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Generative AI is the hot topic on everyones mind. The possibilities seem endless, and thousands of companies, products, models, and platforms have flooded the market to monetize those possibilities. Big players like OpenAI, Microsoft, Meta, Google, and Amazon, along with many other large companies and myriad startups, are vying for market share in the GenAI space.

Real-life use cases are emerging across multiple industries. Wendys is using Google Clouds GenAI at its drive-throughs. Coca-Cola released an AI platform created by OpenAI and Bain & Company as a part of its marketing function. Companies are using GenAI for customer chat applications, to assist with coding, to help write emails, and more.

Right now, Lee Moore, VP of Google Cloud Consulting, sees enterprises on a spectrum when it comes to GenAI. At one end of the spectrum, youve got customers who are almost paralyzed, whether its their internal processes, their concerns around data privacy, or other things that you read about, he explains.

In the middle of the spectrum, enterprises are experimenting with GenAI but not yet investing a significant amount. And then, there are enterprises that are seriously investing in real-life use cases, according to Moore.

Regardless of where an enterprise is in its approach to GenAI, there seems to be no question that the technology is here to stay. But how can enterprise leaders work through the hype cycle to set a budget for this technology and select the right solutions for their organizations?

Related:Thomson Reuters on Using GenAI to Augment the Professional Workforce

GenAI promises a lot of things: improved efficiencies, cost savings, happier employees and customers. But how can that technology do that for your enterprise? Before making a significant investment in any technology, enterprise leaders need to understand the business case.

As C-suite leaders and boards talk about GenAI, they can ask questions about what their priorities are from a cost-saving, revenue-driving, reduction of risk, better customer experience [perspective], and what the right stack is to help serve their enterprise outcomes, says Ryan Martin, partner and AI global lead at Infosys Consulting, a management and IT consulting company.

Being able to have that discussion requires an investment in learning. GenAI technology is evolving so quickly that it demands enterprise leaders constantly learn so they dont fall behind. In my team, I have allocated 10% of everybodys time this year to learning because of the speed of which this is going, Moore shares.

Related:Financial Services Explores GenAI in Interesting Ways

As part of that learning process, Moores team will participate in hackathons or competitions. The team, in a couple of days, came up with the solution that would pretty much automate the generation of our services contracts with our customers, he shares. We get to 80 odd percent completion in minutes. And then the human in the loop gets in there to finish off the rest of the contracting.

Business function leaders can explore how GenAI can solve challenges for their teams.

Demonstrating how the technology can solve a specific problem can create a foundation for an initial investment that can be scaled. It always starts with defining your problem statement and then looking at what are the host of tools you have to solve that, Prasad Ramakrishnan, CIO at Freshworks, a cloud-based SaaS company, tells InformationWeek.

With a business case in mind, enterprise leaders need to sift through a sea of options to find the right GenAI tools and/or partners.

There is a massive proliferation of startups [with] various niche capabilities, and some of them are just out there trying to win budgets and take advantage of the hype, while others have been built on research, says Erik Brown, senior partner, AI and product engineering at digital services firm West Monroe Partners.

Related:Experts Ponder GenAIs Unprecedented Growth and Future

Just how much the GenAI market will change in the coming years is hard to predict. With so many companies offering solutions, it is unlikely that all of them will become long-time players in the space. You cant even keep track of how many are popping up on a regular basis. And my suspicion is within six months half [of] them wont be there anymore, says Ryan Worobel, CIO at LogicMonitor, a cloud-based infrastructure monitoring platform.

Considering a companys funding position and its likelihood of longevity can help narrow down the options. Enterprises can also examine what their existing technology partners are doing with GenAI to see if there is a potential fit. It really is [about] figuring out who are the vendors you trust that youre working with and the vendors you have who you truly consider a partner in the process that wouldnt jeopardize their own relationship with the company just to start throwing capabilities out there, Worobel explains.

While many enterprises are in the experimentation phase, it is important to consider the big picture. AI is a broad category of which GenAI is a piece. How does a potential tool fit in an enterprises overall tech stack?

I would say a potential red flag is one [a tool] thats maybe too myopic in its focus [it] needs to be able to integrate with other AI technologies in the stack, says Martin.

