AI Wars: Will China Defeat The US?

This week’s plunge in Apple’s shares was another sign of the impact of the relations between the US and China. It does look like President Trump’s tariffs are taking a toll and that the tensions maybe lasting.

So this why Kai-Fu Lee’s book, AI Superpowers: China, Silicon Valley, and the New World Order, is so timely. He provides a detailed look at how China is poised to win one of the most important markets. Keep in mind that – according to a research report by PWC – AI is forecasted to add $15.7 trillion to global GDP by 2030. The main reason is that this technology is general purpose, having applications that span industries like healthcare, transportation, financial services, energy and so on. Some consider AI to be on par with what we saw with the revolution of electricity during the 20th century.

Lee certainly has the credentials to make convincing arguments. He got his Ph.D. in computer science from Carnegie Mellon University in 1988, where he focused on AI. Lee worked on leveraging concepts like Bayesian networks for games and voice recognition.

He would then move on to the corporate world. Some of the companies he worked for included Apple, Silicon Graphics, Microsoft and Google. As of now, he’s a venture capitalist with Sinovation Ventures, which is focused on AI opportunities in China.

The Book

Lee notes that China has AI fever, galvanized by heavy investments from the government and VCs.  Yet success is more than just about money.  Lee points out that there are four key factors for AI:  “abundant data, hungry entrepreneurs, AI scientists and an AI-friendly policy environment.”

In light of this, it’s actually easy to see why China is in a strong position to benefit.  After all, the ubiquity of smartphones has meant the accumulation of gigantic amounts of data. But China has also seen more usage of real-world applications, such as with bike-sharing, mobile payments and ride-hailing. Part of this has been due to massive private/public investments. But China’s willingness to be permissive with issues of privacy has been another big factor.
Data is essential for AI because it is required for developing sophisticated neural networks. This makes it possible to better understand language, recognize objects or come up with useful insights.

Once the data threshold is met, which seems to be the case with China (it is the largest producer in the world and the country has more than 700 million internet users), then there is less of a need for top-notch engineers. Lee writes: “Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.”

What Now?

Now the US is not doomed to failure.  We still have many advantages. The university system is standout and there are mega companies that are pushing innovation (Google, Microsoft, Facebook, Amazon.com and Apple).

But much still needs to be done. According to Jason Tan, who is the co-founder and CEO of Sift Science (a company that uses AI to help online businesses prevent fraud and abuse in real time), he believes there needs to be the following:

  • Make much more investments in homegrown STEM talent.
  • Make it attractive for overseas STEM talent to remain in the US
  • Get more government support for entrepreneurship — creating workplace opportunities for that talent to be applied
  • Get more government support for AI research — so that we continue to push the frontier

No doubt, all these would make a difference.  But unfortunately, there has been little progress on these fronts.  So unless there is major change – and soon – Silicon Valley may no longer be the center of gravity in the next 10 years.

2018’s Biggest Moments In AI (Artificial Intelligence)

When it comes to Artificial Intelligence (AI), there is no shortage of hot air and hype. Note that many companies are trying to get a piece of the digital gold rush (similar to what we witnessed during the dot-com boom).

Yet despite all this, there is true innovation occurring in AI.  In fact, some of the new technologies are draw-dropping.  The fact is that AI is experiencing an acceleration of progress.

So what are this year’s most important developments?  Well, let’s take a look:

AI-Driven Medical Diagnosis: “In 2018, we saw artificial intelligence leveraged extensively in healthcare to help medical professionals accelerate and improve diagnoses by combing through mountains of data within 3D and 2D medical imagery,” said Sujai Hajela, who is the president, cofounder and CEO of Mist.

He points out some examples:

  • The improvement of cancer detection rates in tissue by eight percent with iCAD’s ProFound AI for Tomosynthesis
  • The improvement of “diagnostic accuracy” in imaging devices with Samsung Electronic’s “Ultrasound, Digital Radiology, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) concept and [their] software innovations.”

Sheldon Fernandez, who is the CEO of DarwinAI, also agrees that some of this year’s greatest breakthroughs have come in the medical field. Here are some of the developments he considers important:

  • Google DeepMind used AI to predict protein folding–the physical process by which a protein chain acquires its 3-dimensional structure — and won the overall protein-folding competition.
  • AlphaFold shows the crossover potential of AI and deep learning to benefit humankind. This breakthrough regarding the configuration of proteins will help those in the health sciences field better understand what causes specific diseases and develop drugs to treat these illnesses.

Autonomous: The City of Las Vegas Innovation District launched the first completely autonomous electric shuttle on a public roadway in the US, which has carried more than 32,000 passengers. And yes, Google’s Waymo announced the commercial deployment of its self-driving rides, surpassing 10 million miles in 2018.

Talking: “At Google I/O, Sundar Pichai demonstrated Google Duplex, an AI based system that could call a local business and schedule an appointment over the phone using a natural conversation,” said Roy Raanani, who is the CEO and founder of Chorus. “In most cases, the humans did not even realize they were speaking to a computer.”

