Ground Truth
The ACCJ launches its AI Futures Series with a look at infrastructure, energy, and economic security.
The ACCJ launches its AI Futures Series with a look at infrastructure, energy, and economic security.
Artificial intelligence (AI) is all around us. With each passing day, it seems that more of the tools we use are infused with the technology, more people and businesses are relying on it, and demand is forcing rapid reflection on how we work and how we feed AI’s thirst for resources.
American Chamber of Commerce in Japan (ACCJ) members and guests gathered on September 3 at the office of President’s Circle member Cisco Systems G.K. for the first in a series of signature events focused on the technology.
The first session of the AI Futures Series examined the physical and policy foundations that AI requires to scale in Japan.
ACCJ Digital Forum Co-chairs Shuichi Izumo and Ken Haig welcomed attendees and thanked sponsor Aflac and Cisco’s fellow President’s Circle members KKR and Eli Lilly Japan K.K. for their support of the event.
In his opening remarks, ACCJ President Eric John emphasized the importance of the infrastructure behind AI adoption—from semiconductors and data centers to telecommunications and energy.
“The resilience and security of this infrastructure will directly impact Japan’s ability to innovate and compete,” he said.
“The ACCJ strongly supports the Government of Japan’s ambition to make Japan the easiest country in the world for developing and utilizing AI,” he continued. “However, getting the policy balance right will be critical: protecting transparency and security while ensuring that regulation supports, rather than constrains, innovation and the adoption of new technologies.”
Bringing the Pieces Together
The discussion began with a panel moderated by ACCJ Governor Rebecca Green, who is also a partner and APAC technology industry lead at ERM. Offering their perspectives were:
- Nanako Tanaka, country director of government affairs and policy at GE Vernova
- Quint Simon, vice president of public policy for Asia-Pacific at Amazon Web Services
- Junichi Kitamura, director of government affairs and country lead at Micron Memory Japan K.K.
The group represented a range of elements critical to AI infrastructure: semiconductors, the cloud, and power generation. These areas are often discussed in separate rooms, and the goal of the first AI Futures session was to bring them together and highlight how they are interdependent when building a resilient AI supply chain that keeps up with demand and positions Japan as a leader in the field.
“If there is to be a prosperous path forward for AI in Japan, it must be charted by sharing lessons learned, presenting a common industry position, and engaging local governments jointly rather than company by company.”
A Question of Scale
The conversation opened on the sheer size of the moment. Global capital commitments to AI infrastructure now run into the hundreds of billions of dollars annually from hyperscalers alone. Hyperscalers are companies that provide cloud computing resources so businesses can access computing power, storage, and other services on demand.
That astounding investment total does not include outlays for AI labs and colocation providers. According to the panelists, the semiconductor market is on a trajectory that few forecasters anticipated even two years ago, driven in large part by demand for the memory that feeds data-hungry processors. The impact of such demand can be felt by everyday consumers in the rising prices of computers and smartphones caused in part by memory shortages.
Much of this investment has so far been concentrated in the United States. The open question, and the one the panel returned to repeatedly, is how much of the next wave lands internationally, and how much of that share can Japan secure.
The panel was clear that Japan enters this competition with genuine advantages: a strong rule of law, robust intellectual property protection, and a policy environment widely regarded across the region as forward-leaning. These all weigh in its favor, as does the depth of the US–Japan relationship itself. For investors evaluating assets with 15- to 20-year horizons, Japan’s reputation for deliberate, predictable policymaking is an asset rather than a liability.
Where the Friction Lies
But there are challenges. Energy cost and availability surfaced as the single most cited obstacle, followed closely by the state of the grid. Permitting regimes and technical regulations designed for traditional, non-IT facilities add further delay. So does the fragmentation of responsibility for critical infrastructure across prefectures, municipalities, and private utilities, which leaves companies negotiating with multiple parties on questions that elsewhere have a single owner.
Underlying all of it is a mismatch in timescales. Japan has a reputation for being slow-moving. The same deliberateness that reassures long-term investors becomes a liability on project timelines. Industry investment cycles turn over every two to three years, while transmission lines, water infrastructure, and land rezoning move at a pace of five, 10, 15 years or more. The discussion turned to jurisdictions where central government builds enabling infrastructure ahead of committed demand, and contrasted that with a chicken-and-egg dynamic over who takes the first risk.
Talent emerged as a related constraint. Decades of retreat from semiconductor manufacturing have left a generational gap, in engineers and in the professors who train them. This is compounded by shortages in construction labor and specialist trades.
On energy specifically, the discussion converged on two priorities: modernizing the grid, including transmission and interconnection, and expanding the role of nuclear power alongside renewables. Early nuclear power purchase agreements and colocation projects were noted as encouraging signals.
Trust as Infrastructure
Even if all these obstacles can be overcome, winning over everyday people is a must if Japan is to succeed in AI. Community opposition to data centers has become a live political issue in parts of the United States, and while Japan remains comparatively quiet, panelists asserted that social license should be treated as infrastructure in its own right.
Concerns vary sharply from site to site. While energy and water consumption are at the top of the list in news coverage surrounding AI pushback, noise from the construction and operation of data centers, as well as the impact on aesthetics and biodiversity, are also big concerns. The consensus was that industry, not government, must lead here, through transparency, listening, and evidence-based engagement with communities where they wish to operate.
That led to the conclusion: the need for collective action. If there is to be a prosperous path forward for AI in Japan, it must be charted by sharing lessons learned, presenting a common industry position, and engaging local governments jointly rather than company by company. The ACCJ, panelists agreed, offers a natural platform for such work.
In her closing remarks, ACCJ Executive Director Laura Younger expanded on the goals of the chamber’s exploration of AI:
“As we developed the AI Futures Series, we really wanted to look at AI through three different lenses as part of one broader conversation. Tonight we began with the foundations. We looked at infrastructure, energy, and security. In the next session, we are going to start looking at application, what it means for business, and then we are going to broaden the lens even more and to what AI actually means for society, for people—really defining what it means to be human.”
Beyond the Answer
As AI becomes both coder and tutor, teachers ask what students still need to learn and what role they should play.
As AI becomes both coder and tutor, teachers ask what students still need to learn and what role they should play.
According to Satya Nadella, artificial intelligence (AI) writes 20 to 30 percent of the code in Microsoft’s repositories, with some already passing the halfway point. Sundar Pichai puts Google’s figure at roughly a quarter to a third. At my own startup with 15 engineers, no one handwrites code anymore. Two years ago, any of these claims would have sounded like science fiction.
Linus Torvalds, who created Linux, the operating system that powers every Android phone and most of the world’s servers, has pointed out that compilers write 100 percent of everyone’s code and nobody boasts about that. He is right that this has happened before. The difference is that a compiler is reliably correct, and a large language model is not.
If professional software is increasingly written by machines, what do schools have left to teach?
I put that question to four people from very different disciplines who do this for a living: in schools, in coding boot camps, in research, and in the creation of Japanese school textbooks. Surprisingly, there was a good deal of consensus.
That Which Survives
Simon Guest, a computer science educator and former chief technology officer of Code.org, the nonprofit behind the Hour of Code and the largest provider of free computer science curriculum in US schools, teaches AI at both the high school and undergraduate levels. He explains what he thinks endures using house construction as a metaphor.
“The student could ask an AI to ‘build me a house’ in one shot and simply accept whatever it produces,” he said. “Or they could design the house first, deciding on the structure and what each room needs to accomplish, and then use AI to help build each part. Using the second approach, AI may still do much of the construction, but the student is the ‘builder’ of the system.”