Moore argues for a platform approach. You dont want to get locked into any particular model or tool because there are now hundreds if not thousands of them today, he says.

Choosing a GenAI solution also involves an element of risk management. Martin points out that enterprises can learn lessons from technologies that have emerged and generated massive hype and demand in the past.

If you dont [have] guardrails and some level of governance within your own organization, youre going to end up with thousands of one-off, new implementations that add risk to the organization, add overhead in terms of management, add pain that I think can be avoided if you do it intelligently and with the right governance structure, he cautions.

How much an enterprise spends on GenAI this year, next year, and beyond will depend on a multitude of factors: its industry, its size, its maturity, its target use cases. Weve seen success in integrating with an existing AI model an investment of a $150,000 to $200,000. Weve also built highly complex custom models into products that take [an] investment of$1 million to $2 million, says Brown.

Where do enterprises want to put their dollars toward GenAI? For some, it might make sense to focus on external partnerships and solutions. For others, dollars might be spent on internal R&D. Many enterprises will be budgeting for both.

Its going to be far more predictable to think about how you set a blanket budget for the use of licensed-embedded AI tools and enterprise software like Microsoft Office, says Brown. He expects that budgeting for building GenAI and other forms of AI into custom internal products and workflows will likely be the bigger investment. But I think thats where the most compelling opportunity is going to be moving forward, he contends.

Organizations can approach setting a budget for GenAI in different ways. Worobel shares that his team is taking lessons from the advent of cloud technology. They set aside a percentage of the IT budget into an innovation bucket, which they dipped into to test cloud. Now, Worobel and his team are looking at an innovation bucket for GenAI.

We just put a bucket of dollars kind of on the side in the overall AOP [annual operating planning], Worobel shares. We dont really have [it] earmarked for anything yet, and what were doing as we come upon different ideas and look at different components and capabilities, well make decisions or whether or not we fund it.

Choosing what to invest in goes back to the business use case. What will a particular solution deliver in terms of increased productivity or efficiency? Moore recommends targeting a specific improvement and then deciding what piece of the budget is required to achieve it.

I would be asking my team to come back to me with a plan to deliver a certain percentage improvement in efficiency and output, he outlines. Lets say, 5% or 10% in the first year, with an expectation it would grow beyond that and to give me the budget request to achieve that.

Enterprise leaders want to know that GenAI is generating returns. Tracking its ROI does not have to be radically different than doing so for any other technology investment. Select KPIs and monitor them. Is that tool improving, for example, coder productivity? Is it increasing customer conversion rates?

Starting with a small pilot project can help enterprise leaders prove the use case for a GenAI solution and then scale. If you isolate the people using the tools to start and you set them on kind of clear goals of how to use the tools, you can measure productivity pretty quickly and then figure out how to expand that beyond that small pilot group, says Brown.

Enterprise leaders spearheading a GenAI use case test also need to track utilization of the tool in question. You need to track app deployments, app utilization, and [if they are] using all the features, says Ramakrishnan. If users arent adopting the tool or are only using a fraction of the features the enterprise is paying for, there are dollars being wasted.

Budgeting for GenAI is going to be a learning experience. Enterprise leaders are going to need to make adjustments as they learn which external partners and tools and internal development initiatives drive progress toward their business goals.

In anticipation of the rapid pace at which GenAI is evolving, organizations can leave space in their budgets to adapt as changes arise.

I try to keep somewhere between 15% and 20% of my budget unallocated as I enter the year, which can be tough to do in tight budget environments, Worobel shares. But as long as you have that close partnership with your CFO and you can have an agreement that were not going to needlessly spend it and if we dont need it, well give it back.

Enterprise leaders can also track how GenAI generates cost savings and how those cost savings can potentially impact the overall budget. Ramakrishnan emphasizes the importance of app rationalization as a potential avenue to cost savings. How can a new GenAI tool potentially streamline a business function and pave the way for retiring now unnecessary tools?

This year, for many enterprises, is a year of experimentation. But GenAI is poised to soon move beyond the hype cycle, bringing more concrete, real-life use cases to bear.

