2024 are at the forefront of what is feasible, delivering novel methods throughout any selection of parts. These tools are enabling professionals and hobbyists a-like in broad use cases such as content creation, video editing, healthcare and business intelligence. The future will bring many more disruptive tools as the AI technology matures.

Keep up with trends of AI and tools — As the technology evolve, the ability to access, refine or distribute more information grows; knowing when it is better to take this opportunity/avoid getting late will be a thing that keeps you in line with your work and also working just as well as pushing others back down by holding steady in tech. If you have goals, the AI is there: if your profession involves content creation, development, healthcare or business leadership.