Stefania Druga holds a PhD in AI tools for creative coding, created the Cognimates platform, and is currently a staff research scientist at Sakana AI in Tokyo. She arrived at the same place after a decade of studying children learning alongside these tools. When agents write more of the code, she said, “knowing how to decompose a problem, specify what you want, and evaluate what comes back matters more, not less.”
Kani Munidasa cofounded Code Chrysalis in Tokyo in 2017, initially helping people transition into software engineering, and today works primarily with companies to develop their in-house software development and organizational capabilities. He put it in the language of hiring.
“As building software becomes faster and cheaper, the scarce capability is increasingly not simply, ‘Can you code?’ but ‘Can you decide what to build, and can you build it effectively as a team?’ That’s an organizational capability,” he said.
This is not new. I have hired senior engineers for 15 years by asking why they built the thing they are describing—what prompted the migration, who decided, and whether they understood the reasoning. If somebody simply told them to do it, the interview is largely over. What’s new is that I now ask the same of juniors. The judgment that used to earn a promotion is becoming the price of entry.
AI as Teacher
Let’s do a little philosophical exercise. AI could be the perfect teacher. It is infinitely patient. It never tires of the same question asked a fourth time. It explains at whatever level you need, in whatever language you speak, and it will do it at two in the morning.
Munidasa does not hedge: “I think AI will eventually do a lot of what we traditionally think of as teaching, and probably do some of it better than we can,” he said. “It can already explain concepts, answer questions endlessly, adapt to the individual, review code, generate exercises, and give immediate feedback. We shouldn’t pretend those capabilities are somehow protected from AI.”
Of the years Code Chrysalis spent running consumer boot camps, he said: “People weren’t paying us for access to information. Even when we started, you could learn JavaScript, algorithms, databases, or almost anything else online for free. What they were paying for was an environment designed to transform how they learned and worked.”
The evidence for that predates AI. When Harvard put CS50, its introductory computer science course, online for free, 150,349 people registered in the first year. Just 1,388 of them finished. On campus that same year, 703 of 706 students completed the identical course, taught by the same instructor from the same material.
Guest does not think the difference is pedagogical.
“When a student signs up for an in-person course, they’re accountable to another person,” he said. “If they don’t show up or complete the work, there’s a teacher or professor who notices.” Online, most of that disappears. Anyone who has joined a gym in January and stopped going by spring already knows how that story ends.
“I am much more optimistic about AI as a ‘teaching assistant’ rather than a teacher—an AI that can provide immediate, personalized help, while a human teacher remains responsible for the relationship, motivation, and accountability,” he said.
“AI may become an extraordinary teacher, but education still has to develop the human being.”
Who It Fails
“The people who can benefit most from self-directed AI learning are often the people who have already learned how to learn without it,” Munidasa noted.
“The danger,” he said, “is when you don’t yet know what you don’t know. AI can give you an answer that feels complete, and even produce something that works, without you understanding why it works.”
Druga explained it this way: “An answer machine optimizes time to answer. A teaching tool optimizes what you can do after it leaves the room.”
In her most recent study, North American participants already had AI at home, while several from other regions had never used it. “The tool amplifies whatever head start a child already has,” she said. “That is why access has to be designed, not assumed.”
The Adult in the Room
Japan has already tested whether access on its own is enough. In 2018, Japanese students spent less classroom time on digital devices than students in any other country in the Organisation for Economic Co-operation and Development. The government’s response, announced the following December, was the Global and Innovation Gateway for All, or GIGA, School Program, which put a networked device in the hands of every public school student, with 1,769 local governments, or 97.6 percent, having completed planned delivery by the end of March 2021.
Distribution was never the hard part.
The education ministry’s own reviews now describe uneven use between regions and between schools.
The constraint is the adult at the front of the room. A survey by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) for FY2022 found that 23 percent of junior high school teachers responsible for technology were teaching under a temporary license or an out-of-field arrangement.
That is easy to read as a competence problem, but I do not think it is one. “The gap is teacher confidence, and tools built for learning rather than for answers,” Druga said.
Ayana Murakami, who works on the digital content in Japan’s authorized junior high school textbooks and is a software engineer and PhD candidate at Ochanomizu University, describes a second structural problem. “The national curriculum is revised roughly once a decade, and junior high school textbooks follow a four-year cycle, while AI can change dramatically within months.”
The ministry appears to agree. Its budget documents describe a new junior high subject under consideration, provisionally called Information and Technology, and say work on teaching materials will begin without waiting for the next curriculum revision to take effect.
Murakami is not pessimistic about schools. Asked whether compulsory education is better placed than self-directed learning to help students understand the potential, risks, and use of AI, she said: “AI makes it easier to learn on your own, but it can still be hard to study things you are not interested in. Schools provide a common foundation by giving students a structured learning environment.”
What’s Left
Having granted AI that much, Munidasa draws a conclusion that runs against his own interest. “But if that’s all a boot camp does, then yes, I think AI should replace it.”
What survives is the part that was never about information. “You don’t learn teamwork by reading about teamwork. You learn it by having to build something with another human being when you don’t agree.”
Asked what she would fund in Japan if she could fund one thing, Druga did not name a tool for learners. She chose “AI tools that assume a teacher is present and make her better.” Every person I spoke to for this piece said a version of the same thing.
A country where every child has a device, a teacher, and a teacher confident with both would be the first of its kind. Japan already has two of those three.
The third is the hardest. Teachers need confidence with these tools, as well as AI built for learning rather than for answers. Current AI products will not do that out of the box. They are built to answer. So tools that enhance and support the learning process have to be built.
MEXT is reconsidering the curriculum now, and the obvious move is to add AI to the syllabus. But nothing in a syllabus reaches a student except through a teacher. What we do know is that accountability matters more than it did, and that what is worth teaching is the reasoning rather than the result. As Munidasa put it: “AI may become an extraordinary teacher, but education still has to develop the human being.”
AI in the Workplace
As artificial intelligence gets its own desk in the office, workers and leaders weigh its risks and rewards—and whether age shapes their view.
As artificial intelligence gets its own desk in the office, workers and leaders weigh its risks and rewards—and whether age shapes their view.
A client question nobody has an answer for leads to a stalemate in the middle of a meeting. One colleague takes the old-school approach and books another meeting for next week, spending the days between sketching notes. The other opens an artificial intelligence (AI) tool and has a workable plan within the hour.
That contrast, said Roy Tomizawa, chief executive officer of Reinventing Asia, is the story of AI in the workplace: it is changing the distance between thinking and acting.
“So much organizational time is spent waiting and procrastinating,” Tomizawa said. Unread emails and slow-to-schedule meetings are a kind of quiet tax that most companies never measure. “Half of what was discussed could have been thought through, drafted, or prototyped beforehand by the person who leveraged the available AI tools on his or her own.”
How businesses close the gap between thinking and acting is dividing opinion, forcing leaders to decide what AI should handle and what it shouldn’t, while young professionals are discovering that the skills worth building are shifting day to day.
The Age of the Generalist
Tomizawa explained that the shift goes beyond speed. AI is dissolving the old dependency on specialists. An employee who knows how to assemble credible first versions of a website or a video will have an advantage. Tomizawa called this “the age of the generalist.”
But that doesn’t flatten the value of expertise, he said.
“It actually raises the price of real expertise, since shallow, surface-level work is now cheap and abundant.” Someone still has to know when an AI’s answer is actually good, he added. Without that, the same tools that make people efficient just make them “efficient masters of AI slop.”
Sludge, Slop, and Everything in Between
Not everyone sees the acceleration in such rosy terms. Dr. Greg Story, president of Dale Carnegie Training Tokyo, compared the effect of AI on communication to that of social media. Both are technologies that can make people more insular, offering contact with or through a machine as a substitute for real contact with each other.
Story, who has written nine books and recorded thousands of podcast episodes, said listeners can identify his writing by ear. Hand that same task to AI by default, he argued, and you never develop a personal voice.
