AI Trends 2024: Reality Check
Key Points
- 2024 is shaping up as the “reality‑check” year for generative AI, moving from hype‑driven buzz to more measured expectations and widespread integration of AI as co‑pilot features within existing software like Microsoft Office and Adobe Photoshop.
- Multimodal AI is gaining traction, with models such as GPT‑4V and Google Gemini able to process text, images, and video together, enabling richer interactions like visual‑aided instructions and seamless language‑vision queries.
- Energy and scalability concerns are prompting a shift toward smaller, more efficient models; while massive models consume electricity equivalent to thousands of households, newer open‑source LLMs are achieving strong performance with billions rather than trillions of parameters.
- The convergence of these trends—realistic deployment, multimodal capabilities, and resource‑lean architectures—is expected to define how AI is embedded in everyday workflows by the end of 2024.
Full Transcript
# AI Trends 2024: Reality Check **Source:** [https://www.youtube.com/watch?v=sGZ6AlAnULc](https://www.youtube.com/watch?v=sGZ6AlAnULc) **Duration:** 00:09:26 ## Summary - 2024 is shaping up as the “reality‑check” year for generative AI, moving from hype‑driven buzz to more measured expectations and widespread integration of AI as co‑pilot features within existing software like Microsoft Office and Adobe Photoshop. - Multimodal AI is gaining traction, with models such as GPT‑4V and Google Gemini able to process text, images, and video together, enabling richer interactions like visual‑aided instructions and seamless language‑vision queries. - Energy and scalability concerns are prompting a shift toward smaller, more efficient models; while massive models consume electricity equivalent to thousands of households, newer open‑source LLMs are achieving strong performance with billions rather than trillions of parameters. - The convergence of these trends—realistic deployment, multimodal capabilities, and resource‑lean architectures—is expected to define how AI is embedded in everyday workflows by the end of 2024. ## Sections - [00:00:00](https://www.youtube.com/watch?v=sGZ6AlAnULc&t=0s) **2024 AI Reality Check** - The speaker explains that 2024 marks a shift from hype to realistic expectations as generative AI moves from standalone chatbots to integrated co‑pilot features and multimodal models like GPT‑4V and Google Gemini that blend language and vision capabilities. ## Full Transcript
we're a little ways into 2024 now and
the pace of AI certainly isn't slowing
down but where will it be by the end of
the year well we've put together nine
trends that we expect to merge
throughout the year some of them are
Broad and high level some are a bit more
technical so let's get into them oh and
if you stumbled across this video in
2025 let us know how we did okay Trend
number one this is the year of the
reality
check it is the year of more realistic
expectations when generative AI first
hit Mass awareness it was met with
breathless news coverage everyone was
messing around with chat GPT darly and
the like and now the dust is settled
we're starting to develop a more refined
understanding of what AI powered
Solutions can do now many generative AI
tools are now being implemented as
integrated elements rather than
Standalone chatbots and like they
enhance and complement existing tools
rather than revolutionize or replace
them so I think co-pilot features in
Microsoft Office or generative fill in
Adobe Photoshop and embedding AI into
everyday workflows like these helps us
to better understand what generative AI
can and cannot do in its current form
and one area generative AI is really
extending its capabilities that is in
multi
model
AI now ai multimodal models can take
multiple layers of data as input and we
already have interdisciplinary models
today like open AI GPT 4V and Google
Gemini that can move freely between
natural language processing and computer
vision tasks so users can for example
like ask about an image and then receive
a natural language answer or they could
ask out loud for instructions to let say
repair something and receive visual aids
alongside step-by-step text instructions
new models are also bringing video into
the fold and where this really gets
interesting is in how multimodal AI
allows for models to process more
diverse data inputs and that expands the
information available for training and
inference for example by ingesting data
captured by video cameras for holistic
learning so there's lots more to come
this
year now Trend three
that relates to smaller
models now massive models they jump
started the generative AI age but
they're not without drawbacks according
to one estimate from the University of
Washington training a single gpt3 size
model requires the yearly electricity
consumption of over a th000 households
and you might be thinking sure that's
training we know that's expensive but
what about inference well a standard day
of chat GPT queries Rivals the daily
energy consumption of something like
