On June 6, IBM launched Science for Social Good, a new program designed to take on some of the world’s weightiest problems using technology and data. The team of researchers, nonprofits, and postdoctoral fellows will be working on 12 projects for the remainder of 2017 alone, each aligning with at least one of the United Nations’ (UN)Sustainable Development Goals. These goals describe the most significant threats and inequalities that exist in the world today and sets them forth as problems to be solved by 2030.
All the Science for Social Good projects make use of analytics, artificial intelligence (AI), and data science to meet their goals. Specific projects for 2017 include Emergency Food Best Practice, which will improve food distribution practices during times of crisis, and the Overcoming Illiteracy project, which will help illiterate and low-literate adults more easily “decode” our information-rich society using AI. IBM’s Watson will also be working on a project — Combatting the Opioid Crisis.
DEEP DIVE PROBLEM SOLVING
Each project under the new program has been carefully designed to make use of AI, big data, and machine learning and has the potential to change millions of lives. In this way, the teams can shave years or even decades of work from traditional fixes for stubborn social problems. For example, the opioid project starts from the proven premise that most opioid abuse and addiction starts with a prescription. The team can use Watson’s unparalleled abilities to recognize addiction patterns, learn evidence-based rules for more responsible prescription writing, and then develop early warning systems for use by healthcare professionals and public health officials.
“The projects chosen for this year’s Social Good program cover an important range of topics — including predicting new diseases, promoting innovation, alleviating illiteracy and hunger, and helping people out of poverty,” director of IBM Research, Arvind Krishna, said in a press release. “What unifies them all is that, at the core, they necessitate major advances in science and technology. Armed with the expertise of our partners and drawing on a wealth of new data, tools and experiences, Science for Social Good can offer new solutions to the problems our society is facing.”
A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data.
Beneath the surface of the roiling sea of data we’re generating hide exceptional business and sales opportunities. But the problem is there’s now more information available than limited human resources and legacy tools can handle.
Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at. While we’re still least decades away from human-level synthetic intelligence—AI that will match the human brain in reasoning and decision making—machine learning algorithms, computer vision, natural language processing and generation (NLG/NLP), and other forms of narrow artificial intelligence are proving to be the best complement for human activity.
AI-powered tools are now helping scale the efforts of sales teams by gleaning useful patterns from data, finding successful courses of action, and taking care of the bulk of the work in addressing customer needs and grievances.
AI-powered CRM suites
Main providers of Customer Relationship Management (CRM) solutions have started to invest in the added value of AI. Last year,SalesForce, the leader in the CRM industry, announced Einstein, anAI assistant that, when launched, will be omnipresent across its platform. Einstein puts its AI chops to work to continuously study the flood of data SalesForces collects from sales, e-commerce activity, emails, IoT generated data and social media streams among others. The AI engine will then make suggestions across different use cases. For instance, it helps sales reps focus on the most promising leads based on engagement data analysis, or gives advice on when to trigger email campaigns according to customer response history.
SAP, another top competitor in the field, is also joining the fray by adding AI functionalities to its S/4HANA cloud ERP. The tacked-on features will give automated insights into the business data the system collects. This includes monitoring accounts or preparing lists of top vendors for an organization based on their pricing, past performance and ability to deliver.
Oracle also declared its cloud AI project earlier this year, calledAdaptive Intelligence. The initiative involves a series of add-on applications that integrate with its cloud suite. These apps combine third-party data with real-time analytics to optimize decisions and recommendations in various domains. For example, the AI Offers app merges data from the company cloud and the Oracle Data Cloud to extract contextual insights into individual customer behaviors and provide personalized offers as visitors browse websites powered by the Commerce Cloud.
Specialized AI in outbound marketing
Other players are focusing on verticals and optimizing specific disciplines. Growbots, a lead generation platform, uses machine learning algorithms to automate the prospecting process and marketing campaigns. The platform uses machine learning algorithms to scan websites and gather publicly available information, and enrich its database of profiles about people and businesses. Growbotsincorporates this information with client CRM data to identify new potential customers and create tailored prospect lists. The platform integrates with SalesForce and uses AI to enhance and automate email marketing, creating tailored emails for customers, scheduling campaigns and sending follow ups at opportune times. The AI engine uses Natural Language Processing to parse responses and channel positive replies to the sales team.