“If you’re ever going to have a distinctive style, and you keep outsourcing it to AI, then you’ll never achieve that and you’ll be part of the sludge,” he said. “There’s no discrimination between one piece of sludge and the other.”
For most professionals, the risk, he argued, is that AI tends to default to generic text unless given deliberate direction. That caution shows up in how he runs his own training business. Dale Carnegie has built its own AI system, trained only on its own material, to keep answers on brand rather than generic. But Story keeps it confined to prework and follow-up, away from the live instruction that’s actually being sold.
In sales, he explained, AI becomes a genuine research tool for those who ask the right questions, provided someone checks it for hallucinations—the tendency for AI to make up information when it doesn’t have an answer.
“The human dimension is being reduced,” he said, “so we have to hold on to it as much as we can to counterbalance the swing to impersonal communication.”
“AI is dissolving the old dependency on specialists. An employee who knows how to assemble credible first versions of a website or a video will have an advantage.”
Finding a Voice with AI
Ted Katagi, CEO of Kenja K.K., builds tools based on retrieval-augmented generation, or RAG, that draw only on a company’s own material rather than the open internet, including the system behind Dale Carnegie’s own AI tool. Katagi makes a distinction between content and style.
“We have to separate things—the voicing of it, which to me is the style, versus the content,” he said. The content has improved with AI use, he said, but the style “will tend to aggregate” as writers rely on it. Part of that, he argued, is simply that AI eliminates bad writing—which raises its own question. “If we get rid of the bad writing, isn’t that going to make you a little bit more similar in a sense?”
Preserving an individual voice, he said, takes deliberate work. “I wouldn’t just say, ‘Here, AI, write like me.’ I would say, ‘Here are all my papers. How do you think I write?’” Skip that step, he warned, and a personal voice doesn’t get replaced so much as quietly forgotten.
Katagi’s advice to young professionals is to treat AI more as a “sparring partner” than a first-drafter.
“Ask always: What did it do that was smart that I need to learn?” Katagi advised. “What did it do that was bad that I need to check on?” For those who genuinely engage with AI, it “can seem like they gain 10 years of experience.”
“It will get you where you want to go and it will help you finish the last mile,” he said. But human judgment and intuition are still needed. He cited as an example a recruiting-industry deployment that builds an editor directly into the workflow rather than letting AI hand off a polished document unchecked.
Differentiating from Sameness
Meghan Barstow, president and representative director of ACCJ Corporate Sustaining Member Edelman Japan K.K., has watched two prior technology shifts reshape her industry: the internet and mobile phones.
“I experienced the advent of the internet and mobile phones,” she said, “both of which transformed how we create, communicate, and connect.” She expects AI to do the same, viewing it as a tool that “aids and amplifies human creativity rather than replaces it.”
Her concern is closer to what Story called sludge. Barstow called it “sameness.”
“AI is incredibly good at producing something plausible. But plausible isn’t necessarily interesting, original, or culturally meaningful,” Barstow said, adding that her worry is more about the behavior it enables.
“If we all use the same tools, trained on much of the same material, and accept the first answer they give us, there is a real risk that we will create more content but with less creativity.”
As an executive, Barstow, who is also an ACCJ vice president, described adopting AI as “quite fun,” but said the stakes reach into business models themselves, with real winners and losers ahead. Organizations that treat AI as additive to their core value rather than a replacement for it will succeed, she predicts.
For young professionals, she offered this advice: “Raise your hand. Get involved. Be curious. Learn, learn, learn. Use the new tools, absolutely, but don’t do so at the expense of learning the fundamentals or having experiences that develop your judgment and perspective.”
She also viewed the generational exchange as a two-way street. Edelman brought interns back this year for the first time since the pandemic, several through the ACCJ’s own internship portal, and Barstow credited them with making the organization sharper.
“The more opportunities we create for generations to learn from one another, the better all of us—and our organizations—will be,” she said.
What Leaders Need to Know
Digital fluency doesn’t mean becoming a technologist, Tomizawa said. It means understanding how value is created and decisions are made. He said three mindsets matter most:
- Treating information as a strategic asset
- Measuring outcomes, not activity
- Introducing AI deliberately
By treating information as a strategic asset, Tomizawa means that it is important to know where critical data sits, who owns it, and whether it’s reaching the people making decisions. Once that is in place, the value of the technology must be assessed. “High usage of an AI tool, for example, tells leaders little about whether people are saving time, making better decisions, improving customer outcomes, increasing revenue, or reducing risk,” he said. When preparing to implement AI tools, leaders should start by looking at the work itself to find the bottlenecks and errors before choosing the technology. Finally, they should define exactly what AI should recommend and where human approval stays mandatory.
Story said that leaders rarely had time to check employees’ work before AI, and they have even less reason to start now. His solution is to train people on privacy and instill in them a healthy skepticism.
Cybersecurity is another area to be mindful of.
Katagi said a clash between cybersecurity’s oldest principle and AI’s newest capabilities exists, pointing to “zero trust,” the model he said most cybersecurity experts rely on now.
“Zero trust means you say you’re Chris and it looks like it, but I’m not going to trust it until I see some irrefutable proof that you are who you say you are,” he explained. Agentic AI, a system trusted to act on a user’s behalf that Katagi said is at the center of AI right now, asks for the opposite. “Basically, what it’s allowing you to do is say, ‘Okay, trust me and I’ll do the thing.’ But that’s going directly against all the cybersecurity frameworks that people have.”
“The professionals who thrive will be the ones who know when to trust it, when to challenge it, and when to think differently.”
A Youthful Approach
Among young professionals, thoughts on AI lean more toward the opportunities the technology brings.
“AI is one of the most significant shifts I have seen in how we work, and I think young professionals have a real opportunity to shape how it is adopted,” said Fatim Diallo, a director at FGS Global and vice-chair of the ACCJ Young Professionals Forum (YPF). “Used well, it frees up time for the kind of creative thinking and relationship-building that transforms careers.”
The shift has made her more curious about where she can add value than anxious about what AI might replace. At a company dealing with multiple markets, AI has “meaningfully reduced the friction of working across linguistic and cultural contexts,” she said.
Her main concern is what she dubs a “meat proxy”—people who pass AI output straight through without reviewing or questioning it.
“The professionals who thrive will be the ones who know when to trust it, when to challenge it, and when to think differently.”
Kelly Langley, CEO of Gemini Group K.K. and co-chair of the YPF, noted a similar shift in what makes someone valuable. AI increasingly handles the “first 80 percent” of a task, he said, freeing people to spend more time exercising judgment on the final 20 percent.
“That makes me more interested in capabilities that are difficult to replicate: judgment, trust, persuasion, relationship-building, improvisation in moments of crisis, and understanding what motivates people.”
Langley pushed back on the idea that there is a generational divide among AI adopters.
“The bigger distinction is between people who are relentless learners and early adopters and those who remain comfortable with established ways of working.”
Progress in the Age of AI
The next wave of AI innovation isn’t going to come from the massive, companywide rollouts most large organizations are currently betting on, Katagi said.
“People think the big projects are the thing. I think the next innovation is going to occur in agile projects at the middle level,” he said, predicting smaller teams and narrower budgets focused on solving one specific problem well rather than applying a general-purpose tool everywhere at once.
Story said that “at the end of the day, most business problems are people walking around on two legs talking to each other. That doesn’t go away, but the skill sets are going away in a sense, as people become more isolated.”
But he sees an opportunity ahead in the people using the tools themselves.
“As we’re moving away from voice and body language, we are getting to a stage where AI is just going to accelerate that process,” he said. “If you have the ability to communicate, you’re going to stand out head and shoulders above everyone else. You’ll be clear, empathetic. I think that’s an opportunity rather than a threat.”
