We encounter AI when we ask a question of Siri, Alexa or Djingo, when we go through an automated passport control gate at immigration, and when our credit card company contacts us to ask about an unusual transaction.
This is on track with a 2017 Gartner prediction that by 2020, one in five interactions with your smartphone will take place via a smart assistant. There are enterprise implications and enterprise products, such as Voicera and AISense, that build in voice.
There’s more to come.
In July 2019, Nuance introduced a biometric voice recognition solution that can accurately identify a person by their voice, meaning that at some point, all you’ll need to do to take a ride in an autonomous car is a few words to make the vehicle recognize you. Depending on the kind of personal AI you carry with you, the car may already know where you want to go.
Orange Cyberdefense monitors networks and processes large quantities of data in near real-time in order to detect attacks. It uses machine learning algorithms to make this work more efficient in its battle against cybercrime.
AI is used to group and tag images in our Apple Photos. It is used in operating theaters when medical teams use an app called Triton Sponge to count and analyze blood loss during surgery. Network providers, including Orange, are using AI-based tools to enhance the operations and maintenance of their networks.
There are real world industrial implications, too. Orange partner Siemens is developing industrial IoT solutions based on the MindSphere open IoT operating system, and it hopes to build the AI-based technologies that will drive the future factory.
A 2018 Vantage Partners survey found that 97 percent of Fortune 1000 firms are investing in AI in a wide array of industries, from retail to medical to cybersecurity, fraud prevention and beyond. Amazon, for example, has cashier-free supermarkets and warehousing robots already in use.
Signify Research claims AI in medical imaging will become a $2 billion market by 2023, while Frost & Sullivan believes as many as 45% of U.S. operating rooms will be integrated with AI and VR technologies by 2022.
There are also implications in sport. At the Wimbledon Tennis Championships in 2019, IBM Watson was taught to recognize key moments during matches to help identify match highlights for broadcast. Orange demonstrated an AI-based Tournament Assistant at the French Open at Roland Garros.
What do organizations need to deploy AI?
A recent IDC study found that of those organizations using AI, just 25% had developed an “enterprise-wide” AI strategy – and that half of all AI projects fail.
“Organizations that embrace AI will drive better customer engagements and have accelerated rates of innovation, higher competitiveness, higher margins and more productive employees,” said IDC Artificial Intelligence Strategies Vice President Ritu Jyoti. “Organizations worldwide must evaluate their vision and transform their people, processes, technology and data readiness to unleash the power of AI and thrive in the digital era.”
Along with anachronistic internal cultures and unrealistic expectations, the survey identified the following challenges for successful deployment:
- The cost of AI solutions
- A lack of qualified workers
- Biased data
It is important to note that despite the potential of failure, for many companies a move to embrace AI has been highly beneficial.
More than 60% of companies reported changes in their business model in association with their AI adoption, while almost half have now developed frameworks to encourage the ethical use, potential bias risks and trust implications of AI, according to IDC.
For best results, organizations will need to develop an effective AI strategy that matches their business goals. They also need to look to innovate business models to better exploit the digital opportunity.
“Executives hoping to narrow the gap must be able to address AI in an informed way,” wrote Michael Chui, James Manyika and Mehdi Miremadi in McKinsey Quarterly. “In other words, they need to understand not just where AI can boost innovation, insight and decision making; lead to revenue growth; and capture efficiencies – but also where AI can’t yet provide value.”
What happens next?
Oxford University’s Future of Humanity Institute predicts AI will be capable of surgery by 2053 and of automating all human employment within 120 years. Carl Frey and Michael Osborne predict up to 47 percent of U.S. jobs are at risk of being automated.
Alarming stuff, but this must be seen within the context of an evolution:
“Every time humanity goes through a new wave of innovation and technological transformation, there are people who are hurt, and there are issues as large as geopolitical conflict. AI is no exception,” Fei-Fei Li, Director of the Stanford Artificial Intelligence Lab told CNN.
In China, the rate at which workers will be replaced by AI is estimated at 45%, according to research from the National Academy of Development and Strategy, Renmin University of China . This is prompting major action by the Chinese government as it fosters new ways of working and new skills.
While most experts agree that new employment needs will emerge as AI takes hold, the European Economic and Social Committee (EESC) believes that policies for the development, deployment and use of AI must be put in place to ensure that the overall effect is positive.
Economic stimulus to promote lifelong learning and support for communities exposed to such change are being discussed – it’s also possible AI will help resolve the problems it creates, as intelligent machines begin to train humans in the skills the machines themselves need to deliver even more productivity and efficiency in this brave new computer world.
Society and humanity aren’t the only evolutions taking place in AI. The technology itself is also improving.
Think back to Apple’s 1999 introduction of the Power Mac G4, a computer that Steve Jobs praised for achieving performance of one billion floating-point operations a second - a “gigaflop.” These days, we carry this kind of power in our smartphones – Apple’s iPhone XS offers 5 teraflops of performance.
Combined with rapidly improving GPUs and enhanced cooling and interconnect systems, it is rapidly becoming easier to build highly complex AI learning models that run natively on a device. Generation by generation, the processing power these algorithms can access grows to enable faster computational analysis of larger data sets using increasingly sophisticated AI models.
The result?
The AI in our devices, systems, networks and elsewhere is becoming capable of an increasingly wide range of tasks.
We’re a long way from machines that can replace humans, but the capacity of these machines to analyze and act upon the world around us in collaboration with humans has barely been explored since the technology moved from science fiction into science fact.
Challenges remain: not only does algorithm science continue to evolve (an evolution set to become increasingly rapid as the capacity of our hardware improves), but the data we feed these machines – and the learning we provide – must itself endure constant correction and analysis.
In its way, AI represents another step on any enterprise’s digital transformation journey. Sandra Ng, Group Vice President for ICT Practice at IDC Asia Pacific puts it quite well, saying:
“AI is creating a new paradigm for individuals, businesses, industries, economies and governments. It is shaping the future of intelligence in organizations and in workers.” Citing rapidly increasing enterprise use of AI-based voice assistants, Ng adds: “The race to the future enterprise has begun. No one and no entity will be spared of the need to at least reset or reboot, if not reinvent.
Accelerating information technology means information matters more than ever.
“We are witnessing historic economic theory being re-written with data becoming the fourth production factor, every bit as important in the modern economy as land, labor and capital,” wrote Orange Business CEO, Helmut Reisinger. AI is a revolution. You and your enterprise are already in it.
This is the final blog in a four-part series about how AI works, what data it needs and what happens when AI goes wrong. The other articles are: Everything you always wanted to know about AI (but were afraid to ask), Food for thought: why AI needs good data, and The secret life of algorithms.
Jon Evans is a highly experienced technology journalist and editor. He has been writing for a living since 1994. These days you might read his daily regular Computerworld AppleHolic and opinion columns. Jon is also technology editor for men's interest magazine, Calibre Quarterly, and news editor for MacFormat magazine, which is the biggest UK Mac title. He's really interested in the impact of technology on the creative spark at the heart of the human experience. In 2010 he won an American Society of Business Publication Editors (Azbee) Award for his work at Computerworld.