Monthly Archives: April 2019
Lessons learned turning machine learning models into real products and services
Artificial intelligence is still in its infancy. Today, just 15% of enterprisesare using machine learning, but double that number already have it on their roadmaps for the upcoming year. With public figures like Intel’s CEO stating that every company needs a machine learning strategy or risks being left behind, it’s just a matter of time before machine learning enters your organization, too—if it hasn’t already.
However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production. In other words, “The gap between ambition and execution is large at most companies,” as put by the authors of an MIT Sloan Management Review article. Ultimately, there’s a major difference between building a model, and actually getting it ready for people to use in their products and services. Read More
AI agent offers rationales using everyday language to explain its actions
Georgia Institute of Technology researchers, in collaboration with Cornell University and University of Kentucky, have developed an artificially intelligent (AI) agent that can automatically generate natural language explanations in real-time to convey the motivations behind its actions.
The work is designed to give humans engaging with AI agents or robots confidence that the agent is performing the task correctly and can explain a mistake or errant behavior.The agent also uses everyday language that non-experts can understand. The explanations, or “rationales” as the researchers call them, are designed to be relatable and inspire trust in those who might be in the workplace with AI machines or interact with them in social situations. Read More
AI/ML Lessons for Creating a Platform Strategy – Part 1
As data scientists increasingly become critical resources in enabling companies’ exploration and exploitation of their digital resources, it’s also increasingly important that data scientists can provide accurate and focused business guidance. If that seems like a mouthful, try these two scenarios.
Scenario 1: Joe is asked to recommend a portfolio of AI/ML projects that will improve performance, provide measurable ROI, and have relatively low risk of failure.
Scenario 2: Joan is asked to plan AI/ML projects that will maximize the value of the company, protect against competitors, and create the fastest possible market, revenue, and margin growth.
Pretty much any of us could do a competent job with the first scenario. We’d look at the current business and try to find opportunities where traditional ML could be used like scoring, forecasting, or optimization. If we’re sufficiently advanced we’d also look for AI opportunities like the application of NLP or image processing. Since we weren’t asked to challenge the fundamental business model, we just looked to places where we could paste on AI/ML. Read More
Hype And Reality In Chinese Artificial Intelligence
In MIT Technology Review, Jeff Ding shares five takeaways from his experience writing about and translating Chinese-language writing about artificial intelligence (AI) research in China. Ding is a researcher at the University of Oxford who has now published 48 issues of his insightful ChinAI newsletter. From the MIT Tech Review article:
“The Chinese- and English-speaking AI communities have an asymmetrical understanding of each other. Most Chinese researchers can read English, and nearly all major research developments in the Western world are immediately translated into Chinese, but the reverse is not true…
Westerners have a hyped-up view of China’s AI capabilities… A few deep dives from Chinese writers have reported that most of China’s AI giants are much less impressive than they seem, with less-sophisticated algorithms and smaller research teams than commonly believed
.The Chinese government sees AI as a tool for social governance. Security companies account for the highest share of the top 100 AI companies… Some of these firms have been directly involved in the government’s mass surveillance of Xinjiang, the autonomous region where members of China’s ethnic minority group, the Uighurs, are concentrated. Other companies are fueling China’s export of surveillance tech to Central Asia and beyond
.AI research has benefited greatly from China-US collaboration. Historically, talent and ideas have flown freely between American and Chinese companies, challenging notions of what it means to be an American or Chinese. Take, for example, Microsoft Research Asia (MSRA), Microsoft’s hub in Beijing and the largest center outside its headquarters. In its 20-year history, MSRA has played as essential a role in pushing the boundaries of Microsoft’s research efforts as it has in fostering China’s AI ecosystem…
Chinese people care about AI ethics. While it may be true that Chinese and American citizens have differing views on privacy, it’s false to say that the former don’t care about it at all.” Read More
Moonshot thinking for C-level executives in the age of Artificial Intelligence
In the last few years, many companies have embraced innovation methodologies, from design thinking to agile organizations. Most of these are outstanding contributions to the innovation space. However, it is also true that many organizations fail at putting these methodologies into practice.
Contrary to popular belief, sometimes the largest barrier is not technological or operational, but a question of having the wrong mindset: when an organization is relatively successful, the right thing to do is to keep improving what worked in the past, thinking incrementally as a general rule. In contrast, C-level executives should “upgrade” their operating system and leave room for “10 × thinking”, a mental model based on improving a relevant problem by a factor of 10, rather than by 10%. Read More
AI 100: The Artificial Intelligence Startups Redefining Industries
The most promising 100 AI startups working across the artificial intelligence value chain, from hardware and data infrastructure to industrial applications.
CB Insights’ third annual cohort of AI 100 startups is a list of 100 of the most promising private companies providing hardware and data infrastructure for AI applications, optimizing machine learning workflows, and applying AI across a variety of major industries. Read More
Andrew Ng’s AI Transformation Playbook shares key lessons
Co-founder of Google Brain and former Chief Scientist at Baidu, Andrew Ng, has unveiled an AI Transformation Playbook. The guide to successfully adopting AI in enterprise draws on insights gained from leading AI teams at Google and Baidu.
The AI Transformation Playbook is distributed freely on the Landing AI website. Under Ng’s leadership as Chairman and CEO, Landing AI helps enterprises develop and execute cohesive AI strategies.Five key steps form the backbone of the guide:
Execute pilot projects to gain momentum
Build an in-house AI team
Provide broad AI training
Develop an AI strategy
Develop internal and external communications
Read More
Mastering the game of Go without human knowledge
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo. Read More
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