Tomizawa argued that most companies are still years from redesigning how work actually gets done around AI, rather than just using it to make things faster.
“The more capable AI becomes, the more important human judgment becomes in deciding what problem deserves attention, what ‘good’ looks like, and which trade-offs are acceptable,” he said.
Five years from now, he added, the people with the greatest value will be the ones who know how to combine machine capability with sound human judgment.
Unified and Secure
As artificial intelligence sweeps the world, there is growing concern. Cisco is ensuring that it is powerful, efficient, and safe.
How Cisco is ensuring that artificial intelligence is powerful, efficient, and safe.
As artificial intelligence (AI) sweeps the world and finds its way into nearly everything we touch, there is growing concern about its safety. The breakneck pace at which companies are deploying this emerging technology—whose behavior is not fully understood—has some people delighted and others sounding alarms.
American Chamber of Commerce in Japan President’s Circle member Cisco is committed to securing AI technologies by integrating advanced measures and encouraging a culture of innovation and responsibility. By doing so, the company is ensuring that AI solutions are not only powerful and efficient but also safe and reliable for users in Japan and around the globe.
On March 26, Cisco hosted the AI Infrastructure and Security Summit at the ANA InterContinental in Tokyo for customers, partners, analysts, and the press. During the event, the tech innovator presented its latest solutions to promote further safe and secure utilization of generative AI by Japanese companies and local governments.
“To address the increasing challenges of managing AI security risks, Cisco’s latest innovations harness agentic AI to empower IT professionals with advanced tools for efficiently managing threats and streamlining security operations,” Cisco Japan President Yoshiyuki Hamada told The ACCJ Journal. “These innovations simplify the complexities of navigating the AI era, enabling security teams to stay ahead in today’s dynamic landscape,” he explained.
Thinking Machines
Agentic AI is a form of artificial intelligence that acts with agency to achieve specific goals—essentially a machine that can take on tasks and make decisions on its own. Jason Clinton, chief information security officer of Anthropic, developer of the Claude large language model, told Axios in April that he expects to see fully AI employees become part of companies in 2026.
“Agentic AI systems can make rapid decisions, manage complex tasks, and adapt to changing conditions,” Denise Shiffman, senior vice president of networking strategy and marketing explains on Cisco’s official blog. “They have agency to reach beyond the data their large language model was trained on and interact with external environments, such as IoT sensors, cloud platforms, [and] analytics software. The possibilities are endless for what an agentic AI system can achieve in improving customer experiences, increasing productivity, and creating new innovation.”
Such independent technology might give many IT departments pause. Clinton noted when speaking to Axios: “In that world, there are so many problems that we haven’t solved yet from a security perspective that we need to solve.”
This and other AI-related challenges are why Cisco has formed a partnership with chipmaker Nvidia to provide enterprises with an AI factory architecture that puts security at its core.
Called Cisco Secure AI Factory with Nvidia, these are data centers purpose-built for AI workloads that dramatically simplify how enterprises deploy, manage, and secure AI infrastructure at any scale.
Safe in the Fast Lane
At the core of these data centers is Cisco Silicon One, a unified network silicon architecture that facilitates fast switching and routing. Launched in 2019, the family of network processors is now in its fourth generation. Whereas a CPU is the brain of a computer or smartphone, Silicon One is the brain of high-performance routers and switches. The top-end G200 model is capable of routing data and switching traffic at 51.2 terabits per second—speeds essential for handling the massive amounts of data flowing among AI clusters.
“We are collaborating to deliver networking technology solutions through a unified architecture, with a focus on simplifying and optimizing customer experiences,” explained Hamada. “By enabling interoperability between Cisco Silicon One and Nvidia Spectrum, as well as their respective networking architectures, the partnership aims to provide full-stack solutions that prioritize customers’ needs.”
This approach, Hamada added, allows customers to maximize AI infrastructure investments while leveraging existing management tools and processes across both front- and back-end networks. “Additionally, the collaboration creates new market opportunities for Cisco by streamlining the management of enterprise and cloud provider networks through a unified architectural model.”
A New Architecture for Security
It seems as if every application you launch in 2025 is brimming with AI. Document readers want to save you time by offering a summary. Mail applications want to pull out key points and reply on your behalf. And large language models are handing smart assistants and search engines their coats and hats as they see them to the door. How we interact with devices and information is changing in the blink of an eye, and the amount of data being passed around is growing exponentially.
Networks and data centers as we’ve known them are often not up to the task of securing the high-performing, scalable infrastructure and AI software required to develop and deliver AI applications. A new architecture is needed—one that embeds security in all layers of the AI stack and automatically expands and adapts as the underlying infrastructure changes.
This is where the strengths of, and synergy between, Cisco and Nvidia technologies come into play.
“Cisco and Nvidia are collaborating to deliver networking technology solutions through a unified architecture, with a focus on simplifying and optimizing customer experiences,” said Hamada.
Cisco Secure AI Factory with Nvidia is expected to build on the companies’ unique abilities to offer flexible AI networking and full-stack technology options that leverage the planned joint architecture. The partnership will bring together technologies from Cisco, Nvidia, and our ecosystem partners into a secure AI factory architecture for enterprise customers.
The State of AI Security
Cisco did extensive research in developing the Cisco Secure AI Factory with Nvidia and its solutions such as Hypershield and AI Defense.
The company recently published the Cisco State of AI Security Report, which analyzes dozens of AI-specific threat vectors and more than 700 pieces of AI-related legislation to highlight key developments in a rapidly evolving AI security landscape.
In its conclusion, the authors note that the report “validates that the AI landscape has and continues to evolve rapidly. As we drive towards future breakthroughs in AI technology and applications, Cisco remains committed to AI security through our contributions to the community and cutting-edge solutions for customers pushing the envelope of AI innovation.”
AI 1988
Look back at how members viewed the potential of AI in 1988 in this reprint from the ACCJ Journal archives.
Look back at how members viewed the potential of AI in 1988 in this reprint from the ACCJ Journal archives
Artificial intelligence (AI) is becoming more and more widely used in Japan. Companies in this country are coming to realize that if they want to stay internationally competitive, they have to incorporate this technology. Davis is a popular contributor to these pages who specializes in high-technology subjects.
Japan’s large electronics companies are constantly looking for ways to boost the efficiency of their administrative work while searching for new markets so as to diversify their business. AI, a type of sophisticated computer programming that promises to revolutionize many job-related tasks, is catching on among electronics companies and software businesses here, and is also a target of interest among trading houses. All of these enterprises want to establish a foothold in this up-and-coming technical sector so as to enhance their long-term prospects. AI systems in the years to come may make or break certain companies in highly competitive areas of business. It would not be an exaggeration to say that a “mini boom” is being seen today in Japan’s AI sector.
AI systems under development here are intended to address bottlenecks within corporate product-development departments, and they are being marketed to outside customers, sometimes together with special types of data processing equipment. One of the best ways to sell more computer hardware is to market such equipment with an emphasis on higher value-added features, such as the ability to effectively handle AI writing tasks. However, Japanese companies in this field are all well aware that they have to contend with the likes of Symbolics Inc., a Cambridge, Massachusetts, global leader in AI workstations.
Two years ago (in 1986) the Artificial Intelligence Association of Japan was established in Tokyo by electronics companies, telecommunications businesses, software houses, and others interested in new developments in computer programming. The association cultivates exchanges between researchers in various AI-related fields and disseminates technical information to its members. Moreover, the association promotes specialized training of so-called knowledge engineers and other experts needed for the advancement of the new discipline. Establishment of the special association signifies the maturation of the initial commercial phase of AI here.