Translation: Microsoft leveraged AI to translate sentences of news articles from Chinese to English with the “same quality and accuracy as a person.” According Xuedong Huang, who is one of the company’s technical fellows: “Hitting human parity in a machine translation task is a dream that all of us have had. We just didn’t realize we’d be able to hit it so soon.”

Virtual Assistants: Of course, this technology has been around for a while. But for this year, virtual assistants have hit critical mass.

“For the first time, conversational search — the process of interacting with a technology and getting responses in the form of a natural conversation — serves as a conduit for contextually relevant and personalized interactions,” said Nicolas Dessaigne, who is the co-founder and CEO of Algolia.

Based on research from the “Voice Assistant Consumer Adoption Report 2018,” the number of voice-enabled digital assistant devices has grown to over 1 billion worldwide over the past two-and-a-half years.

Connected Supply Chain: In January, Amazon.com launched its wristbands that can track warehouse employees, so as to streamline factory order fulfillment.

“Amazon said that the wristbands can help employees improve time-consuming tasks such as inventory management,” Phil Friedman, who is the CEO of CGS. “Agility and automation in smart factories can allow the factory to adapt to changes with minimal intervention. Now with workers and technology working hand-in-hand, manufacturers can monitor and adjust in real-time.”

Recognition: This year Pindrop was granted the first ever patent on utilizing an end-to-end Deep Neural network for speaker recognition. The technology, which the company calls Deep Voice, can passively identify legitimate and fraudulent callers — solely by voice.

According to Dan Capozzi, who is the EVP and President of Credit Operations and Decision Management at Discover Financial Services:  “Discover constantly seeks new ways to protect customers to help them continue to feel secure using their accounts freely and easily.  Pindrop’s AI technology brings additional layers of convenience and security to our customer experience, while allowing us to provide excellent service at the speed of natural conversation.”

What To Expect For AI (Artificial Intelligence) In 2019

AI (Artificial Intelligence) continues to be red hot. Then again, every top tech company is investing heavily in the technology, such as Amazon.com, Facebook, Microsoft and Google.

But AI is more than just about big companies.  Keep in mind that the technology is getting much easier and affordable to use.

“We are seeing the democratization of AI through open source algorithms, affordable computing power and AI specialized hardware,” said Roy Raanani, who is the CEO and founder of Chorus.ai. “Google TensorFlow released open source software to allow anyone to build on Google’s own machine learning algorithms. Also the introduction of AI specialized hardware by Apple, Google, Tesla and NVIDIA is increasing AI performance by tens to hundreds, and enabling that performance in smaller form factors.”

Then what may we see next year? What are some of the emerging trends?

Here’s a look:

Video and AI will Make Voice the Last Frontier in Business Communications

Santi Subotovsky, General Partner, Emergence:

“We’ve already seen a huge rise in revenue generating applications that combine voice and AI to improve human interactions, sales, and customer service. In 2019, we’ll see new applications that, among other capabilities, will allow enterprise users to employ voice, AI and video to capture and analyze content, interpret non-verbal cues, and quickly respond to queries for data needed in discussions. The increased productivity, efficiency and insights provided by these applications will shift the center of business communications from text to face-to-face meetings, bringing voice and video full circle from the first to the last frontier.”

AI Smart Features Will Improve Meetings

Oded Gal, Head of Products, Zoom Video Communications:

“We believe that in 2019, video meetings will surpass other means of business communications to become the de facto standard. Why? Because video communication has more AI-driven ‘smart’ features than ever and those technologies can dramatically improve meeting productivity and the user’s experience. For example, AI-based features such as voice-to-text transcription can take meeting notes, and soon, virtual personal assistants will record tasks and help set up meetings, and voice recognition will identify meeting participants and provide relevant details on their background. Together, we believe that these features will make many video meetings superior to in-person meetings.
“Additionally, we predict that AI-driven facial recognition will be used in video conference rooms for a variety of purposes. For example, insights into who has used the conference room, when, and for what purpose will also help IT and Facilities staff better plan space allocation and usage.”

Chief Analytics Officer (CAO) or Chief Data Officer (CDO) Roles Will Become More Prevalent

Candace Worley, Chief Technical Strategist, McAfee:

“There are myriad decisions that must be made when a company extends their use of AI. Implications exist for privacy regulation but there are also legal, ethical, and cultural implications that warrant the creation of a specialized role in 2019 with executive oversight of AI usage.
“In some cases, AI has demonstrated unfavorable behavior such as racial profiling, unfairly denying individuals loans, and incorrectly identifying basic information about users. CAOs and CDOs will need to supervise AI training to ensure AI decisions avoid harm. Further, AI must be trained to deal with real human dilemmas and prioritize justice, accountability, responsibility, transparency and well-being while also detecting hacking, exploitation and misuse of data.”

