Revenue for cognitive/AI systems to top $47 billion by 2020


The use cases that are attracting the most investment in 2016, according to the IDC, are automated customer service agents, quality management investigation and recommendation systems, diagnosis and treatment systems, and fraud analysis and investigation.

The use cases that will experience the fastest revenue growth over the next five years are public safety and emergency response, pharmaceutical research and discovery, diagnosis and treatment systems, supply and logistics, quality management investigation and recommendation systems, and fleet management.

23 August 2016 – The beginning of AI in the enterprise

On Monday Microsoft announced it had purchased Genee, a smart scheduling app startup. Genee’s artificial intelligence technology will be integrated into Microsoft’s Office 365.

Microsoft has been actively acquiring similar businesses this year in order to make sure it is well placed for the digital revolution, which will see AI at its core.

By the “end of 2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products”, said Deloitte.

Digital Assistants teaming up


Microsoft and Amazon partner to integrate Alexa and Cortana digital assistants

This cross-platform integration will also allow Alexa users to access some of the more unique aspects of Cortana. Microsoft has built its digital assistant more directly into its Office products, and now Alexa will get that functionality via Cortana — accessing work calendars and email, for example. While Microsoft is still tempting developers to create their own Cortana skills, existing Cortana users will be able to call up Alexa to get access to the ones that Cortana is missing. This might mean controlling smart home devices, or shopping on Amazon.

While Microsoft and Amazon have formed a closer partnership, Bezos also welcomes Apple and Google to offer similar integration, and says he’d support it. “There are going to be multiple successful intelligent agents, each with access to different sets of data and with different specialized skill areas,” said Bezos in a press statement. “Together, their strengths will complement each other.” Nadella also appears to welcome the idea of collaboration with Apple and Google, saying “Hopefully, they’ll be inspired by it” in an NYT interview.

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The new spring of artificial intelligence: A few early economies


The new spring of artificial intelligence: A few early economies

Jacques Bughin, Eric Hazan 21 August 2017

There are three reasons why AI is experiencing a new spring and will not go way.

  • First, more and more of clever investors, from venture capital and private equity, have tripled their AI investments over the past three years are now investing billions in AI. And even if this is small option bets – about 3% of total venture capital funding today – it is growing very quickly, even faster than biotech.
  • Second, while private equity and venture capital firms can still be wrong, of course, we found that corporate investment in AI is already three times the amount of private equity and venture capital firms. Among the corporations betting on AI, the most bullish are high-tech companies such as Intel and Samsung, along with the digital native players, such as Alphabet, Facebook and Amazon. Automotive companies are active, too — think about GM acquiring Cruise Automation for more than $1 billion last year. For anyone questioning the wisdom of paying so much for relatively new companies, it is worth noting that AI investments are already paying off — remember Kiva, the robotics company Amazon bought for $775 million in 2012? Kiva robotics used for logistics in Amazon reported to generate returns on investment of 50% for its new owner.
  • Third, the set of AI technologies we focus on are actually being deployed (see Figure 1). In our survey of more than 3,000 businesses, we found two-thirds of the companies are AI-aware. They fall into three clusters. About 20% are already serious adopters – mostly deploying machine learning or computer vision technologies, mirroring the investments made by venture capital, private equity, and high-tech firms. About an extra 40% of firms have begun to experiment or are partial adopters. The others are not yet experimenting or implementing, but still this means that a majority is trying. And more: out of the 40% who are not adopting, the main reason isn’t that they don’t believe in AI. Our research shows there is a mix of commercial and technical obstacles; regarding the later, 28% of firms don’t feel they have the technical capabilities to implement.

Full article available here:

A survey of 3,000 executives reveals how businesses succeed with AI



Total investment (internal and external) in AI reached somewhere in the range of $26 billion to $39 billion in 2016, with external investment tripling since 2013. Despite this level of investment, however, AI adoption is in its infancy, with just 20% of our survey respondents using one or more AI technologies at scale or in a core part of their business, and only half of those using three or more. (Our results are weighted to reflect the relative economic importance of firms of different sizes. We include five categories of AI technology systems: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.)

Without support from leadership, your AI transformation might not succeed. Successful AI adopters have strong executive leadership support for the new technology. Survey respondents from firms that have successfully deployed an AI technology at scale tend to rate C-suite support as being nearly twice as high as those companies that have not adopted any AI technology. They add that strong support comes not only from the CEO and IT executives but also from all other C-level officers and the board of directors.

The biggest challenges are people and processes. In many cases, the change-management challenges of incorporating AI into employee processes and decision making far outweigh technical AI implementation challenges. As leaders determine the tasks machines should handle, versus those that humans perform, both new and traditional, it will be critical to implement programs that allow for constant reskilling of the workforce. And as AI continues to converge with advanced visualization, collaboration, and design thinking, businesses will need to shift from a primary focus on process efficiency to a focus on decision management effectiveness, which will further require leaders to create a culture of continuous improvement and learning.