In contrast to Japan’s AI infrastructure, state-of-the-art American AI work is typically dominated by clusters of small businesses mainly located around major universities. In fact, many Japanese AI specialists have studied at leading US universities. As a result of the difference in the two paradigms, the large electronics enterprises of Japan have tremendous potential resources to devote to AI studies, whereas in the US, venture capital must typically be raised to fund much of the innovative work in AI.
“The global market for AI systems is likely to grow to as large as much as $10 billion per year sometime between 1995 and 2000, according to Japanese electronics industry estimates.”
The Japanese approach to the AI business often relies as much on proximity to leading US universities as it does on relationships with the top Japanese universities. In other words, Japanese universities are not major actors within the immediate sphere of AI business here. The paradigms are not without exception, however, because some smaller businesses in Japan, such as CSK Corp., are doing work in the field as well.
As AI is widely considered a promising growth market within the information processing sector, electronics companies are offering products that will allow users to develop their own AI systems, such as so-called expert systems. This customized programming is developed on the basis of experts’ knowledge; hence expert systems comprise handy tools for novices—so that they may easily draw upon the comprehensive knowledge of specialists to assist them in complicated tasks, such as writing specific types of software programs.
The global market for AI systems is likely to grow to as large as much as $10 billion per year sometime between 1995 and 2000, according to Japanese electronics industry estimates. The leading AI language today is LISP (LISt processor), and it is widely expected to retain its front-running position. Four of the largest AI applications expected in the mid-to-late 1990s are those for integrated circuit design assistance, manufacture planning, financial planning, as well as computer systems diagnosis and maintenance.
An example of a medical application of AI systems is the so-called RINGS program—rheumatology information counseling system—developed recently by Nippon Telegraph and Telephone Corp. and a medical college in Tokyo. The system is used by those suffering from rheumatism to help them in diagnosing minor problems over the telephone. When more serious problems arise, doctors are to be consulted. A variety of other medical-related AI systems are now under development, in part because the medical sector is likely to see rapid growth due to the aging of Japan’s population.
In the area of nuclear power plant operations, a group of Japanese enterprises is developing an expert system to enhance the safety of pressurized water reactors (PWRs). The LISP-based expert system is intended for use in new types of PWRs to be operated by Kansai Electric Power Co., Inc. and three other electric utilities.
Greater safety in operating nuclear plants can lead to enhanced profits for the utility companies, as they will not need to shut down reactors for prolonged periods in order to do repairs, precautionary tests or other types of maintenance.
The most prominent of Japan’s AI-related development programs is the so-called fifth-generation computer project, which is administered by the Institute for New Generation Computer Technology (ICOT). The institute was established in 1981 under funding from the Ministry of International Trade and Industry’s (MITI’s) Machinery and Information Industries Bureau.
“Although today’s AI systems can only cope with surface level knowledge, those of the year 2000 are likely to be capable of dealing with more abstract forms of knowledge. ”
Altogether, there are nine private companies participating in the project. Researchers based at MITI’s Electrotechnical Laboratory (ETL) in Tsukuba, Ibaraki Prefecture, are also involved. Moreover, the ETL, which is administered by MITI’s Agency of Industrial Science and Technology, is doing its own independent work in the field. Six to eight researchers from each of the electronics companies work at the ICOT center in Tokyo, and then only for periods generally ranging from two to four years.
When the project began in 1982, it was the subject of considerable attention throughout the world, due to its bold proposals and the perceived threat that it posed to the American and European computer software industries. However, recently it has not attracted much interest because Americans and Europeans have been less than impressed by the meager results of the project. US interest in the ICOT project led to the establishment of Microelectronics and Computer Technology Corp., a research consortium headquartered in Austin, Texas.
AI systems have a long way to go before they reach a phase of maturity. Although today’s AI systems can only cope with surface level knowledge, those of the year 2000 are likely to be capable of dealing with more abstract forms of knowledge. Advances in the memory capacity of computer microchips, parallel processing capabilities of computers, data processing speeds, and knowledge bases will accelerate the progress of the AI business sector.
Let us hope that people will always be able to keep the upper hand of control on such advanced tools as AI systems, and that the sophisticated tools won’t ever “discard” the humans they are supposed to be helping.
Your Own Private AI
As AI evolves, businesses are turning to custom LLMs to unlock corporate resources.
As artificial intelligence evolves, custom systems unlock business resources
Konosuke Matsushita was one of Japan’s greatest entrepreneurs. As the founder of a light socket company that evolved into Panasonic, he inspired legions of salarymen with his business wisdom. Twenty-five years after his death, the “god of management” was effectively resurrected as an artificial intelligence (AI) model. A chatbot trained on his writings and speeches can produce eerily lifelike Matsushita answers, according to one relative, and will eventually be used to make business decisions. It’s a dramatic example of how businesses are using AI to leverage intellectual property built up over decades.
The past few years have seen an explosion of AI applications based on large language models (LLMs) and tools such as OpenAI’s ChatGPT and Google’s Gemini. They have been used for everyday tasks such as writing text for slide decks, lessons, and articles, as well as synthesizing search results as in Google’s AI Overview that now appears with most searches.
LLMs are based on computational systems using neural network transformers that perform mathematical functions. Measured by the number of parameters they contain, LLMs learn by analyzing vast amounts of text from books, websites, and other sources. During training, the model identifies patterns, relationships among words, and sentence structures. This process involves adjusting millions of parameters—values that help the model predict what comes next in a sequence of words.
A major problem with LLMs and generative AI, however, is that they usually draw entirely from online content and thus are prone to inaccuracies. AI hallucinations, as they are called, occur when LLMs observe patterns in the data that are nonexistent, or at least imperceptible to humans.
One solution is private AI. It brings the power of LLMs inside a company, where queries are secure and limited to the company’s own data, reducing the risk of security leaks and incorrect or misleading responses. Private AI has traditionally been limited to government, defense, finance, and healthcare users, but it’s spreading to a broader spectrum of industries due to fears about intellectual property theft.
“[Private AI] brings the power of LLMs inside a company, where queries are secure and limited to the company’s own data, reducing the risk of security leaks and incorrect or misleading responses.”
Kenja KK, a member of the American Chamber of Commerce in Japan (ACCJ), is opening up the market in Japan to private AI. The Tokyo-based company offers AI solutions for enterprises that include purpose-built expert systems, incorporating a relatively new AI technology called retrieval-augmented generation (RAG).
Bearing a name coined as recently as 2020, RAG relies on a predetermined collection of content to improve the accuracy and reliability of generative AI content. Kenja offers a self-service plan for small and medium-sized businesses and a more comprehensive enterprise plan for businesses.
“Private AI is the next frontier,” said Kenja founder and Chief Executive Officer Ted Katagi, who is also chair of the ACCJ’s Marketing and Public Relations Committee. “All companies face the same issues: you have very sensitive data that you don’t want to make accessible to everybody at the same time. Private not just in terms of someone outside the company, but within the company, too. You may not want HR data to be shared with people in finance, for example. That’s an issue you want to solve, and we solve that.”
Kenja users create so-called rooms where they can upload thousands of documents or other content, organizing this into topic-specific folders. The process can be automated, and Kenja can train and fine-tune the system. For instance, it can be taught to forget certain words or trained to understand a balance sheet in order to do financial tasks such as due diligence.
“You are kind of building a wall around a set of information and telling it to only use what’s in this area,” explained Katagi. “Having 85–90 percent accuracy—which is what current generative AI, such as ChatGPT, Gemini, or Claude, will give you—is not good enough. Private AI models that are fine-tuned and query a closed set of materials can close that gap.”