Trust AI the Same Way You Trust Your Doctor in 2019

Nick Caldwell, Chief Product Officer, Looker:

“In 2019, interpretability (the ability to understand how an AI system works) will become a nice-to-have. Think about it: when you visit the doctor’s office to get a diagnosis, you never once ask them to provide all their reference materials, case studies, comparative patient records, etc. to prove their point. At some level you accept that the doctor is an expert and you trust them. If that is okay, why do we hold AI to a higher standard of interpretability than we hold other humans? The reality is that over the past few years AI has begun to exceed human capabilities and 2019 is the year we will begin to accept it. As humans we do not need to fully understand why AI’s make decisions and maybe the systems can become better and faster when we decide to get out of the way.”

How To Create An AI (Artificial Intelligence) Startup

AI (Artificial Intelligence) is definitely the “in” thing right now in the tech world. It seems like there is a new startup spinning up every day.  And yes, many existing companies are re-branding themselves as AI operators.

The irony is that this technology has been around for decades — but it is only recently that it has gotten traction.  Then again, there has been a convergence of various technologies that has made AI a reality.

“There has been more progress in speech recognition technology in the last 30 months than in the first 30 years,” said Jamie Sutherland, who is the CEO and co-founder of Sonix (which is an AI service for transcription). “It’s not just the massive amounts of data that are being collected, it’s the fact that this data can be mined at amazing speeds. Computing power is increasing at an exponential rate. This opens up a whole new world of opportunity for novel applications to be developed that wouldn’t have been possible only a few years ago.”

Yet the AI market is getting increasingly crowded, with the noise level hitting fever pitch. So then, how can you set yourself apart? What are the strategies to consider?

Well, to get some answers, I reached out to various CEOs and founders of AI companies:

Dan O’Connell, chief strategy officer and GM of VoiceAI at Dialpad (formerly the CEO at TalkIQ):

Founders often forget about three important elements of building an AI company, which we learned first-hand while building TalkIQ and are now applying as we continue our journey at Dialpad (which acquired TalkIQ in May). The first is hiring a team with a diverse combination of academic experience and product development expertise – this mix is important in building models, designing features, and bringing them to market. The second is setting realistic expectations on which problems your product can and can’t solve — otherwise users are highly likely to experience disappointment because AI as a whole is still in the first innings. Finally, it’s crucial for your engineers to interact directly with customers and the features they’re building. If you’re not excited about what you’re building and it doesn’t work in the desired way when you test it, you can’t expect someone else to pay for it.

Roy Raanani, the CEO and founder of Chorus:

Plan a product roadmap that gets a minimum viable product into customer’s hands as quickly as possible, even with no AI implemented. This will dial you in to your customers, how your solution fits into their existing days and workflow, and allows you to start collecting real-world data you can evaluate AI algorithms on.

Peter Wang, co-founder and CTO of Anaconda:

AI startups face both strategic and tactical challenges. Tactically speaking, getting and maintaining access to high-quality data is typically the lynchpin of any prediction system. Oftentimes, the corporate customers most likely to understand the value of a sophisticated AI product will also probably have massive amounts of internal, messy proprietary data to integrate with before they will purchase. On the flip side, potential buyers with lesser data integration challenges may also have less understanding of the unique value of AI over traditional business analytics and statistics.

Related to this, AI startups must make a fairly strategic decision early on in their lifecycle about whether they primarily want to be a “prediction-as-a-service”, API-style offering, or if they want to build a full-featured polished app that faces the business end user. The former lets them focus on core differentiators, at the risk of commoditization by other larger platform vendors. The latter lets them own the user experience, but at a much larger up-front cost and a risk of unnecessarily boxing their technology into a niche.

Pini Yakuel, the CEO of Optimove:

The ability to perform better math is no longer enough to build a successful AI-driven startup because in the rush to be an AI-focused company, startups have stopped taking into account the business problem they are solving (and not every business problem is best solved by AI). What can increase chances of success, is knowing that a certain industry challenge has a strong likelihood to be solved ‘well enough’ with an AI approach. For that, you need to excel at defining and framing the problem you are out to solve. Your creativity and expertise will be your trump card, not a better algorithm.

Dimitri Sirota, the CEO and co-founder of BigID:

One mistake companies in tech sometimes make is getting their message lost in the jargon they use to describe it. AI and ML are a means to an end. Saying you are an AI company alone is not a durable difference when AI becomes commonplace. The essence of a successful start-up is solving a problem that a large enough universe of customers understand themselves to have. Telling that story effectively is how a company wins. AI is can be the adjective but its not the noun.

Mahesh Ram, the CEO of Solvvy:

AI companies should pick a domain where consumers or businesses are currently willing to adopt AI/automation solutions and there’s social and cultural acceptance. This way you are not fighting against the current early on. For example, people don’t want robots as their doctors (yet); however, they are ok with automation when it comes to customer service.

Andrew Filev, the CEO and founder of Wrike:

Don’t include “AI” in the name of the company. Although AI is far less sci-fi and more commonplace now, it can still scare a few folks off and you don’t want to frighten your potential key buyers by making them think you are offering an AI solution that will one day replace them.