Such solutions can be a boon to salespersons who are under constant pressure to meet quotas. By enhancing and automating the routine-based parts of the business process, AI-powered tools enable sales teams to focus on their efforts on better serving the more complicated and human-demanding needs of customers. Over time, as these solutions continue to process company and customer data, they become more efficient in their functionality.
Another example is Conversica, an AI-powered assistant that functions like a sales employee and reaches out to anyone who has shown interest in the company, such as by downloading a whitepaper or requesting information from the website. The assistant processes replies from customers, determines feedback and potential questions and crafts a meaningful reply. The assistant passes off the lead to a human salesperson when the time is right.
AI-powered customer service chatbots
Another interesting development in the space is the advent of customer service chatbots, which have become more popular in recent years. Powered by AI algorithms, these bots are becoming much more efficient at independently identifying and resolving customer problems through natural conversation. These assistants free up staff time for more critical and complicated tasks.
Amelia is a virtual customer assistant that uses natural language processing to understand customer queries and provide answer based on data gathered from previous interactions and the company knowledge base. According to estimates, Amelia solves 55 percent of incidents. When it doesn’t have an answer or senses a frustration or hostility, it will pass on to a human operator.
Soul Machines, a eerily named startup based in New Zealand, is creating chatbots with expressive digital faces that understand and manifest human emotion. Nadia, the first iteration of their technology, uses AI algorithms to discern human tone and facial expression. The developers believe these chatbots will eventually create a richer experience and be able to engage customers at a much more personal level.
These are some of the trends that are transforming the ways businesses interact with their customers. AI-powered customer support and management will surely result in more satisfied and less frustrated customers, and more productive sales teams. We expect to see more exciting developments in the space in the coming months.
A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead?
Yoshua Bengio believes that understanding the basics of AI is within every citizen’s reach. That democratizing these issues is important so that our societies can make the best collective decisions regarding the major changes AI will bring, thus making these changes beneficial and advantageous for all.
“Our world is changing in many ways. And one of the things which is going to have a huge impact on our future is Artificial Intelligence.. AI is bringing another Industrial Revolution.
Previous industrial revolutions expended humans’ mechanical power. This new revolution this second Machine Age is going to expand our cognitive abilities our mental power. Computers are not just going to replace manual labor but also mental labor. So where do we stand today?
You may have heard about what happened last March when a machine learning system called AlphaGoused deep learning to beat the world champion at the game of “Go”. “Go” is an ancient Chinese game which had been much more difficult for computers to master than the game of chess. How did we succeed now after decades of AI research?
AlphaGo was trained to play “Go”. first by watching over and over tens of millions of moves made by very strong human players then by playing against itself millions of games.
Machine learning allows computers to learn from examples to learn from data. Machine learning has turned out to be a key to cram knowledge into computers. And this is important. Because knowledge is what enables intelligence.
Putting knowledge into computers had been a challenge for previous approaches to AI. Why?
There are many things which we know intuitively. So we cannot communicate them verbally. We do not have conscious access to that intuitive knowledge. How can we program computers with that knowledge? What’s the solution?
The solution is for machines to learn that knowledge by themselves just as we do. This is important because knowledge is what enables intelligence. My mission has been to contribute to discover and understand principles of intelligence through learning whether animal or human or machine learning.
I and others believe that there are a few key principles just like the laws of physics.. Simple principles which could explain our own intelligence and help us build intelligent machines. For example think about the laws of aerodynamics which are general enough to explain the flight of both birds and planes. Wouldn’t it be amazing to discover such simple but powerful principles that would explain intelligence itself.
Well we’ve made some progress.. My collaborators and I have contributed in recent years in a revolution in AI with our research on neural networks and deep learning an approach to machine learning which is inspired by the brain.
It started with speech recognition on your phones with neural networks since 2012. Shortly after came a breakthrough in computer vision. Computers can now do a pretty good job of recognizing the content of images. In fact, approaching human performance on some benchmarks over the last five years, a computer can now get an intuitive understanding of the visual appearance of a GO board that is comparable to that of the best human players.
More recently following some discoveries made in my lab, deep learning has been used to translate from one language to another and you know start seeing this in Google Translate. This is expanding the computer’s ability to understand and generate natural language.
But don’t be fooled! We are still very very far from a machine that would be as able as humans to learn to master many aspects of our world. So let’s take an example.. Even a two-year-old child is able to learn things in a way that computers are not able to do right now. A two-year-old child actually masters intuitive physics. She knows that when she drops a ball that it is going to fall down. When she spilled some liquids, she expects the resulting mess. Her parents do not need to teach her about Newton’s laws or differential equations. She discovers all these things by herself in an unsupervised way.
Own supervised learning actually remains one of the key challenges for AI. It may take several more decades of fundamental research to crack that not. Unsupervised learning is actually trying to discover representations of the data.
Let me show you an example. Consider a page on the screen that you’re seeing with your eyes or that the computer is seeing as an image a bunch of pixels. In order to answer a question about the content of the image, you need to understand its high-level meaning. This high level meaning corresponds to the highest level of representation in your brain.
Lower down you have the individual meaning of words and even lower down you have characters which make up the words. Those characters could be rendered in different ways with different strokes that make up the characters. Those strokes are made up of edges and those edges are made up of pixels. So these are different levels of representation.
But the pixels are not sufficient by themselves to make sense of the image to answer a high-level question about the content of the page. Your brain actually has these different levels of representation starting with neurons in the first visual area of cortex v1 which recognize edges and then neurons in the second visual area of cortex v2 which recognize strokes and small shapes. Higher up you have neurons which detect parts of objects and then objects and full scenes.
Mule networks when they are trained with images can actually discover these types of levels of representation that match pretty well what we observe in the brain. Both biological neural networks which are what you have in your brain and the deep neural networks that we train on our machines can learn to transform from one level of representation to the next with the higher levels corresponding to more abstract notions.
For example the abstract notion of the character “A” can be rendered in many different ways at the lowest levels as many different configurations of pixels depending on your the position, rotation, font, and so on.
So how do we learn these high levels of representations? One thing that has been very successful up to now in the applications of deep learning is what we call Supervised Learning. With Supervised Learning, The computer needs to be taken by the hand and humans have to tell the computer the answer to many questions.
For example, on millions and millions of images humans have to tell the Machine: Well for this image it is a cat, for this image it is a dog, for this image it is a laptop, for this image is the keyboard, and so on and so on.. millions of times…
This is very painful and we use crowdsourcing to manage to do that. Although this is very powerful and we are already able to solve very interesting problems, humans are much stronger and they can learn over many more different aspects of the world in a much more autonomous way just as we’ve seen with the two-year-old child learning about intuitive physics.
Unsupervised learning could also help us deal with self-driving cars. Let me explain what I mean.. Unsupervised learning allows computers to project themselves into the future to generate plausible futures conditioned on the current situation.
That allows computers to reason and to plan ahead even for circumstances that they have not been trained on. This is important. Because if we use supervised learning, we would have to tell the computers about all the circumstances where the car could be and how human would react in that situation. How did I learn to avoid dangerous fighting behavior? Did I have to die a thousand times in an accident? Well.. That’s the way we’re trying machines right now. So it’s not going to fly or at least not to drive.
What we need is to train our models to be able to generate plausible images, plausible futures be creative. We’re making progress with that.
We are training these deep neural networks to go from high-level meaning to pixels rather than from pixels to high-level of meaning going in the other direction through the levels of representation. In this way the computer can generate images there are new images different from what the computer has seen while it was trained, but are plausible that looked like natural images.
We can also use these models to dream up strange, sometimes scary images just like our dreams and nightmares.
Here are some images that were synthesized by the computer using these deep genitive models they look like natural images but if you look closely you’ll see there are different and they’re still missing some of the important details that we would recognize as natural.
About ten years ago, unsupervised learning has been a key to the breakthrough that we obtained discovering deep learning. This was happening in just a few labs including mine at a time when neural networks were not popular; they were almost abandoned by the scientific community.
Now things have changed a lot. It has become a very hot field. There are now hundreds of students every year applying for graduate studies at my lab with my collaborators.
Montreal has become the largest academic concentration of deep learning researchers in the world. We just received a huge research grant of 94 million dollars to push the boundaries of AI and data science and also to transfer technology of deep learning and data science to industry. Business people stimulated by all this are creating startups, industrial labs many of which near the universities. For example just a few weeks ago, we announced the launch of a startup factory called Element AI which is going to focus on deep learning applications.
There is just not enough deep learning experts. So they’re getting paid crazy salaries and many of my former academic colleagues have accepted generous deals from companies to work in industrial labs.
I for myself have chosen to stay in university to work for the public good, to work with students to remain independent, to guide the next generation of deep learning experts. One thing that we’re doing beyond commercial value is thinking about the social implications of AI.