I think this year is all about actually justifying the spend [on] enterprise supplementary tools, embedded AI tools, [whereas] the investment in engineering around data science and artificial intelligence for the general enterprise is going to grow more significantly going into next year, says Brown.

While each enterprise has to lay out its own path for adopting a new technology, GenAI will not be ignored.

I think the lesson from the dot-com boom 20 years ago, people didnt fully believe in the possibilities. Here, the possibilities are going to become real very, very quickly, says Moore.

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How to Budget for Generative AI in 2024 and 2025 - InformationWeek

Bruno Zerbib is not your traditional Orange board member. His colleagues past and present have often spent their whole careers at the French telecom incumbent, even joining it immediately after they attended one of the grandes coles, France's higher-education Ligue 1. The man now wearing the mantle of chief technology and innovation officer had not been employed by Orange in a significant role before June last year. Zerbib's resume shows he has spent most of this century living and working in California for some of the world's best-known Silicon Valley firms. His accent carries undertones of America.

This atypical profile partly explains his appointment, according to one company source. US Internet giants and the technologies they have unleashed are quickly reshaping Orange and other telcos, just as those same telcos previously reshaped economies through mobile and data connectivity. A CTIO with experience on the other side of the fence both geographically and industrially may look invaluable as outside technological forces including generative artificial intelligence (GenAI), accelerated computing and the public cloud pose unanswered questions for telcos.

It is AI in its various forms that has, unsurprisingly, become one of the big priorities for Zerbib amid a lack of telecom-industry consensus on the right next steps. Germany's Deutsche Telekom, the biggest service provider in Europe, thinks operators should build their own large language models (LLMs). Vodafone does not. Japan's Softbank is investing in Nvidia's graphical processing units (GPUs), the versatile chips used in hyperscaler facilities for LLM training. Many are wary. The phenomenon of "inferencing," the rollout of AI applications once LLMs are fully trained, could bring revenue opportunities for telcos at the network "edge," some hope. Others with an eye on recent history are skeptical.

The camp of the Nvidia skeptics

When it comes to Nvidia, Zerbib clearly belongs in the camp of the skeptics. "This is a very weird moment in time where power is very expensive, natural resources are scarce and GPUs are extremely expensive," he told Light Reading during an interview at this year's Mobile World Congress, held last month in Barcelona. "You have to be very careful with our investment because you might buy a GPU product from a famous company right now that has a monopolistic position." Low-cost alternatives are coming, said Zerbib.

He is not the only telco individual right now who sounds worried about this Nvidia monopoly. The giant US chipmaker's share price has doubled since mid-October, valuing Nvidia at nearly $2.2 trillion on the Nasdaq. But a 6% drop on March 8, sparked by wider economic concerns, illustrates its volatility. Its gross margin, meanwhile, has rocketed by 10 percentage points in just two years. The 77% figure it reported last year screams monopoly. And Nvidia controls not just the GPUs but also the Infiniband technology that connects clusters of them in data centers. Howard Watson, who does Zerbib's job at the UK's BT, wants to see Ethernet grow muscle as an Infiniband alternative.

But Zerbib spies the arrival of alternatives to GPUs "for a fraction of the price, with much better performance" in the next couple of years. In his mind, that would make an Orange investment in GPUs now a very bad bet. "If you invest the money at the wrong time, depreciation might be colossal and the write-off could be huge," he said.

What, then, could be the source of these GPU rivals? Established chipmakers and chip design companies such as Intel and Arm have pitched other platforms for AI inferencing. But disruption is likely to come from the hyperscalers or the startups they eventually buy, according to Zerbib. "Right now, the hyperscalers are spending billions on this," he said. "There are startups working on alternatives, waiting to be acquired by hyperscalers, and then you are going to have layers of optimization."

While Zerbib did not throw out any names, Google's own tensor processing units (TPUs) have already been positioned as a GPU substitute for some AI workloads. More intriguing is a startup called Groq, founded in 2016 by Jonathan Ross, an erstwhile Google executive who led the development of those TPUs. Tareq Amin, the CEO of Aramco Digital and a former telco executive (at Japan's Rakuten), thinks Groq's language processing units (LPUs) might be more efficient than GPUs for AI at the edge.

