Most enterprises failed to scale their AI deployments this year.
Will 2022 be the year when this actually happens?
Wanna know the top AI predictions for 2022?
Keep watching!
Hi!
My name is Alex and welcome to AI News Daily.
In this video, we will be looking at the top AI Predictions for 2022.
Don’t forget to subscribe and turn on the notifications.
Let’s get started!
In 2021, businesses expected artificial intelligence to help them make better and faster predictions
in order to achieve a competitive advantage.
Despite their best efforts, nearly 80% of organisations failed to scale AI installations
across the organisation last year, according to an Accenture study.
However, we believe AI will evolve swiftly enough in 2022 to begin delivering on its
promise.
We anticipate a fresh wave of technology developments that will assist businesses in overcoming
the common problems associated with implementing AI.
This year, data scientists spent more than half of their time cleaning data, integrating
different storage systems, and determining the storage capacity and computing power required
to deploy AI and machine learning models.
Because most models were constructed in a sterile setting with clean data, a significant
amount of highly trained human resources were spent resolving operational concerns.
Because of a lack of understanding of the complexity of integrating real data, models
that were placed into production did not perform as expected.
As more and more machine learning activities become automated, we predict some relief for
data scientists in 2022.
In the following year, many labor-intensive operations involving repetitive, laborious,
and time-consuming functions will begin to be automated.
Not only would automating the process results relieve data scientists by allowing them to
focus on what they do best — improving algorithms — but it will also allow IT teams to deploy
higher-performing AI/ML models faster.
Previously, only large companies with vast budgets, such as Google and Facebook, could
make AI/ML models a reality.
In 2022, developers at midsize businesses will employ more off-the-shelf technology
to make AI/ML models more accessible.
Making apps talk, converting speech to text, automating video and picture analysis, automatically
removing improper or unlawful information, and many more industry-specific use cases
will all be possible.
Furthermore, because bringing your own computer and storage becomes a reality, there will
be fewer hassles when picking and porting AI software to other platforms.
AI developers will be able to choose the best-of-breed compute and cloud solution for each machine
learning model as AI/ML models consume more resources.
Choosing the most efficient platform on the fly avoids vendor lock-in and gives data scientists
the flexibility they need to optimise computing resources while also allowing them to experiment
with new, innovative technologies without jeopardising their entire AI/ML ecosystem.
Most businesses were still in the experimentation or proof-of-concept stages of AI in 2021.
We will see a move toward AI-first methods in the coming year.
Artificial intelligence applications will be at the forefront of business initiatives.
Companies will see a steeper curve of development as AI/ML models become the standard, spreading
AI to every department and touching every business function.
According to the Deloitte State of AI survey, nearly 80% of business and IT executives believe
AI will be important to their company's success in the next two years.
In addition, over three-quarters of respondents expect that AI will be used by all enterprises
within the next three years.
We predict AI to become more widely adopted in a variety of industries, but particularly
in healthcare, retail, manufacturing, and finance.
AI will not only be more common, but it will also be more strategic in 2022.
AI will be utilised to boost productivity in the future.
Do you not believe it is already taking place?
In addition, AI will be utilised to rethink and rebuild products, services, business models,
and overall strategy in the coming year.
AI will not be a bolt-on to an existing infrastructure, but rather an essential element of a company's
technology stack, giving real-time insights to customers, partners, and employees.
In 2022, organisations will strive to employ AI insights to become more competitive by
being completely data driven, thanks to the emerging internet economy.
The giant firms' increased research and development will result in more innovation and enablement,
bringing AI within reach of smaller businesses as well.
The issues of integrating, cleaning, and processing data will persist this year, according to
Sony, but there will be a flood of more generic open-source solutions to replace manual operations,
freeing up data scientists for more strategic responsibilities.
There will be more enabling technologies available to facilitate the transition to an AI economy
as the growing online economy wants more data for companies to be more efficient and give
a better customer experience.
For the food and beverage business, AI and robots bring exciting new potential, with
companies aggressively researching the use of these new technologies in all aspects of
their operations "Spranger said.
"Some of the most fascinating AI applications in gastronomy are on the creative side, where
AI is being utilised to expand chefs' and culinary specialists' imagination and creativity
beyond what is currently achievable.
Chefs will soon be able to use some of these AI applications to improve sensory experiences
while cooking and eating, create healthier and more sustainable recipes, and automate
and scale food preparation.
These advancements will alter the way cooks make food combinations, pairings, and platings,
as well as aid chefs in generating new, innovative dishes that are both nutritious and environmentally
friendly."
The COVID pandemic has resulted in workforce shortages, which AI-driven automation helps
to address.
The automation also keeps food away from human contact, which can spread germs.
Almost every company in the COVID era is experiencing labour shortages, which they could help to
ease if they widened their hiring criteria for candidates.
Employers can use AI to broaden their view as they consider a larger pool of employment
candidates.
"Any technology that has an influence on human life must be produced from a diverse set of
voices," Spranger added.
"Historically, AI has progressed without the engagement of people from various backgrounds,
resulting in negative repercussions for underrepresented groups.
Diversity is essential for preventing harm and fostering creativity...
When extending our teams, one aspect to consider is if we hire people from other areas, such
as the arts, social sciences, philosophy, and so on.
Without the input from a diverse range of human experiences, AI will not be able to
fulfil its promise to humanity."
That's all right.
Since the inception of AI and machine learning, there have been fears and concerns that these
disruptive technologies could supplant human labour and even eliminate some jobs.
Working alongside machines with smarter cognitive functionality, on the other hand, increased
individuals' abilities and skills as organisations began to adopt these technologies and introduce
AI/ML literacy into their workforce.
For example, in marketing, AI/ML tools are already being used to help firms identify
possible leads and the business value that potential consumers may provide.
Furthermore, AI and ML technologies in engineering provide predictive maintenance, which is the
ability to predict and inform the servicing and repair needs of enterprise equipment.
Furthermore, AI/ML technology is commonly employed in disciplines of expertise, such
as law, to sift through ever-increasing volumes of data and locate the most relevant information
for a given task.
OpenAI's release of GPT-3, the most advanced and largest natural language processing model
ever constructed, marks a significant step forward in language processing.
It has roughly 175 billion 'parameters,' which are data points and variables that robots
use to process language.
GPT-4, a more powerful successor to GPT-3, is currently being developed by OpenAI.
According to speculation, GPT-4 could have 100 trillion parameters, making it 500 times
larger than GPT-3.
This advancement brings us closer to constructing machines that can learn language and have
conversations that are indistinguishable from human talks.
Sentiment analysis, process description, machine translation, automatic video caption production,
and chatbots are some of the NLP technologies that are projected to gain in prominence.
The World Economic Forum recently said that cybercrime is a greater threat to society
than terrorism.
Cybercrime becomes rampant, and cybersecurity solutions become sophisticated, as more intelligent
but complex robots connected to a large network gain control of every aspect of our life.
AI and machine learning tools have the potential to help solve this problem.
AI/ML algorithms, for example, can scan greater network traffic and spot patterns of illicit
virtual activity.
This is likely to be the site of some of the most significant AI/ML technology advances
in 2022.
With that, we have come to the end of the video.
If you enjoyed the video, hit the like button and let us know your thoughts in the comments
section below.
See you again with another video, until then.
Bye!