ChatGPT is mind-blowing – for want of a better word. Since its launch in November end, this smart chatbot has triggered some vital discussions on what AI as a technology can potentially achieve. 

And, it has already made history. In just about five days after its release by OpenAI, an astounding 1 million users signed up for Chat GPT. Within a span of two months, ChatGPT became the fastest application to hit 100 million users, surpassing the records set by TikTok, Instagram and Facebook. 

“What makes ChatGPT so impressive is its ability to produce human-like responses. Another thing that differentiates ChatGPT is its ability to log context from users’ earlier messages in a thread and use it to form responses later in the conversation. So, this is very different from how computers used to behave before,” wrote Jijy Oommen, Chief Technology Officer & Digital enthusiast in her recent Linkedin post. 

In the revolutionary world of Generative AI

What it also did in the process is to push Generative AI into the broader mainstream discussions. If we borrow Bill Gates’ words, “Generative AI like ChatGPT will change our world!”. 

Generative AI is the branch of artificial intelligence that uses algorithms to ‘generate’ new content in the form of text, audio, images or videos. 

By now, we have seen numerous thrilled users across the globe taking to social media to showcase their experiences with this bot. 

While ChatGPT may be the most popular ‘Generative Pre-trained Transformer’, a number of players have already launched similar products in the market – such as Jasper, Anthropic, Bloom, Chinchilla or the new kid on the block – Google’s Bard. 

And yet, we have probably only seen the tip of the iceberg. As Microsoft CEO Satya Nadella rightly said at the World Economic Forum 2023, ‘the world is entering a golden age of Artificial Intelligence, and AI is just the beginning of the S-curve.’

The global generative AI market size is anticipated to reach USD 109.37 billion by 2030 growing at a CAGR of 34.6%, according to a new report by Grand View Research. 

Investments around generative AI has been soaring. According to Pitchbook, Venture Capitalists have increased investment in Generative AI by 425% since 2020 to $2.1bn. 

Business applications abound 

New business use-cases of this technology are expected to create far-reaching impact across industries. Early foundation models like ChatGPT focus on the ability of generative AI to augment creative work, but by 2025, we expect more than 30% — up from zero today — of new drugs and materials to be systematically discovered using generative AI techniques,” says Brian Burke, Research VP for Technology Innovation at Gartner.

 According to experts, here are a few of the emerging yet sophisticated applications of generative AI for businesses:  

Marketing & social media: From creating personalized content to driving customer experience, generative AI has broad range of use cases within marketing and social media. According to Gartner estimates 30% of outbound marketing messages from large organizations will be synthetically generated, by 2025. Currently, this number is just above 2%.

Product design & development: Smart algorithms have the power to redefine the process of product design and development. This will ensure faster product development and better products. 

Real-time &Data-driven decision making: Generative AI is expected to play a crucial role in decision optimization, by allowing decision makers to leverage huge amounts of real-time data. 

The post ChatGPT has just kicked off the Generative AI era appeared first on CIO Dimension.

The 2023 State of AI Infrastructure Survey, commissioned by Run:ai, sheds light on the growing challenges faced by organizations in AI development. The survey, which was conducted by Global Surveyz Research and gathered responses from 450 industry professionals across the US and Western EU, reveals that infrastructure and compute, chosen by 54% and 43% of respondents respectively, are now the primary hurdles, surpassing data as the key challenge facing AI development. This marks a shift compared to last year’s survey by Run:ai, where the largest number of respondents – 61% cited data as their top challenge.

The survey also pointed to another shift over the past year as the number of organizations deploying less than half of their AI models in production increased from 77%, according to last year’s survey, to 88% this year. Weighted for average, just 37% of AI models make it into production.

The adoption of cloud services for AI infrastructure continues to rise, with 73% of surveyed organizations using cloud services. However, the survey found a significant challenge in accessing GPU compute, as only 28% of respondents reported having timely and sufficient access to compute power upon demand. This shortage of on-demand access leads to frequent GPU allocation issues for 89% of respondents who use a ticketing system.

“Despite being on the cloud, organizations are still facing limitations with unlocking the full potential of their data,” said Omri Geller, CEO of Run:ai. “This highlights the reality that cloud hasn’t delivered on its on-demand promise and the importance of building a robust and scalable infrastructure.”

The survey also found that as organizations scale and require more GPUs, they face a proliferation of third-party tools, making it increasingly complex to manage AI infrastructure and get the most out of it. 77% of respondents indicated they are using multiple third-party tools, making it difficult to get the right amount of compute to different workloads and end-users.

“Organizations must shift their focus from solely acquiring more data to ensuring they have the proper infrastructure in place to effectively process and utilize it,” added Geller.

Some other findings of the survey:

• 91% of companies are planning to increase their GPU capacity or other AI infrastructure by an average of 23% in the next 12 months. This shows that despite the uncertain economic climate, companies are still investing in AI due to the potential and value they see in it.

• 50% of companies plan to implement monitoring, observability, and explainability in the next 6-12 months to keep track of their AI models.

• The second and third priorities were model deployment and serving (44%) and orchestration and pipelines (34%). This indicates that companies are focused on bringing their AI models into production and streamlining the process to make it more efficient.

The post Data no longer the top challenge for AI projects; Infra & Compute are appeared first on CIO Dimension.