Efficiency is paramount for Zerbib as Orange strives to reach a "net zero" target by 2040. "We need to reduce by ten the amount of resource consumption for a given number of parameters," he said. "It is unacceptable. Right now, we are going to hit a wall. We cannot talk about sustainability and then generate a three-minute video that is equivalent to someone taking a car and doing a long trip. It makes no sense."

Models of all sizes

Today, he says he prefers to invest in people rather than GPUs. For Orange so far that has meant equipping about 27,000 members of staff (Orange had 137,000 employees worldwide at the end of last year) with AI skills. Unlike certain other telcos, however, Orange appears to have no interest in building an LLM from scratch. Instead, Zerbib is an advocate of fine-tuning for telecom purposes the LLMs that already exist.

Orange has, accordingly, spent money on building an AI tool it can effectively plug into the most appropriate LLM for any given scenario. "We have built internally a tool that is an abstraction layer on top of all those LLMs and based on the use case is going to route to the right LLM technology," said Zerbib. "We are very much proponents of open models, and we believe that we need to take huge models that have been trained at scale by people that have invested billions in infrastructure."

Available like items of clothing in numerous different sizes, future language models including those adapted by Orange are likely to be worn at multiple types of facilities, he clearly believes. Zerbib breaks them down into three categories: small language models, featuring up to 10 billion parameters and deployable on user devices; medium-sized models, which include up to 100 billion parameters and can be hosted at the network edge; and the very largest, trillion-parameter LLMs that will continue to reside in the cloud.

With GenAI, an employee could feasibly communicate "in layman's terms" with a telco's NOCs and SOCs (its network and service operations centers) to find out "what is going on," said Zerbib. Language models running at the network edge might also support applications for customers. "Let's say you have a French administration that needs to offer certain services to constituents throughout the country, and they want to make sure they do that with GenAI and have a distributed architecture to do it everywhere," said Zerbib. "Well, this is an opportunity for a telco to say we might provide an infrastructure to run those distributed LLMs that will handle social administration."

The most obvious AI attractions are still on the cost side. AI-generated insights should clearly help telcos reduce customer churn, minimize network outages and "essentially extract more value from the infrastructure for the same amount of capital investment," as Zerbib describes it. The telco dream, though, is of AI as a catalyst for sales growth. "There is a question mark with slicing, with the emergence of new use cases and low-latency capabilities," said Zerbib. "We might be able to monetize that." Conscious of the telecom sector's historical tendency to oversell, Zerbib is for now putting a heavy emphasis on that "might."

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Orange CTO: Now's not the time to invest in Nvidia GPUs for AI - Light Reading

As represented in this illustration, deep distilling transforms complex neural network insights into concise, human-readable rules or code, making AI-discovered knowledge more accessible and transparent. [Illustration: Anda Kim/UTSW]

Researchers at UT Southwestern Medical Center in Dallas have developed an artificial intelligence (AI) method that writes its own algorithms and may one day operate as an automated scientist to extract meaning from complex datasets.

Milo Lin, Ph.D., is Assistant assistant professor in the Lyda Hill Department of Bioinformatics, Biophysics, and the Center for Alzheimers and Neurodegenerative Diseases at UT Southwestern. [Photo: UTSW]

Our work is the first step in allowing researchers to use AI to directly convert complex data into new human-understandable insights, said Milo Lin, Ph.D., assistant professor in the Lyda Hill Department of Bioinformatics, Biophysics, and the Center for Alzheimers and Neurodegenerative Diseases at UT Southwestern.

He noted that while researchers are increasingly employing AI and machine learning models, these high-performing models provide limited new direct insights into the data.

Lin co-led the study, published in Nature Computational Science, with first author Paul J. Blazek, M.D., Ph.D., who worked on this project as part of his thesis work while he was at UT Southwestern.

According to UTSW, the field of AI has exploded in recent years, with significant crossover from basic and applied scientific discovery to popular use.

One common branch of AI, known as neural networks, emulates the structure of the human brain by mimicking the way biological neurons signal one another, the university said. Neural networks are a form of machine learning, which creates outputs based on input data after learning on a training dataset, the school noted.

Lin said that although this tool has found significant use in applications such as image and speech recognition, conventional neural networks have significant drawbacks.