33,000 households smaller models
meanwhile are far less resource
intensive much of the ongoing innovation
in llms has focused on yielding greater
output from fewer parameters now GPT 4
that is rumored to have around
1.76 trillion parameters but many
open-source models have seen success
with model sizes in the 327 billion
parameter range so billions instead of
trillions now in December last year
mistol released mixol that is a mixture
of experts or Ane model integrating
eight neural networks each with 7
billion parameters and mistol claims
that mixol not only outperforms the 70
billion parameter variant of llama 2 on
most benchmarks at six times faster
influence speeds no less but that it
even matches or outperforms open AI far
larger GPT 3.5 on most standard
benchmarks smaller parameter models can
be run at lower cost and run locally on
many devices like personal laptops which
conversely brings us to Trend number
four which is
GPU and Cloud
costs the trend towards smaller models
is p driven as much by necessity as it
is by entrepreneurial Vigor the larger
the model the higher the requirement on
GPS for training and inference
relatively few AI adopters maintain
their own infrastructure so that puts
upward pressure on cloud costs as
providers update and optimize their own
infrastructure to meet gen demand or
while everybody is scrambling to obtain
the necessary gpus to power the
infrastructure if only these models were
a bit more optimized they need less
compute yes that is Trend number five
that is model
optimization now this past year we've
already seen adoption of techniques for
training tweaking and fine-tuning
pre-train models like quantization you
know how you can reduce the file size of
an audio file or a video file just by
lowering its bit rate well quantization
lowers the Precision used to represent
model data points for example from 16bit
floating point to 8 bit integer to
reduce memory usage and speed up
inference
also rather than directing directly
fine-tuning billions of model parameters
something called Laura or low rank
adaptation entails freezing pre-train
model weights and injecting trainable
layers in each Transformer block and
Laura reduces the number of parameters
that need to be updated which in turn
dramatically speeds up fine tuning and
reduces the memory needed to stor model
updates so expect to see more model
optimization techniques emerge this year
okay let's uh let's knock out a few more
and the next one is all about custom
local
models open-source models afford the
opportunity to develop powerful custom
AI models that means trained on an
organization's proprietary data and
fine-tuned for their specific needs
keeping AI training and inference local
avoids the risk of proprietary data or
sensitive personal information being
used to train closed Source models or
otherwise pass through to the hands of
third parties and then using things like
rag or retrieval augmented generation to
access relevant information rather than
storing all of that information directly
within the llm itself that helps to
reduce model
size Trend number seven that is virtual
agents now that goes beyond the
straightforward customer experience
chatbot because virtual agents relate to
task automation where agents will get
stuff done for you they'll they'll make
reservations or they'll complete
checklist tasks or they'll connect to
other services so lots more to come
there Trend number eight that is all
about
regulation now in December of last year
the European Union reached provisional
agreement on the artificial intelligence
act also the the role of copyright
material in the training of AI models
used for Content generation remains a
hotly contested issue so expect much
more to come in the area of
Regulation and finally we're at Trend
number nine which is the continuance of
something called Shadow
AI what's that well it's The Unofficial
personal use of AI in the workplace by
employees it's about using gen AI
without going through it for approval or
oversight now in one study from Ernest
and Young 90% of respondents said they
used AI at work but without corporate AI
policies in place and importantly
policies that are observed this can lead
to issues regarding security privacy
compliance that sort of thing so for
example an employee might unknowingly
feed trade secrets to a public facing AI
model that continually trains the model
on user input or they might use
copyright protected material to train a
proprietary model and then that could
expose the company to legal action the
dangers of generative AI rise kind of
almost in a linear line with its
capabilities and that Line's going up
with great power comes great
responsibility so so there you have it
nine important AI trends for this year
but but but why nine don't these things
almost always come in tens well yes yes
they do and that's your job what is the
one AI trend for 2024 that we haven't
covered here the missing 10th Trend let
us know in the
comments if you have any questions
please drop us a line below and if you
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for watching