Private AI is being used in surprising applications. Just as Panasonic has cloned its founder in digital form, Dr. Greg Story is using Kenja to share the teachings of another business luminary, Dale Carnegie. The self-improvement guru from Missouri wrote a book in 1936, How to Win Friends and Influence People, that still counts among the world’s all-time bestsellers. As president of Dale Carnegie Tokyo Japan, Story has been teaching Japanese businesspeople about leadership, communications, and other skills in Dale Carnegie seminars for the past 14 years. Dale Carnegie started in Japan in 1963.
Since learning about the impact of content marketing, he has built up an enormous corpus consisting of white papers, e-books, printed books, course manuals, 270 two-hour teaching modules, as well as video and audio recordings that include hundreds of podcast episodes. He has penned a series of books himself in English and Japanese that includes Japan Sales Mastery, Japan Business Mastery, Japan Presentations Mastery, and Japan Leadership Mastery.
“If you like the cut of our jib and you want a Dale Carnegie point of view and a curated, trustworthy response, we provide that through this AI.”
The material was scattered in different places, and when clients began asking for on-demand training, Story decided to get ahead of the curve by including all his company’s content in AI-curated form, something public chatbots cannot do.
“ChatGPT will give you everything it can scrape together, but it’s everything and therefore nothing,” said Story. “You get generic answers, and you don’t know if they’re trustworthy. But if you like the cut of our jib and you want a Dale Carnegie point of view and a curated, trustworthy response, we provide that through this AI.”
Story thinks the technology can benefit businesses that have substantial bodies of work to draw on, but those that don’t will get thin answers. He adds that using tools such as those from Kenja will not only help his company learn about the benefits of AI, but it will also give it an edge over competitors. He plans to roll out his AI offerings in 2025, delivering customized responses to students’ questions in English or Japanese on topics ranging from sales to diversity, equity, and inclusion.
Could there be a Dale Carnegie version of the Matsushita chatbot one day?
Kenja has begun working with Dale Carnegie’s global team to do just that, and has developed a prototype revival of Dale Carnegie’s voice, avatar, and writing style. The writing style and word generation are done with Kenja RAG AI technology.
“Carnegie became a global superstar in a non-digital world,” noted Story. “There’s no question we can get an AI to read a script generated in his style, in his voice. It’s amazing.”
AI Audits
AI's ability to analyze considerable amounts of information quickly offers great potential for auditors. How is this rapidly evolving tool impacting the audit process?
What impact does artificial intelligence have on auditing?
In the Fourth Industrial Revolution, with increasingly developed technology, the application of artificial intelligence (AI) has become popular both in everyday life and work. Thanks to AI’s outstanding features, multiple tasks can be performed in less time and with less effort. For this reason, AI is playing an important role in some areas which require the processing of large amounts of information, such as auditing. So how does AI affect the work of an auditor?
AI is defined in the Oxford English Dictionary as “the capacity of computers or other machines to exhibit or simulate intelligent behavior; the field of study concerned with this.” In later use, it is also defined as “software used to perform tasks or produce output previously thought to require human intelligence, especially by using machine learning to extrapolate from large collections of data.” In other words, AI is used to perform tasks that require human intelligence through programmed algorithms.
AI processes supplied information and produces results after conducting an analysis. Thus, AI can convert huge amounts of data in a short time and increase work efficiency.
With those exceptional aspects, AI can be applied at many stages of the audit process. How is it used and how does it affect the efficiency of the audit?
This can be broken down into three stages:
Audit Planning
Based on the customer data input, AI will propose appropriate audit procedures to optimize the audit plan.
Risk Assessment
Using the provided information, AI will analyze past trend fluctuations and financial indicators. Since AI can process and synthesize considerable amounts of information, the analysis will be more specific and more effective, giving auditors a deeper view of the business’s situation. Accordingly, auditors will identify potential risks more accurately and provide more appropriate material. As a result, AI also can assist auditors to predict the potential financial situation and determine the reasonability of financial forecasts as well as potential future risks of the business.
Substantive Procedures
At this stage, auditors must perform many repetitive tasks, such as checking details of documents (e.g., invoices and contracts), matching data among documents, and verifying the accuracy of the financial statements. AI can perform these tasks automatically through programmed algorithms, allowing auditors to review more data promptly with a higher level of accuracy in less time than a traditional audit.
By using AI to analyze and process the large volume of transactions, auditors can easily detect anomalies, errors, and risks in financial data. It allows auditors to focus more on high-risk areas that are prioritized, thereby improving the audit quality. Additionally, AI has a function known as machine learning which allows the system to learn from past data and improve its performance, thus enhancing accuracy and effectiveness.
It can be seen that applying AI in auditing brings many benefits. On the one hand, labor savings and productivity increases are the prominent characteristics of AI system. And with the ability to review and analyze information on a wide scale, AI can help identify fraud or potential risks that may be overlooked, thereby improving risk assessment and strengthening audit quality.
“With the ability to review and analyze information on a wide scale, AI can help identify fraud or potential risks that may be overlooked, thereby improving risk assessment and strengthening audit quality.”
However, using AI still has certain limitations:
AI works based on the provided data, so ensuring that the data is accurate, complete, and taken from reliable sources is crucial. Additionally, with a colossal volume of data, errors are likely to occur during analysis and processing. This can result in inaccurate conclusions and affect audit results.
Another limitation relates to cybersecurity, as using AI requires an internet connection. Therefore, if the internet system is compromised, AI algorithms could be altered and routed to discrepancies in AI operations.
AI is, after all, a machine set up by humans which performs tasks based on pre-established patterns. Hence, AI cannot respond to or handle unforeseen situations. Moreover, maintaining an attitude of professional skepticism is extremely important during the audit process to minimize potential risks. Nevertheless, the nature of AI is mechanical, so it is impossible to possess this skepticism when analyzing information and handling situations as auditors do.
Another limitation is that AI might not be able to satisfyingly detect fraud or window dressing in accounting that may occur in a business, because AI lacks the ability to think and evaluate like humans. Fraud detection requires auditors to possess a professional skepticism to assess the evidence collected during the audit and to evaluate it based on the business operations and internal control activities.
With workloads increasing, the benefits that AI provides are indispensable and will have a positive impact on the audit process. However, auditors should use AI appropriately and not abuse it or rely entirely on it, because AI is precisely a tool and cannot solve complex issues that require human decision-making on a system basis. Consequently, the balance between using AI and manual work in auditing is prerequisite. Furthermore, auditors should be equipped with the necessary knowledge and skills to fully understand AI’s operations, employ its produced results, and avoid cybersecurity attacks.
For more information, please contact Grant Thornton Japan at info@jp.gt.com or visit www.grantthornton.jp/en
Disclaimer: Opinions or advice expressed in the The ACCJ Journal are not necessarily those of the ACCJ.
Synthetic Savants
Since the introduction of consumer-facing artificial intelligence applications such as ChatGPT and Google’s Bard, generative AI has transformed how people work around the world. How might it impact specific industries in the years to come?
As generative AI sweeps the world, how will it transform the way we work and innovate?
We live in an age of intelligent machines. Since the introduction of consumer-facing artificial intelligence (AI) applications such as OpenAI’s ChatGPT and Google’s Bard over the past year, generative AI has transformed how people work around the world.
From $40 billion in 2022, the market size for generative AI will balloon to $1.3 trillion over the next 10 years, according to Bloomberg Intelligence. First popularized through image generators, the technology has been applied in fields ranging from neuroscience to advertising, sometimes in surprising ways.
Generative AI programs like the large language models powering ChatGPT are trained on enormous volumes of data to sense patterns and predict how they will play out in a piece of content. These models can be trained on linguistic, financial, scientific, sensor, or other data—especially data that is uniform and structured—and can then create new content in response to user input. They have had remarkable success, particularly in image and text generation, and have seen rapid uptake in sectors ranging from education to computer programming. “This technology is set to fundamentally transform everything from science, to business, to healthcare … to society itself,” Accenture analysts enthused in a report. “The positive impact on human creativity and productivity will be massive.”