Most notably, they often dont generalize far beyond the data they train on, and the rationale for their output is a black box, meaning researchers dont have a way to understand how a neural network algorithm reached its conclusion.

UTSW said the study was supported by its High Impact Grant Program, which was started in 2001 and supports high-risk research offering high potential impact in basic science or medicine.

Seeking to address both issues, the researchers said they developed a method called deep distilling.

According to UTSW, deep distilling automatically discovers algorithms or the rules to explain observed input-output patterns in limited training datadatasets used to train machine learning models.

Thats accomplished by training an essence neural network (ENN), previously developed in the Lin Lab, on input-output data. The parameters of the ENN that encode the learned algorithm are then translated into succinct computer codes so users can read them, the university said.

Researchers said they tested deep distilling in a variety of scenarios in which traditional neural networks cannot produce human-comprehensible rules and have poor performance in generalizing to very different data.

Those included cellular automata, in which grids contain hypothetical cells in distinct states that evolve over time according to a set of rules. Thats often used as model systems for emergent behavior in the physical, life, and computer sciences, UTSW noted.

The school said that despite the grid the researchers used having 256 possible sets of rules, deep distilling was able to learn the rules for correctly predicting the hypothetical cells behavior for every set of rules after only seeing grids from 16 rule sets, summarizing all 256 rule sets in a single algorithm.

In another test, researchers trained deep distilling to accurately classify a shapes orientation as vertical or horizontal.

According to UTSW, the method only needed a few training images of perfectly horizontal or vertical lines. However, it was able to use the short algorithm it found to correctly solve much more complicated cases, such as patterns with multiple lines or gradients and shapes made of boxes and zigzag, diagonal, or dotted lines.

Eventually, Lin said, deep distilling could be applied to the vast datasets generated by high-throughput scientific studies, such as those used for drug discovery, and act as an automated scientist capturing patterns in results not easily discernible to the human brain, such as how DNA sequences encode functional rules of biomolecular interactions.

UT Southwestern said that deep distilling potentially could serve as a decision-making aid to doctors, offering insights on its thought process through the generated algorithms.

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UT Southwestern Medical Center and pharmaceutical company Pfizer have teamed up to develop new ways to deliver genetic treatments to patients. Together they aim to accelerate the development of therapies that work through RNA biology and engineered delivery methods to fix root genetic causes of disease, which Pfizer could apply to its portfolio of investigational programs.

Saturday's event is one of many marking this year's 150th anniversary of Dallas' historic Deep Ellum neighborhood. Saturday's event will begin just down the street from the center beneath an overpass, where attendees will experience the Centers outdoor installation, Invisible Deep Ellum, followed by a live music performance at the new center by the Light Crust Doughboys.

The initial investment will support the commercialization of life science research, including identifying researchers with entrepreneurial mindsets, safeguarding intellectual property, and recruiting key executives.

Known as the birthplace of the blues in Texas, the district just east of downtown Dallas regularly buzzes with live music, street art, galleries, restaurants, clubs, and culturally creative retail. But all this year, Deep Ellum is kicking it up a notch. Here's what's in store over the next several weeks.

That was then. This is now. New streetlight pole banners are hitting the streets in Dallas' Deep Ellum neighborhood, offering looks at the past and present of the historic area east of downtown.

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UTSW Researchers Develop AI That Writes Its Own Algorithms - dallasinnovates.com

There are so many buzzwords in the tech world these days that keeping up with the latest trends can be challenging. Artificial intelligence (AI) has been dominating the news, so much so that AI was named the most notable word of 2023 by Collins Dictionary. However, specific terms like machine learning have often been used instead of AI.

Introduced by American computer scientist Arthur Samuel in 1959, the term machine learning is described as a computers ability to learn without being explicitly programmed.

For one, machine learning (ML) is a subset of artificial intelligence (AI). While they are often used interchangeably, especially when discussing big data, these popular technologies have several distinctions, including differences in their scope, applications, and beyond.

Most people are now aware of this concept. Still, artificial intelligence actually refers to a collection of technologies integrated into a system, allowing it to think, learn, and solve complex problems. It has the capacity to copy cognitive abilities similar to human beings, enabling it to see, understand, and react to spoken or written language, analyze data, offer suggestions, and beyond.

