Future of artificial intelligence AI
Technology is like a constantly moving living organism. Just as a person
starts from the beginning, they grow, sometimes they are pushed out of the
competition and eliminated. Let's look at the changes related to AI in the
future.
Technology is like a constantly moving living organism. Just as humans
start at their infancy, technology also emerges in a poor state and grows to a
high level through innovation, or it is pushed out of competition with other
technologies. The way the market embraces technology is also constantly
changing. As soon as it appears, the public pours a lot of interest, and
although the initial market is formed, it often takes a long time to spread. In
order to devise a strategy to create a big impact through AI, it is necessary
to understand the mechanism and long-term direction of how technology and
market environment change. In that sense, let's take a look at the changes
related to AI in the future.
1. The exponential: Accelerating technological advancement Artificial intelligence AI
Above all, AI technology will advance at an even faster pace. The MIT
Technology Review has consistently introduced AI technologies that are
constantly evolving. Moore's Law is a concept commonly used when talking about
the development of IT technology. This means that the number of transistors in
a semiconductor circuit doubles every 1.5 years. The rate at which the
technological performance of AI is advancing is projected to be 5 to 100 times
faster than Moore's Law (ARK Investment, 2020). The computational processing
power of AI learning models is growing tenfold every year, so this exponential
growth will continue in the future.
2. Artificial intelligence AI Data Big Bang: Explosive Data Growth
Second, the amount of data is growing rapidly. Data is an essential
material for AI modeling. Worldwide, data is growing exponentially. Data Never
Sleeps, which analyzes data statistics, presented the amount of data produced
per minute through various applications and services in April. YouTube is
pouring out a lot of data with 500 hours of video uploaded every minute. About
147,000 photos are uploaded to Facebook and 150,000 messages are shared, and
about 41 million messages are shared per minute on WhatsApp, an SNS service. In
the case of Zoom, more than 200,000 people connect for meetings per minute,
TikTok has 2,704 people installing the application, and Amazon is shipping
6,659 products per minute. A significant amount of data is produced among these
activities, and the amount is increasing every year.
3. Reduction of cost: reduction of Artificial intelligence AI training cost
The third is cost. Advanced AI models such as deep learning provide
excellent performance, but the disadvantage is that they are expensive. For
deep learning training, high-end hardware must be prepared, and it takes a long
time to learn massive data. The amount of manpower that is put in to prepare
data and carry out modeling is not formidable. However, as hardware
technologies such as GPU and TPU are developing recently and more efficient
data processing methods have emerged, the learning cost is decreasing to 1/10 a
year.
4. Heterogeneous bonding: bonding between technologies in other fields artificial intelligence AI
The fourth is that the combination of technologies will become more
widespread. AI can create added value by being compatible with various
technologies. For example, Metaverse, which has recently become an important
topic in the industry, can be combined with AI. Metaverse is a compound word of
meta meaning virtual and transcendence and universe meaning real world, and
refers to mixed reality where virtual and real interact. AI can create an
avatar in the metaverse similar to the user's real appearance, or it can be
designed to communicate with the user by applying a natural language
processing-based communication model. In addition, it can recognize users in
virtual reality and show customized advertisements. AI will play an important
role in making this metaverse world intelligent. In addition, it can be
combined with blockchain to establish an intelligent security system or to
predict the volatility of household currency to improve asset stability. The
4th industrial revolution is a time when advanced new technologies appear
simultaneously. In this situation, AI is expected to play a role in promoting
disruptive innovation through combination with other technologies.
5. Proliferation: Expanding the scope of artificial intelligence AI adoption
Fifth is the expansion of the scope of application of AI. Due to the
great potential of AI, many industries are adopting this technology. Currently,
AI is being actively introduced in fields such as life science, distribution,
and consumer goods. So far, it is generally at the pilot or proof-of-concept
(PoC) level. However, in the future, it will be applied to all industries and
is expected to mature in the direction of being applied to actual products and
services.
6. artificial intelligence AI Universalization: universal use of technology
The sixth is the generalization of AI technology. Although AI technology
has a long academic history, it has only recently entered the industry. As a
result, it is true that this technology is unfamiliar to many companies now.
However, it will not remain as uncharted territory in the future as it is now.
In the early 1990s, when the Internet boom occurred, many companies introduced
web services and e-commerce. This is because it was expected that new added
values would be created in this area. However, in a situation where everyone
is using the Internet, the Internet itself cannot make a difference now. The
same goes for AI. As the potential is great, many companies will adopt this
technology, and over time it will become a general-purpose technology. In other
words, introducing AI itself does not guarantee differentiation. Currently,
there is a shortage of AI technical talents, but the AI graduate school and
various educational programs are set up to produce many talents, so the talent
shortage problem is expected to be gradually resolved. Of course, talent with
both technical and industry domain knowledge will continue to be scarce.
7. Impact Gap: The Competitiveness Gap Deepens artificial intelligence AI
The seventh is that the gap in impact created by AI is widening among
companies. The introduction of AI does not mean that the same impact can be
created. Few of the real AI adopters know exactly how AI works and are linking
it to distinct value creation. According to a McKinsey study, mature companies
that successfully introduced AI achieved 3 to 4 times higher operating profit
than latecomers. These companies account for about 10% of the total. On the
other hand, about 30% of immature companies that have introduced AI but have
not yet used it effectively, although they create an impact, only produce an
operating profit effect of 1.8 times that of latecomers. In other words,
although the introduction of AI can have an impact, there can be an impact gap
depending on how effectively AI is used. Over time, this impact gap is expected
to widen.
Summarizing these changes, the technological performance and
cost-effectiveness of AI will be further improved, and it will be applied to a
wider range of industries through technology convergence. However, as the
technology becomes commoditized, the benefits of AI in itself will gradually
disappear. In the future, the difference in competitiveness will widen
depending on how effectively AI has been introduced and created a lot of
impact. From now on, it should be seen that the task to be focused on has been
decided. It is to have a strategy to flexibly absorb the increasingly advanced
AI technology into its business and create a distinct impact through it. In
order to have such a strategy, it is necessary to understand the types of
impact that can be created through AI, along with an understanding of the
technology itself, and how to lead from the actual AI introduction to the
impact.
※ Jung Doo-hee is the editor-in-chief of MIT Technology
Review Korea, and is a professor in the Department of ICT and Entrepreneurship
at Handong Global University. She serves as a representative partner of
Impactive AI, an AI consulting company, helping Korean companies successfully
introduce AI. She is the author of <AI Business Model Ended in One Book>,
<The AI Hypergap Era is Coming in 3 Years>, and <TQ Technology
Intelligence>.
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