Appier Shares AI Predictions and Trends to Watch in 2021

TAIPEI, TAIWAN – Media OutReach – 23 February 2021 – Appier, a leading
artificial intelligence (AI) company, today shares AI Predictions and Trends to
Watch in 2021. Artificial intelligence (AI) and machine learning (ML)
have moved from the backrooms of computer science into the mainstream. Their
impact is being felt in everything from how we shop through to finance markets
and medical research, as well as the agriculture and manufacture industries.

 

Larger models have
been trained in separated modality. For instance, GPT-3 is the first
100-billion-parameter model for natural language processing (NLP). Recently,
a-trillion-parameter model (T5-XXL) has also been trained. They can be used to
write articles, analyze text, perform translations and even create poetry.

 

In parallel, we’ve
seen models used for image recognition and generation greatly improved as they
have also been trained with more data sets. What we are seeing emerge is the
power that can come from combining two or more AI models without changing these
large models. In this way, combining these large models becomes affordable.
That will allow us to use AI to interpret text and generate a completely new
image. The following are the current observations and predictions of AI
applications in five major fields.

 

The E-Commerce Boom Is AI-Driven

Over the last
year, online commerce has grown
significantly and is expected to continue to increase. COVID-19
restrictions have resulted in people spending much more time online — not just
shopping but in online meetings, playing games, accessing social media and
using apps. The growing digital journeys undertaken by people have generated
more data that can be used to understand human behavior. However, more data
also brings a greater complexity. Today, there’s no single, most effective
channel for reaching customers. Reaching the right customer on the right
channel at the right time is complicated for humans, but that complexity can be
overcome through the use of AI.

 

AI gives marketers a way to influence customer’s behavior at a
pace and scale previously thought impossible. AI not only finds the right
customers, but also accesses the often-forgotten long tail of customers. It can
also effectively generate creatives and develop customized content for
different customers, and test the performance for different creatives to
increase user engagement. 

 

Data-Driven Finance Relies on AI

Furthermore, the main application of AI in finance has been in
high-frequency trading where transactions are conducted between machines faster
than people can communicate. This will continue in both traditional finance and
in the world of cryptocurrencies, where we see different AIs engage in
‘warfare’. Investors have been using AI to make long-term predictions — which
has required systems that can understand investors’ long-term targets. These
were typically centered around measures such as revenues, incomes and profits.

 

While high-frequency trading strategies are important, there is another
factor to show that cryptocurrencies are far more challenging to predict. Much
of what we see in cryptocurrency markets is driven by ‘human madness’. While AI
models struggle with this today, we can expect the AI models of the future to
evolve and do a better job of predicting this behavior through closely
monitoring trends in media and social networks.

 

AI in Healthcare and Biomedical Research

The prototype of messenger RNA (mRNA) COVID-19 vaccines was developed in
days thanks to the digitization tools of genetic code sequencing and the
transcription tools of making mRNA from genetic code sequence. With the help of
AI to predict new mutations in the Sars-Cov-2 virus, the process of developing
mRNA vaccines will be even faster. AI can also be used as a diagnostic tool to read x-rays, based on the sound of someone
coughing and indicate whether the patient is likely to be suffering from
COVID-19 or some other illness.

 

In the biomedical
domain, sequences of codes, such as DNA or amino acid, are commonly used. Since
sequences of codes can be treated as a type of language with hidden structure,
the architecture used in NLP models can be potentially used to understand and
generate sequences of codes in the biomedical domain as well. One impressive
example in early 2021 is that biomedical researchers used language model
architecture to predict virus mutations and to understand protein folding — a
key challenge in the creation of some of the vaccines now available. This
finding is actually adapting the architecture of one model to solve problems in
the biomedical domain.

 

Machine learning
and AI don’t replace clinicians and researchers; they allow these professionals
to work faster and rapidly test hypotheses. Instead of waiting for cell
cultures to grow in the physical world, they can use these models to understand
what will happen much faster in the digital simulation. As more and more
people wear devices that can monitor heart rate, body temperature, blood
pressure and other critical factors, the data can be used to give doctors
greater insight into a patient’s condition. It also aids accuracy when making
diagnoses as doctors and other clinicians are no longer reliant on patient
recollections.

 

The Future of Education

Curricula and textbooks have typically been developed to
serve large populations of ‘average’ students. These materials include content
designed for a wide gamut of different abilities. However, experts, such as Sir Ken Robinson, point out that the
‘conveyor belt’ model of education doesn’t take into account the individual
abilities and needs of students. Therefore,
we have seen AI being used to revolutionize the way curricula is created and
delivered. It can be used to provide more personalized curricula or personal
problem sets for students. Instead of every student working through the same
set of problems or questions, they receive a set that are customized to their
specific level.

 

For example, a student may be very strong with fractions
in mathematics, but have a problem with trigonometry. Instead of putting the
student through the standard curriculum, he or she would spend less time on
fractions and more time on trigonometry. As a student proceeds through a
course, AI will monitor his progress and self-modify to meet the specific needs
of that student.

 

With so much content now available online, cheating and
plagiarism has become a huge issue. While detecting plagiarism is quite easy –
there is already AI that can detect direct copying and similar text where just
a few words or the tense are altered — there are other challenges. For example,
a student may take content from one language and translate it to another. This
is harder to detect, but AI is being developed to solve this problem. Similarly, image interpretation AI
is being developed to find instances where arts students copy or imitate a
design.

 

Smart Farming and
Factories

Factories and farms are using data in innovative ways too.
However, they differ from many other AI applications as they don’t focus on
end-users. Instead, they focus on products, produce and machines. This requires
an investment in sensors, robots and automation, and the optimization of
operations.

 

The biggest development we are seeing in this area is in
the generalization of findings between different areas. For example, if AI is
being used to increase yields in an apple crop, can those AI models be
reapplied for the growing of other fruits such as bananas or peaches? Similarly, if a factory is
manufacturing LCD panels and has found ways to increase their yield rates, can
those tools and lessons be applied to other manufacturing processes and
factories?

 

Perhaps the biggest prediction we can make
about AI in 2021 and beyond can be summarized in one word: leverage. Using
existing AI model architecture, combining well developed models and finding
ways to generalize existing models to other applications will continuously
increase the impact of AI along with accelerated digital transformation across
many domains. For more artificial intelligence and machine learning blog
information, please refer to the Appier blog.