"Some of the Latest Trends in Artificial Intelligence"
From Nanalyze:
We’re into our second year of publishing a “Global AI Race”
series of articles on artificial intelligence startups from around the
world and it continues to pose a challenge. We use an objective measure
of “total funding taken in so far” and that excludes any firms that
choose not to disclose funding or are bootstrapped. We search for
various categorizations like “artificial intelligence” or “deep
learning” and that means we’ll miss any firms that haven’t chosen those
categories in their Crunchbase profile. But the ones we worry about the
most are those firms that we might include in one of our “top AI
startups” lists that don’t actually do AI. It’s a huge problem, and one
that was highlighted recently by a European venture capital firm, MMC
Ventures, that surveyed 2,830 startups in Europe that were classified as
being AI companies and found out that 44% of these companies were incorrectly classified as being “AI startups.”
Still, the
fact that there are 1,580 AI startups across Europe means that we’re
reaching a tipping point, or what MMC Ventures calls “a
divergence.” Working together with Barclays, they produced a 149-page
report titled “The State of AI: Divergence 2019”
which takes a holistic look at AI across the globe finding a growing
division between leaders and laggards. We pored through every page of
that report to extract some of the latest trends in artificial
intelligence that you might find insightful.
Artificial Intelligence and Adoption
“In
2019, AI ‘crosses the chasm’ from early adopters to the early
majority,” says the report. As an investor, you should now be looking
for companies that don’t use artificial intelligence since they’re
quickly going to become laggards. For many of the companies adopting
artificial intelligence, it may seem all new and shiny. Truth be told,
AI has been around for decades with “seven false dawns” taking place
between 1965 until now.
Artificial
intelligence may be “the fastest paradigm shift in technology history.”
In just three years, the number of enterprises with “AI initiatives”
rose from 1 in 25 to 1 in 3. One in ten enterprises use more than ten AI
applications, and the most popular use cases are chatbots (26% of enterprises), process automation solutions (26%), and fraud analytics (21%). Nearly half of all companies prefer to buy AI solutions from third parties as opposed to building their own.
Globally, China leads the charge
with twice as many Asian firms adopting AI as compared to North
American firms. The report points out some interesting high-level
reasons why China has become a global leader:
- Data Availability – China has more permissive policies than Europe regarding use of personal data.
- Less siloed data within companies –
According to MIT Sloan Management Review, 78% of leading Chinese
companies maintain their corporate data in a centralized data lake,
compared with 37% of European and 43% of US companies.
- Legacy technology – Chinese companies typically have fewer legacy applications and processes to deal with.
It’s
not surprising that two out of three reasons involve data. The best AI
algorithms are the ones with exclusive access to high-quality data sets.
With that said, some of the developments being made in artificial
intelligence hardware and technologies are of equal importance.
Artificial Intelligence Technologies
When
it comes to understanding the underlying technology behind artificial
intelligence, most of us can get by with the very basics. For the people
who are building these algorithms, it’s a different story. They’re
highly paid and highly educated. Salaries for AI engineers average
$224,000 at the 20 highest-paying companies and 60% of AI developers
have a Master’s or Doctoral degree. Demand has been so high for talent,
that even the academics are being pulled into the corporate world.
According to The Economist, between 2006 and 2014, the proportion of AI
research publications including an author with corporate affiliation
increased from approximately 2% to nearly 40%.
There are more
than 15 approaches to machine learning, and trying to understand even
one would probably take as much time as studying for the CFA with about
the same benefits at the end. Instead, you just need to know about the
latest trends in machine learning so you can throw them around at your
next board meeting to demonstrate what a thought leader you are. Here
are some trends to watch when it comes to how artificial technology is
developing.
AI Hardware Trends
AI Software Trends...
...
MUCH MORE