Powerful New Assistants
Generative AI first gained public attention thanks to its ability to change how we communicate through words, images, and video. It’s no wonder, then, that the world’s largest public relations company has embraced it. Edelman worked with OpenAI to launch the original ChatGPT-2 and delivered the first application in an ad campaign. In the spots for Hellmann’s Mayonnaise, the tool is tasked with finding new ways to use leftovers.
Edelman believes the technology will reconfigure the communications industry, but it won’t replace human ingenuity, strategic advice, and ethical decision-making that builds trust, said Meghan Barstow, president and representative director of Edelman Japan.
“We predict that AI will become an essential assistant in our work, helping to brainstorm, research, summarize, trend spot, monitor media, and generate content, among other tasks,” explained the ACCJ governor and chair of the chamber’s Communications Advisory Council. “The emphasis here is on ‘assistant,’ as we believe there will always be a human in the loop, that AI and people working together will provide the most effective and valuable work output.
“As with any technology, there are risks that require appropriate caution, education, processes, and policies to ensure the safe and trustworthy use of generative AI to protect our work, our clients, and end users from issues related to disinformation, bias, copyright infringement, and privacy.”
Similarly, lawyers such as Catherine O’Connell are also using generative AI as smart assistants. O’Connell is principal and founder of Catherine O’Connell Law and co-chair of the American Chamber of Commerce in Japan (ACCJ) Legal Services and IP Committee.
After taking a course on how to get the most out of ChatGPT, she has been using it for writing keynote speeches, article outlines, posts on social media, and skeletons of presentations. She compares the tool to a human intern, and praises its time-saving efficiencies, but warns that it should not be used for legal work, such as contracts or legal advice. Attorneys in the United States, she noted, have found themselves in trouble after producing legal filings referencing non-existent cases that generative AI simply made up.
“Generative AI is like a teenager that has a lot of promise but has not learned how to be a whole professional yet; it needs guidance,” said O’Connell. “However, in terms of an idea generator or idea expander, a time-saving device, and an assistive tool, generative AI is an asset. The rest falls to me to add my human touch to check and verify, to add my own personality and insights only I have, and to make the output my very own. I think generative AI is so good that its cousin, Google search, may be out of a job sometime soon.”
Smart Tools for Talent
Recruiting is another industry in which workers deal with mountains of structured data, in the form of resumes and online posts, that can be utilized by AI. Robert Half Japan, an ACCJ Corporate Sustaining Member company, uses a system called AI Recommended Talent (ART) to match resumes to client needs. The system speeds up matching for job hunters and employers, allowing staff to spend more time with clients.
“The real power of generative AI is how much it can integrate with our existing systems,” explained Steven Li, senior division director for cybersecurity. “We are piloting ChatGPT-4 integration in our Salesforce CRM. Studies have shown benefits from integrating generative AI into workflows. Other industry examples that highlight the benefit of integration include the GitHub CoPilot generative AI feature.”
The effectiveness of AI in recruiting has led some people to speculate that it could render many human recruiters obsolete. Deep learning algorithms are figuring out what a good resume looks like, and generative AI can craft approach messages and InMails, a form of direct message on the popular LinkedIn platform, noted Daniel Bamford, Robert Half’s associate director for technology.
“However, the real value of agency recruitment is not, and never was, a simple job-description-to-resume matching service,” added Bamford. “Agency recruitment done well is a wonderful journey of problem-solving, involving the goals of organizations and teams and the values and desires of individuals. Excellent recruiters will thrive. They will use AI’s capacity to handle simple tasks like scheduling and shortlisting. This will free up time for high-value interactions, delivering even greater value for their partners and industries through the human touch. The future of excellent recruiters will be brighter with AI’s support.”
Tracking Ships and Patients
Even a traditionally hardware-oriented industry like logistics is being transformed by generative AI. Shipping giant Maersk is using a predictive cargo arrival model to help customers reduce costs with more reliable supply chains. It also wants to harness the power of AI to recommend solutions when shipping routes are congested, advising on whether goods should be flown or stored, and better understand the sales process, Navneet Kapoor, Maersk’s chief technology and information officer, told CNBC.
Maurice Lyn, head of Managed by Maersk for Northeast Asia, also sees great potential in the technology. “The biggest changes that I foresee will be related to the enhanced visibility into, and agility of the management of, the global supply chains of our clients on an execution level,” he told The ACCJ Journal. “The data aggregated will allow logistics service providers [LSPs] to deliver predictive and proactive solutions to our clients. If clearly interpreted by the LSPs, stability and uniformity of costs and deliverables will be provided globally and locally to our clients.”
Generative AI may even help us live longer, healthier lives via long-term patient monitoring. Sydney-based medical AI startup Prospection recently launched its first generative-AI model in Japan to analyze anonymized patient data for pharmaceutical companies so they can better understand patient needs. A Japanese drug company, for instance, could look at cancer patient outcomes across the country and find that they are slightly worse in a particular region, possibly because less-effective drugs are prescribed there.
Founded in 2012 and operating in Australia, Japan, and the United States, Prospection now has data on half a billion patients. For the first 10 years, it was using traditional AI methods, but generative AI has opened new services for the company. Users can query Prospection’s AI services about typical pathways for patients who took a certain drug, or what therapy they underwent after quitting the medication. A Prospection model can predict whether a patient will experience a certain event, such as needing to be hospitalized, over the next year.
“The ChatGPT transformer model is trained on billions of sentences consisting of words. We see each patient’s journey as the sentence and events in the journey as the words. That’s the vocabulary,” said Eric Chung, co-founder and co-CEO of Prospection. “The data is very powerful. There are lots of insights to be gained from data on 500 million patients. It’s beyond the power of humans to analyze, but AI can do it.”
Do Androids Dream of Electric Sheep?
AI is beginning to create content that is sparking questions about ownership. For some time, companies have been using AI-powered tools to give computers the task of writing articles, social media posts, and web copy. Now, AI-powered image-generation engines, such as Stable Diffusion, Midjourney, and the Deep Dream Generator, have hit the mainstream. One day, might DEI be extended to machines?
Rethinking DEI in an age of rapidly expanding artificial intelligence
I have always been fascinated by the idea of artificial intelligence (AI). I remember chatting back in the 1980s with a version of Eliza for Commodore 64. Eliza is a program created in 1964 by German American computer scientist Joseph Weizenbaum at the MIT Artificial Intelligence Laboratory. Rudimentary by today’s AI standards, Eliza is a natural language processor that converses with the user based on their input. It tries to mimic a real person and was one of the earliest applications to attempt what has come to be called the Turing test, a way of gauging a machine’s ability to exhibit intelligence. Passing this test, developed by English scientist Alan Turing, means a machine can conceal its identity, making a human believe it is another human.
I’ve been thinking back to that experience because we are now at a point where we must start considering how we will coexist with and treat truly intelligent machines. We’re not quite there yet, but the rapid advance of AI, and its integration into so many aspects of life, means this is a question that is no longer the providence of science fiction. It will be a real part of our future. Machine identity and rights will one day be an extension of the diversity, equity, and inclusion (DEI) that we talk about in this issue of The ACCJ Journal.
AI is beginning to create content that is sparking questions about ownership. For some time, companies have been using AI-powered tools to give computers the task of writing articles, social media posts, and web copy. Now, AI-powered image-generation engines, such as Stable Diffusion, Midjourney, and the Deep Dream Generator, have hit the mainstream. You may have seen some of their creations in the news. As these engines are trained on existing art, often scraped from the internet, there are questions about copyright and plagiarism. Stock media giant Getty Images announced on September 21 that it is banning AI-created art over these concerns.