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3 ways AI will change project management for the better


Today: narrow project assistants

Early project management AI will be a project assistant focused on a narrow area of managing a project or team. By focusing on supporting a team in one specific area rather than dealing with all the complexities involved in managing a project, project management AI will be useful to teams sooner rather than later.

stratejos, for example, has started out by focusing on assisting with estimates, budget, and sprint management. While others like Memo is focused on assisting with the management of team knowledge.

Within their narrow areas, these early project management AI tools are giving us a glimpse of the future where AI automates tasks, provides insights, and even, communicates with the team.

However, there are some challenges. These early, narrow project management AI tools rely on people to input data correctly, update tools in a timely manner, and make corrections. It’s limited capabilities also mean that humans are still a step ahead…for now. In order to provide even more value, project management AI needs to evolve.

Second generation: expanding project understanding

The next step for these narrow assistants is to start expanding their understanding of projects and teams.

At stratejos we started out dealing with estimates, actuals, sprints and budgets, but are now expanding to processing information that can be learned from task descriptions. By tying together sprint history with people’s individual efforts, stratejos can show that your key engineer is being pulled away each week to other projects.

As the assistants expand their understanding, new metrics will be revealed that weren’t previously possible, such as quality, performance, learning, change, and effort.

For example, AI will know the changes made to source code and link those changes to people and tasks performed. This will allow AI to link bugs reported to a line of code, the person that wrote it, and the tasks that relate to it. This will allow for real, actionable indicators of team and project performance.

With more data points about projects, predictions will become more reliable, more appropriate, and easier for people to understand. But even this enhanced understanding will still require one thing: usable data.

Third generation: Filling in the data gaps

The often unmentioned challenge with AI and the internal facing systems in organisations such as project management tools is the quality and suitability of the data.
Some teams enter minimal to no data into their project management tools. And even the most disciplined teams have issues with their data being interpreted by machines – maybe they inconsistently name their tasks, or enter minimal information. Whatever the reasons or the maturity of the team, it’s almost a given on that any project management system or toolset, there is missing data or messy, unstructured data.

Data size is certainly a challenge but not an insurmountable one. Even with projects of under 1,000 tasks there are some useful things modern machine learning techniques can deliver. Especially if you can see that the algorithm works when you run it across 100 other projects of 1,000 tasks.

Project management AI can deal with the data challenge by:

  1. Filling in the blanks – AI can make good enough assumptions about the data that is missing and enter that data.
  2. Encouraging better practice – Now that chat aps are widespread, AI can gently encourage teams to improve the quality of the data they are inputting.
  3. Creating new layers of metadata – In order to really understand the state of projects and the performance of teams AI will need to create metadata to represent additional concepts that aren’t currently represented. This meta-data can then feed into machine learning algorithms as features that will enhance the ability of AI to provide meaningful advice.


Full article available here:


PS: Lili is currently working on the third generation mentioned above.

Microsoft aims to make artificial intelligence mainstream


Improving programs with artificial intelligence that could tap into services in the internet “cloud” and even take advantage of computing power in nearby machines, was part of a vision unveiled as the US technology titan’s annual Build Conference opened.

“We are infusing AI into every product and service we offer,” said Microsoft executive vice president of artificial intelligence and research Harry Shum.

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Engineers without borders – 2014 Failure report monitoring, evaluating and adapting to failure


Report about the lessons learned of this ONG. So important that ONG share their lessons learned!!

My life, both personally and professionally, has been shaped by my experiences as a young man in Africa. I’ve told this story many times before, notably in my book, Ripples From the Zambezi, but I tell it here again because I believe it’s important. For seven years, I worked for an Italian NGO in Zambia, Kenya, Algerian, Somalia, and the Ivory Coast. My faith in the change we were driving was gradually eroded as every well-meaning project we embarked upon returned the same result – resounding failure!

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How Machine Learning Advances Will Improve the Fairness of Algorithms

As computers become more “intelligent,” some data scientists have been puzzled as they’ve observed their algorithms being sexist or racist. This shouldn’t be surprising, these algorithms were trained with social data that reflect society’s biases, and algorithms amplify these biases to improve their performance metrics.

The good news is that we have many computer scientists who care deeply about the fairness of ML algorithms, and have developed methods to make them less biased than humans. A few years ago, a group of researchers at Microsoft Research and Boston University uncovered gender discrimination inherent in certain linguistic tools used in many search engines. When used to complete the analogy “man is to computer programmer as woman is to ___,” this tool produced the answer “homemaker.” Our team debiased this tool so that it delivered gender neutral results, making it less biased than humans.

Full article available here: