ai future outlook Articles and Reports
Explore future perspectives on artificial intelligence applications and trends - including products and applications in marketing, finance, and other sectors.
In 2015, more than a billion dollars was spent on artificial intelligence research. That's more than in the field’s entire history combined. AI systems saw advancements in aspects as diverse as consciousness and comedy. Even the entertainment industry seemed to ride the wave with films like Ex Machina and Chappie performing well with critics and fans. And yet, according to venture capital database CB Insights, last year business investment (particularly on the corporate side) in AI slowed from the six-year high it found in 2014. Investment, it seems, didn’t match public and private interest.
Just a few weeks ago, the artificial intelligence community and board game community alike were shocked when an AI system named AlphaGo defeated an expert player five games straight in the sophisticated game, Go. The surprise wasn't so much that the system won, but that it was capable of winning nearly a decade before experts expected.
Go can colloquially be called the “chess of East Asia”. But Go actually trumps chess in terms of complexity and required intuition from players. By alternating black and white pieces on a grid of 19 horizontal and 19 vertical lines, players are challenged to surround and trap their opponents’ pieces. The result is a game with many more potential moves at a given time and no apparent method to determine any players specific advantage. Thus, Go demands immense practice and subtle, human-like intuition. Likewise, the system must be developed to process data more like a human than like a machine. Dennis Hassibis, head of the Google team that developed AlphaGo, told a press briefing after the win, “Go is the most complex and beautiful game ever devised by humans.”
In case we haven’t been cautioned enough about the threats of emerging artificial intelligence, a panel of academics addressed the American Association for the Advancement of Science (AAAS) on Sunday with a warning that advancements in intelligent and semi-intelligent automation could lead to overwhelming unemployment across many industries.
Machines’ ability to recognize patterns is yet to match our own, but their increasing sophistication in regards to tasks like speech recognition and data analysis has seen AI applied to real world applications such as autonomous driving. In this vain Bart Selman, professor of computer science at Cornell University, said, “For the first time, we’re going to see these machines and systems as part of our everyday life.”
The predicted success of self-driving cars may prove to be a blessing that greatly reduces car accidents, but – with 10% of U.S. jobs requiring some degree of vehicle operation – the technology will also undoubtedly effect the labor market. Moshe Vardi, professor of computer science and director of the Ken Kennedy Institute for Information Technology at Rice University, told AAAS, “We can expect the majority of these jobs will simply disappear.” He went on to suggest that the disconnect between the manufacturing industry and job growth is a result of automation. Though manufacturing volume is right now at its peak, U.S. manufacturing jobs are currently below the figures from the 1950s. He pointed to the 250,000 industrial robots in the U.S. and the increasing growth rate of their use.
What Vardi suggests will happen is “job polarization”, a phenomenon that emerges when high-skilled jobs demand complex human intelligence and low-skilled jobs are too expensive to automate. Thus, the middle ground jobs will be the easiest to automate, leading to greater economic inequality. Vardi also noted that although this issue is widely regarded as a threat that could make a huge impact on American economic life, there is no discussion of it in politics, particularly not in the presidential election. “We need to start thinking very seriously: What will humans do when machines can do almost everything?” he said. “We have to redefine the meaning of good life without work.”
Furthermore, Wendell Wallach, an ethicist at Yale University’s Interdisciplinary Center for Bioethics and the Hastings Center, said “There’s a need for concerted actions to keep technology a good servant and not let it become a dangerous matter.” He also proposed that 10% of AI research funding should be put towards studying the impact that AI machines will have on society, echoing Vardi’s concern that politics has failed to address the tremendous issue. “We need strong, meaningful human control,” he said.
How emotions influence consumer buying habits has long intrigued and evaded the business sector. Face recognition technology, once limited to security and surveillance systems, has made it possible to gauge more specific metrics to allow companies to predict consumer behavior and accelerate revenue growth.
Despite the progress made in artificial intelligence over the past few years, deep learning software still lags far behind the pattern recognition and learning capabilities of the mammalian mind. Where a human might be able to recognize an apple after seeing just a couple apples, even the most sophisticated deep learning software has to review hundreds of thousands of apples to identify one.
At the heart of our present day sharing economy is the often lauded, sometimes corrupted, and occasionally controversial open source model. Though the open source model has its roots in the early days of automobile development, our Internet age has proved an ideal medium for free licensing and distribution.
The world’s biggest names in technology – particularly those in Silicon Valley – have released their artificial intelligence technology via the open source model over the past few months in a domino effect that has made some of the most sophisticated AI programs available to anyone with Internet connection. In huge maneuvers, Google, Facebook, Microsoft, and China’s search engine giant Baidu have taken deep learning even deeper.
In November of last year, Google open sourced the software library for TensorFlow, the tech giant’s perceptual and language comprehension program. Though TensorFlow wasn’t the first open source AI software out there – software such as Torch, Caffe, and Theano – it is widely regarded as some of the most advanced AI algorithms in the world. Thus Google’s move to make TesorFlow open source marked an unparalleled step forward, which its competition couldn't resist but to follow.