What to Expect from Generative AI in 2024

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What to Expect from Generative AI in 2024
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A recent McKinsey study suggests that using
generative AI (GenAI) could significantly boost
global productivity, potentially adding trillions
of dollars in value to the economy. They estimate
an annual addition of 2.6 trillion to 4.4
trillion across 63 analyzed use cases. About 75
of this value falls into customer operations,
marketing and sales, software engineering, and
RD. The report also notes that this estimate
could double if we consider embedding generative
AI into existing software for tasks beyond the
analyzed use cases. Some organizations eagerly
adopted this technology, while others took a more
cautious "wait-and-see approach." Regardless of
the approach, enterprises can expect to witness
various generative AI trends in 2024.
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Emeroftence Of Multimodal AI Models
In 2024, the world of large language models, like
GPT4, Meta's Llama 2, and Mistral, showcases
exciting progress. These models don't just stick
to text they embrace multimodal AI, enabling
users to combine text, audio, images, and videos
to create and prompt new content. This involves
blending various data types, like images, text,
and speech, using smart algorithms to predict and
generate outcomes. Looking ahead, 2024 promises
a significant evolution in multimodal AI,
reshaping generative AI capabilities. These
models are moving beyond the usual single-mode
functions, integrating different data types such
as images, language, and audio. This shift to
multimodal models is making AI more intuitive and
dynamic. ChatGPT Plus subscribers already
appreciate the capabilities of GPT4-V, a popular
choice for its multimodal features. In 2024, open
models like LLaVA (Large Language and Vision
Assistant) are anticipated to gain prominence,
marking a continued trend toward more accessible
and versatile AI.
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Decision-tree chatbots will become a thinoft of
the past
Currently, when you chat with customer service
bots, they often ask you a bunch of similar
multiple-choice questions. If your question
doesn't fit into those options, they tell you to
call a customer representative. With the rise of
advanced AI, these bots will soon get
smarter. They'll consider important info like
what you've bought before and your
preferences. Then, they'll give you personalized
and accurate suggestions whenever you need them.
This way, businesses can recreate the experience
of talking to a salesperson in a store, but now
it's online and available 24/7. Brands using AI
can now help customers with their questions,
recommend products based on what they've bought
in the past, and address support issues instantly
with the help of a Generative AI Development
Company.
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Cloud Native Becomes Key To On-Prem GenAI
Kubernetes is the go-to choice for hosting
generative AI models. Big names like Hugging
Face, OpenAI, and Google plan to use
Kubernetes-powered cloud infrastructure for their
generative AI platforms. Tools like Hugging
Face's Text Generation Inference, AnyScale's Ray
Serve, and vLLM already support running model
inference in containers. In 2024, we can expect
frameworks, tools, and platforms on Kubernetes to
reach maturity, handling the complete lifecycle
of foundation models. This means users can
efficiently pre-train, fine- tune, deploy, and
scale generative models. Major players in the
cloud-native ecosystem will share reference
architectures, best practices, and optimizations
for running generative AI on cloud
infrastructure. LLMOps will also be expanded to
support integrated cloud-native workflows.
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Expect a flood of new AI-powered tools

• Increased investments in AI software-as-a-service
  (SaaS) this year mean we can expect lots of
  AI-powered products in 2024. This surge may make
  it tricky for businesses buying software to sift
  through the many automation tools available. The
  best products will be the ones that genuinely use
  AI to boost productivity. On the flip side, there
  will be many similar-looking applications using
  LLM APIs, competing based on cost and likely
  fading away over time.

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Capable And Powerful Small Lanoftuaofte Models

• If 2023 was all about large language models, 2024
  is set to highlight the effectiveness of small
  language models (SLMs). While large language
  models (LLMs) learn from huge datasets like
  Common Crawl and The Pile, which come from
  countless public websites, the data can be a bit
  noisy due to its general internet origins.
• On the flip side, SLMs are trained on more
  focused datasets with high-quality content from
  sources like textbooks and journals. These models
  are smaller in terms of parameters, making them
  efficient on less powerful hardware. Despite
  their size, SLMs can generate content comparable
  to their larger counterparts.

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Notable SLMs like Microsoft's PHI-2 and Mistral
7B are poised to drive the next wave of
generative AI applications.
Enterprises can fine-tune SLMs to suit specific
tasks and domains, meeting legal and regulatory
requirements, thus accelerating the adoption of
language models.
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Wrappinoft up
The potential economic impact of generative AI
(GenAI) is substantial, with McKinsey estimating
a significant boost to global productivity. The
emergence of multimodal AI models in 2024,
exemplified by GPT4 and LLaVA, signals a
transformative shift towards more intuitive and
dynamic generative AI capabilities. The evolution
beyond decision- tree chatbots towards
personalized AI-driven customer interactions is
imminent. Cloud-native solutions using
Kubernetes are becoming integral to on-premises
GenAI deployment and the rise of capable small
language models promises efficiency without
compromising quality. As 2024 unfolds, the AI
landscape is poised for a flood of new tools, but
only those genuinely enhancing productivity will
endure.