Eventually, I believe, the visuals that machines create will become less obviously imitative and will express a view of the world unique to the creator, in the same way that the work of a human artist is an expression of the inner working of their mind. And when that happens, we really will have to ask ourselves what distinguishes us from machines.
Back to the Present
We still have some time before that question must be answered. For now, our focus can remain on the people who make our companies successful and our societies prosperous.
We explore DEI initiatives in this issue, along with sustainability efforts that can help ensure that our world has a healthy future.
I take to the road and the air on page 26 to explore the future of transportation and sustainability initiatives by member companies. I also talk to Bank of America’s Japan country executive and president of BofA Securities Japan, Tamao Sasada, on page 18 about the importance of diversity and the company’s efforts in the areas of DEI; environmental, social, and corporate governance; and sustainable finance.
I hope you enjoy this special issue and find useful ideas to help you achieve your own DEI and sustainability goals.
Sincerely yours, Eliza.
State of Mind
For millions of people around the world who were already struggling with mental health issues, the past two-and-a-half years of the coronavirus pandemic have been a further trial. Isolation, a sudden shortage of opportunities to interact with friends or family in person, additional stresses in the workplace or the home, new financial worries, and difficulty in accessing appropriate mental healthcare have taken their toll, experts in the field told The ACCJ Journal.
How artificial intelligence is helping identify mental health concerns for better treatment
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For millions of people around the world who were already struggling with mental health issues, the past two-and-a-half years of the coronavirus pandemic have been a further trial. Isolation, a sudden shortage of opportunities to interact with friends or family in person, additional stresses in the workplace or the home, new financial worries, and difficulty in accessing appropriate mental healthcare have taken their toll, experts in the field told The ACCJ Journal.
However, in the battle against mental health complaints, this time of adversity has also served to fast-track development and adoption of a new tool: artificial intelligence (AI). While the technology may be relatively new to the sector, the potential is huge, according to companies that are applying it to assist physicians with diagnosis and treatment.
A Tool for Our Time
AI has come a very long way since the first chatbots appeared back in the 1990s, and early mental health monitoring apps became available, explained Vickie Skorji, Lifeline services director at the Tokyo-based TELL Lifeline and counseling service. And it is urgently needed, she added.
“When we have something such as Covid-19 come along on a global scale, there is inevitably a sharp increase in anxiety, stress, and depression. The mental healthcare systems that were in place were simply flooded,” she said.
“A lot of companies were already playing around in the area of AI and mental healthcare, but the pandemic has really pushed these opportunities to the forefront,” she explained. “If, for example, a physician is not able to meet a client in person, there are now ways to get around that, and there has been an explosion in those options.”
Not every purported tool is effective, she cautions, and there are going to be questions around client confidentiality and keeping data current. The clinician must also become sufficiently adept at interpreting a client’s genuine state of mind, which might be different from the feelings that are communicated through the technology. On the whole, however, Skorji sees AI as an extremely useful weapon in the clinician’s armory.
Voice Matters
One of the most innovative solutions has recently been launched by Kintsugi, a collaboration between Grace Chang and Rima Seiilova-Olson, engineers who met at the 2019 OpenAI Hackathon in San Francisco. In just a couple of years, the company has gone from a startup to being named in the Forbes list of North America’s top 50 AI companies.
Kintsugi has developed an application programming interface called Kintsugi Voice which can be integrated into clinical call centers, telehealth platforms, and remote patient monitoring applications. It enables a provider who is not a mental health expert to support someone whose speech indicates they may require assistance.
Instead of using natural language processing (NLP), Kintsugi’s unique machine learning models focus on signals from voice biomarkers that are indicative of symptoms of clinical depression and anxiety. Producing speech involves the coordination of various cognitive and motor processes, which can be used to provide insight into the state of a person’s physical and mental health.
In the view of Prentice Tom, chief medical officer of the Berkeley, California-based company, passive signals derived from voice biomarkers in clinical calls can greatly improve speed to triage, enhance behavioral health metadata capture, and benefit the patient.
“Real-time data that augments the clinician’s ability to improve care—and that can be easily embedded in current clinical workflows, such as Kintsugi’s voice biomarker tool—is a critical component necessary for us to move to a more efficient, quality-driven, value-based healthcare system,” he explained. The technology is already in use in the United States, and Japan is on the waiting list for expansion in the near future.
Chang, the company’s chief executive officer, is confident that they are just scratching the surface of what is possible with AI, with one estimate suggesting that AI could help reduce the time between the appearance of initial symptoms and intervention by as much as 10 years.
“Our work in voice biomarkers to detect signs of clinical depression and anxiety from short clips of speech is just the beginning,” she said. “Our team is looking forward to a future where we can look back and say, ‘Wow, I can’t believe there was a time when we couldn’t get people access to mental healthcare and deliver help to people at their time of need.’
“My dream and goal as the CEO of Kintsugi is that we can create opportunities for everyone to access mental health in an equitable way that is both timely and transformational,” she added.
The Power of Data
Maria Liakata, a professor of NLP at Queen Mary University of London, is also the joint lead on NLP and data science for mental health groups at the UK’s Alan Turing Institute. She has studied the use and effectiveness of AI in communicating with the public during a pandemic.
Liakata’s own work has focused on developing NLP methods to automatically capture changes in individuals’ mood and cognition over time, as manifested through their language and other digital content. This information can be used to construct new monitoring tools for clinicians and individuals.
But, she said, a couple of other projects have caught her eye.
One is Ieso Digital Health, a UK-based company that offers online cognitive behavioral therapy for the National Health Service, utilizing NLP technology to analyze sessions and provide data to physicians. And last October, US-based mental and behavioral health company SonderMind Inc. acquired Qntfy, which builds tools powered by AI and machine learning that analyze online behavioral data to help people find the most appropriate mental health treatment.
“There has definitely been a boom over the past few years in terms of the development of AI solutions for mental health,” Liakata said. “The availability of large fora in the past 10 years where individuals share experiences about mental health-related issues has certainly helped in this respect. The first work that came to my attention and sparked my interest in this domain was a paper in 2011 by the Cincinnati Children’s Hospital. It was about constructing a corpus of suicide notes for use in training machine learning models.”
Yet, as is the case during the early stages of any technology being implemented, there are issues that need to be ironed out.
“One big hurdle is the availability of good quality data, especially data over time,” she continued. “Such datasets are hard to collect and annotate. Another hurdle is the personalization of AI models and transferring across domains. What works well, let’s say, for identifying a low mood for one person may not work as well for other people. And there is also the challenge of moving across different domains and platforms, such as Reddit versus Twitter.
“I think there is also some reluctance on the part of clinicians to adopt solutions, and this is why it is very important that AI solutions are created in consultation with clinical experts.”
Over the longer term, however, the outlook is positive, and Liakata anticipates the deployment of AI-based tools to help with the early diagnosis of a range of mental health and neurological conditions, including depression, schizophrenia, and dementia. These tools would also be able to justify and provide evidence for their diagnosis, she suggested.
To Assist, Not Replace
Elsewhere, AI tools will be deployed to monitor the progression of mental health conditions, summarize these with appropriate evidence, and suggest interventions likely to be of benefit. These would be used by both individuals, to self-manage their conditions, and clinicians.
Despite all the potential positives, Skorji emphasizes that AI needs to be applied in conjunction with in-person treatment for mental health complaints, rather than as a replacement.
“The biggest problem we are seeing around the world at the moment is loneliness,” she said. “Technology is useful, but it does not give people access to people. How we deal with problems, what the causes of our stress are, how can we have healthy relationships with other people—we are not going to get that from AI. We need to be there as well.”