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Tag: IEEE

technology

What’s Next for Generative AI?

Dec 4, 2024 by Sujata 0 Comments #Context Window, #Generative AI, #Hector Azpurua, #IEEE, #Lin

Generative AI took the world by storm in recent years after several chatbots, like ChatGPT, entered the public domain. The chatbots generated human-like text with a speed that seemed almost magical – writing sonnets in the style of Shakespeare, translating texts between numerous languages, churning out computer code and so much more.

Businesses and business pundits saw the potential benefits immediately. However recent months have seen small but growing doubts about generative AI. Detractors say generative AI’s capabilities have been overhyped. Hallucinations – false statements that generative AI models can make – decrease its usefulness, and many businesses have yet to find an ideal strategy to use these tools. And though ChatGPT is one of the fastest-growing applications of all time, the proportion of people who say they use it regularly remains fairly rare.

In “The Impact of Technology in 2025 and Beyond: an IEEE Global Study,” a recent survey of global technology leaders, 91 percent of respondents agreed that “in 2025 there will be a generative AI reckoning as public fascination and perception shift into a greater understanding of and expectations for what the technology can and should do — in terms of accuracy of results, transparency around deepfakes and more.”

But the survey doesn’t anticipate a lasting stumbling block for generative AI. A sizeable majority (91%) also agreed that “generative AI innovation, exploration and adoption will continue at lightning speed in 2025.”

So, what’s in store for generative AI in 2025? What’s the product roadmap, and what impact will they have on how we work and live?

More Multimodal Capabilities

IEEE Senior Member Daozhuang Lin expects generative AI models to make it easier to provide images and videos from short text snippets in the coming years. Text-to-image, text-to-video and speech synthesis will improve, and models will achieve better contextual understanding across diverse inputs.

“The first step is the deep integration of multi-modal to create more complex, detailed, accurate and self-consistent content for consumers and even professional content creators,” Lin said.

Cleaning Up Accuracy and Bias

Concerns over hallucinations, accuracy and bias have also slowed the adoption of generative AI models. Bias may creep in when the models are trained on biased data. Some image-generating models may show a preference for people of a certain race.

“The developers of the model need to focus on how to remove the bias and ethical issues generated by AI in the process of consumer data training,” Lin said. “It’s important to guide users to more universal and long-lasting values and to guide the model to become more ‘kind’.”

Improved Context Window

One limitation generative AI model face is the amount of information they can process at one time in a prompt. This is referred to as the context window or context size. Imagine, for example, that you need to input a very long prompt – or description – in an attempt to generate an image. At some point, the generative AI model will not be able to process the entire prompt. The output will only reflect a portion of the prompt, omitting potentially important information.

In another scenario, you may need to have a conversation with the model about a long document. As the conversation progresses, the model may forget earlier parts of the conversation.

Improving the context window would allow generative AI models to handle more complex tasks and improve the coherence of their responses.

“The limit of what we can do with generative AI has yet to be reached; we are not at the plateau of this technology,” said Hector Azpurua, an IEEE Graduate Student Member.

 

 

Disclaimer: The information contained in this press release is provided by a third party. We do not endorse or guarantee its accuracy. Recommendations, suggestions, views and opinions given by the experts are their own. These do not represent the views of Shreyas Web Media Solutions Pvt Ltd.

business

Dark Data Steps Into the Infrastructure Spotlight

Feb 22, 2024 by Sujata 0 Comments #Dark Data, #electrical grids, #IEEE, #Infrastructure, #maintenance, #operations, #Qi Qi Wang, #Sensors

Sensors are everywhere – turning off highway lights when roads are empty, monitoring the health of bridges, and monitoring the intricate dance of telecommunications networks and electrical grids. Every flicker of these sensors is a byte of data, meticulously logged and stored. With the costs of data storage plummeting over the past decade, we’re talking about an avalanche worth of data digitally warehoused.

Much of this data has been resting in the dark, unanalyzed and unseen. This is what experts call dark data. And now, as AI steps into the infrastructure arena, this dormant data is about to step into the spotlight.

“Indeed, there appears to be an enormous amount of data collected on infrastructure operations that could be better used to improve their effectiveness,” said IEEE Life Senior Member Raul Colcher.

AI thrives on data – the more, the better. And when it comes to training sophisticated AI models, this dark data, collected over years from myriad sensors and systems, may be extremely valuable.

