ELM – A Solution To The Power Problem

The U.S. power grid simply won't be able to handle the increased load without significant investment – Goldman Sachs pegs the required investment at $50+ billion. This comes at a time when the nation is already committed to significant investments to upgrade the grid ($22 billion since 2021) to support the growing demands arising from national initiatives to transition away from natural gas appliances, EV market expansion, crypto mining operations, domestic manufacturing and the increasing need to safeguard against disruptions caused from extreme weather events or heightening risk of cyberattacks.

The increasing demand for electricity poses a significant challenge, especially considering the aging infrastructure of the U.S. power grid. To meet the growing needs, substantial investments are crucial. Goldman Sachs has highlighted the urgent requirement for over $50 billion in grid upgrades. This comes at a time when the nation is already engaged in significant endeavors to enhance the grid's capacity. Since 2021, approximately $22 billion has been allocated for these upgrades. These initiatives aim to support the rising demands stemming from national efforts to transition away from natural gas appliances, bolster the EV market, accommodate crypto mining operations, and foster domestic manufacturing. Additionally, there is a pressing need to safeguard against disruptions caused by extreme weather events or the escalating risk of cyberattacks.

Solutions To Big Problems Can Be Surprisingly Small

We’ve encountered similar challenges before; in 2023, we transitioned from incandescent light bulbs to energy-efficient alternatives at the federal level to alleviate power demands on our national infrastructure. Although no individual light bulb posed a substantial problem, the sheer volume made a significant impact. Similarly, AI applications are now emerging, showcasing a wide range of uses and potentially being limited only by their efficiency at scale.

The current state of AI mimics the introduction of electricity as it's poised to enable major new industries and drive economies. Today AI relies heavily on Graphics Processing Units (GPUs), which are specialized processors originally designed to accelerate graphics rendering. The parallel structure of GPUs is also ideal for traditional AI model training of applications and is used broadly in the Artificial Intelligence of Things (AIoT), which raises efficiency concerns at scale. AI companies are effectively over-deploying the most advanced, energy-intensive processors to fulfill some of their simplest application needs. While a one-size-fits-all approach can work in early AI applications, it simply can't be the sustainable standard for all AI implementations.

The inability to adapt the security, scalability and efficiency of AI solutions to specific applications is not unlike driving a tractor trailer to pick up your groceries. While it will certainly do the job it's not optimized for most tasks as it is tremendously inefficient as well as expensive. It's this mismatch between application needs that leads to a huge array of unintended consequences. The growing demand for AI is undeniable, but when AI relies on GPUs, the resultant applications overburden our already fragile infrastructure.

AI companies need to look for alternative ways to bring application needs into alignment to deviate away from this path that could threaten our infrastructure. Companies like Brand Engagement Network Inc. or BEN BNAI, realize this and have optimized their solutions to deliver the power and performance of AI while doing it in a way that can be scalable and supportable.

BEN's ELM – A Solution To The Power Problem

So how does BEN do it? Through its Efficient Language Models (ELMs): a combination of sectioning and optimization of language models for specialized tasking. This patent-pending technology concentrates on efficiency and application specialization, which contrasts with more traditional LLMs like those used by OpenAI's ChatGPT that attempt to generalize everything into an indiscriminate model for generative purposes.

Although this may seem like a small distinction, the computational and processing power required in each approach differs significantly. When traditional LLMs utilize all-inclusive models, it means their solutions are not defined. They task their AI solution to address all needs of all challenges or applications. Not only does this increase the likelihood of generated errors but it also demands massive parallel processing and, when operating with the motive of timely responses, necessitates the use of GPUs. BEN's ELM, on the other hand, focuses on defined application needs and allows a secure, small footprint, and concentrated solution. This means that solutions targeted with the ELM can run with the limited resources of CPUs, which are more readily available, significantly lower cost, and use less processing power.

Dependencies on CPUs provide many more deployment options, including SaaS, Private Cloud, Mobile, and On Prem solutions where industries such as Healthcare and Financial Services have struggled to minimize the potential risk of data breaches and leakages. Typically, CPUs are significantly cheaper to deploy & operate, already established in the market, and most importantly, available in large quantities. This is not the case with GPUs, which are in the midst of an availability issue that has even forced Elon Musk to get creative with the procurement of these processing units for his various companies.

ELM + RAFT: Powerful Yet Efficient Combination

BEN's ELM also augments RAFT (Retrieval Augmented Fine-Tuning) systems to ensure its applications are reliable, predictable and efficient. A significant challenge posed by AI is the risk of ‘hallucinations' where AI gives misleading or outright false answers as a result of the AI being built on unknown data sources and designed to generate a response no matter what. Hallucinations are a lot like the wasted heat energy from incandescent lights. They still demand the same power to generate the response but are an unintended consequence of traditional LLM technology. Some estimations indicate that hallucinations can occur

The above is the detailed content of ELM – A Solution To The Power Problem. For more information, please follow other related articles on the PHP Chinese website!