Meanwhile, machine learning is just one area of AI that automatically enables a machine or system to learn and improve from experience. Rather than relying on explicit programming, it uses algorithms to sift through vast datasets, extract learning from the data, and then utilize this to make well-informed decisions. The learning part is that it improves over time through training and exposure to more data.

Machine learning models are the results or knowledge the program acquires by running an algorithm on training data. The more data used, the better the models performance.

Machine learning is an aspect of AI that enables machines to take knowledge from data and learn from it. In contrast, AI represents the overarching principle of allowing machines or systems to understand, reason, act, or adapt like humans.

Hence, think of AI as the entire ocean, encompassing various forms of marine life. Machine learning is like a specific species of fish in that ocean. Just as this species lives within the broader environment of the ocean, machine learning exists within the realm of AI, representing just one of many elements or aspects. However, it is still a significant and dynamic part of the entire ecosystem.

Machine learning cannot impersonate human intelligence, which is not its aim. Instead, it focuses on building systems that can independently learn from and adapt to new data through identifying patterns. AIs goal, on the other hand, is to create machines that can operate intelligently and independently, simulating human intelligence to perform a wide range of tasks, from simple to highly complex ones.

For example, when you receive emails, your email service uses machine learning algorithms to filter out spam. The ML system has been trained on vast datasets of emails, learning to distinguish between spam and non-spam by recognizing patterns in the text, sender information, and other attributes. Over time, it adapts to new types of spam and your personal preferences like which emails you mark as spam or not continually improving its accuracy.

In this scenario, your email provider may use AI to offer smart replies, sort emails into categories (like social, promotions, primary), and even prioritize essential emails. This AI system understands the context of your emails, categorizes them, and suggests short responses based on the content it analyzes. It mimics a high level of understanding and response generation that usually requires human intelligence.

There are three main types of machine learning and some specialized forms, including supervised, unsupervised, semi-supervised, and reinforcement learning.

In supervised learning, the machine is taught by an operator. The user supplies the machine learning algorithm with a recognized dataset containing specific inputs paired with their correct outputs, and the algorithm has to figure out how to produce these outputs from the given inputs. Although the user is aware of the correct solutions, the algorithm needs to identify patterns, all while learning from them and making predictions. If the predictions have errors, the user has to correct them, and this cycle repeats until the algorithm reaches a substantial degree of accuracy or performance.

Semi-supervised learning falls between supervised and unsupervised learning. Labeled data consists of information tagged with meaningful labels, allowing the algorithm to understand the data, whereas unlabeled data does not contain these informative tags. Using this mix, machine learning algorithms can be trained to assign labels to unlabeled data.

Unsupervised learning involves training the algorithm on a dataset without explicit labels or correct answers. The goal is for the model to identify patterns and relationships in the data by itself. It tries to learn the underlying structure of the data to categorize it into clusters or spread it along dimensions.

Finally, reinforcement learning looks at structured learning approaches, in which a machine learning algorithm is given a set of actions, parameters, and goals. The algorithm then has to navigate through various scenarios by experimenting with different strategies, assessing each outcome to identify the most effective approach. It employs a trial-and-error approach, drawing on previous experiences to refine its strategy and adjust its actions according to the given situation, all to achieve the best possible result.

In financial contexts, AI and machine learning serve as essential tools for tasks like identifying fraudulent activities, forecasting risks, and offering enhanced proactive financial guidance. Apparently, AI-driven platforms can now offer personalized educational content based on an individuals financial behavior and needs. By delivering bite-sized, relevant information, these platforms ensure users are well-equipped to make informed financial decisions, leading to better credit scores over time. Nvidia AI posted on X that generative AI was being incorporated into curricula.

During the Covid-19 pandemic, machine learning also gave insights into the most urgent events. They are also powerful weapons for cybersecurity, helping organizations protect themselves and their customers by detecting anomalies. Mobile app developers have actively integrated numerous algorithms and explicit programming to make their apps fraud-free for financial institutions.

Featured image: Canva

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Artificial intelligence vs machine learning: what's the difference? - ReadWrite