So, what’s the big deal about bringing this dark data to light? For starters, it’s a game-changer for infrastructure operations. With AI algorithms churning through mountains of previously unused data, we can expect leaps in efficiency and new ways to design and use our infrastructure for a future where data moves more frequently than people.

BYTES BUILD BETTER

Much of the time, dark data isn’t used because it isn’t properly tagged, and is therefore difficult to analyze. Some research suggests that the machine learning algorithms that allocate resources within mobile phone networks could be greatly improved with the use of dark data. In another case, data scientists at an oil and gas plant were able to use dark data to improve a digital model of the plant without disrupting operations.

WHERE WILL THE IMPACT BE FELT THE MOST?

The benefits of analyzing and modeling this data are vast and varied. From planning to operations, maintenance, and beyond, every facet of infrastructure could see a transformation. Picture more accurate models, better automation, and a deeper understanding of how our systems truly work.

CHALLENGES ON THE HORIZON

But, it’s not all smooth sailing. Dark data, while abundant, isn’t always clean or error-free. Questions of bias, data provenance, and security loom large. How we address these challenges will be crucial in unlocking the full potential of AI in infrastructure.

“The surge in data quantity doesn’t guarantee better results,” said IEEE Member Qi Qi Wang. “Filtering out disruptive or poor-quality data presents a substantial challenge.”

Learn more: 2023 was a landmark year in AI, as broad swaths of the public became more aware of AI thanks to the power of generative AI tools. IEEE Spectrum covered developments in-depth. Check out their run-down of the top AI stories in 2023.

business

Driven by Data: Fueling EVs

Feb 14, 2024 by Sujata 0 Comments #Data, #EV, #EV adoption, #future electric vehicles (EVs), #IEEE, #Kyri Baker, #Senior Member Marcio Andrey, #THE GRID

Imagine a world where the hum of engines and the smell of exhaust are things of the past. That’s the future electric vehicles (EVs) promise us. As it stands, our roads contribute about 12% of global carbon emissions, but the rise of EVs is key to turning this around and making transportation cleaner and greener.

But, to accelerate EV adoption, the sector needs to leverage a new type of fuel altogether: data.

EVs are game changers for infrastructure around the globe. With their growing numbers, they’re reshaping everything from city layouts to how we manage our electricity supply. The shift to EVs brings new challenges for our power grids and the need for innovative infrastructure to keep them running.

Here’s how data can help meet those challenges:

THE GRID

The central challenge that data can solve boils down like this: Lots of people tend to charge their EVs at the same time, placing sizable strain on electricity grids. But there are also periods when few people are charging their cars, meaning there is excess capacity in the grid. Is there a way to shift EV owners’ behaviors so they charge during times of lower demand?

“Intelligent EV charging is becoming a big area,” said IEEE Senior Member Kyri Baker. “Charging EVs at high power rates can strain infrastructure like transformers, so smart scheduling of charging can help extend the lifespan of these components.”

By looking at how customers have used power in the past, it’s possible to make the distribution of electricity – especially for charging things – more efficient. By figuring out the busiest times and places for electricity use, the people running the power grid can spread out the power usage better. This way, they can avoid overloading the system, save on energy costs, and make the whole network run smoother.

WHERE CAN YOU CHARGE?
Knowing how much battery power an EV has left is super useful, not just to individual drivers, but to all their fellow drivers on the road. That information, sometimes referred to as the vehicle’s state of charge, can tell an individual motorist how far they can go before they need to refuel. By collecting and analyzing state-of-charge data for many vehicles, a driver can know the best charging station to use to avoid a wait. And builders would also be able to use the data to understand the best place to build new charging stations.

“By analyzing the historical data of electric vehicle charging station use, like time of day, day of the week, seasonal variations, etc., it is possible to understand where the demand is high,” said IEEE Senior Member Marcio Andrey Teixeira. “The behavior of the data is another important factor because it provides insights like preferred charging times and popular routes. This information helps in the optimization of the placement of charging stations along frequently traveled routes.”

Learn more: Electric vehicles are here to stay, according to an editorial from IEEE Power & Energy Magazine. This means that the distribution grid and its stakeholders need to ensure that EVs and the grid work together. The Nov.-Dec. 2023 issue devotes numerous articles to the challenges of integrating EVs.