Many of us are now starting to turn our eyes towards social value added applications like health. We think that we can use deep learning to improve treatment with personalized medicine. I believe that in the future as we collect more data from millions and billions of people around the earth, we’ll be able to provide medical advice to billions of people who don’t have access to it right now.
We can imagine many other applications for social value of AI. For example, something that will come out of our research on natural language understanding is providing all kinds of services like legal services to who can’t afford them.
We are now turning our eyes also towards the social implications of AI in my community. But it’s not just for experts to think about this. I believe that beyond the math and the jargon ordinary people can get a sense of what goes on under the hood enough to participate in the important decisions that will take place in the next few years and decades about AI.
So please set aside your fees and give yourself some space to learn about it. My collaborators and I have written several introductory papers and a book entitled deep learning to help students and engineers jump into this exciting field.
There are also many online resources, software, tutorials, videos. Many undergraduate students are learning a lot of this about research and deep learning by themselves to later join the ranks of labs like mine.
AI is going to have a profound impact on our society. So, it’s important to ask how are we going to use it. Immense positives may come along with negatives such as military use or rapid disruptive changes in the job market. To make sure the collective choices that will be made about AI in the next few years will be for the benefit of all, every citizen should take an active role in defining how AI will shape our future.”
Who is Yoshua Bengio?
Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research,head of the Montreal Institute for Learning Algorithms (MILA), CIFAR Program co-director of the CIFAR program on Learning in Machines and Brains, Canada Research Chair in Statistical Learning Algorithms. His main research ambition is to understand principles of learning that yield intelligence. He teaches a graduate course in Machine Learning (IFT6266) and supervises a large group of graduate students and post-docs. His research is widely cited (over 65000 citations found by Google Scholar in April 2017, with an H-index of 95).
Yoshua Bengio is currently action editor for the Journal of Machine Learning Research, associate editor for the Neural Computation journal, editor for Foundations and Trends in Machine Learning, and has been associate editor for the Machine Learning Journal and the IEEE Transactions on Neural Networks.
Yoshua Bengio was Program Chair for NIPS’2008 and General Chair for NIPS’2009 (NIPS is the flagship conference in the areas of learning algorithms and neural computation). Since 1999, he has been co-organizing the Learning Workshop with Yann Le Cun, with whom he has also created the International Conference on Representation Learning (ICLR). He has also organized or co-organized numerous other events, principally the deep learning workshops and symposiua at NIPS and ICML since 2007.
81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI).
Cowen predicts AI will drive user productivity to materially higher levels, with Microsoft at the forefront.
Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption.
According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.
Cowen sees Salesforce ($CRM), Adobe ($ADBE) and ServiceNow ($NOW) as well-positioned to deliver and monetize new AI-based application services.
These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd). The study is based on interviews with 146 leading AI researchers, entrepreneurs and VC executives globally who are involved in the field of artificial intelligence and related technologies. Please see the Appendix of the study for a thorough overview of the methodology. This study isn’t representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning. Cowen finds the market is still nascent, with CIOs eager to invest in new AI-related initiatives. Time-to-market, customer messaging, product positioning and the value proposition of AI solutions will be critical factors for winning over new project investments.
Key takeaways from the study include the following:
Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption.Customer self-service, Enterprise Resource Planning (ERP), Human Resource Management (HRM) and E-Commerce are additional areas that have upside potential for AI/ML adoption. The following graphic provides an overview of the areas in software that Cowen found the greater potential for AI/ML investment.
www.cowen.com
Artificial Intelligence: Entering A Golden Age For Data Science
81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.
www.cowen.com
How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017
Market forecasts vary, but all consistently predict explosive growth. IDC predicts that the Cognitive Systems and AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 55%. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. IBM claims that Cognitive Computing is a $2T market, including $200B in healthcare/life sciences alone. Tractica forecasts direct and indirect applications of AI software to grow from $1.4B in 2016 to $59.8B by 2025, a 52% CAGR.
www.cowen.com
Artificial Intelligence: Entering A Golden Age For Data Science
According to CBInsights, the number of financing transactions to AI start-ups increased 10x over the last six years, from 67 in 2011 to 698 in 2016. Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include FinTech, Healthcare, Transportation and Retail/e-Commerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.
www.cowen.com
Artificial Intelligence: Entering A Golden Age For Data Science
Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the b top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years. The following graphic compares the top 10 uses cases by projected global revenue.
www.cowen.com
Machine Learning is predicted to generate the most revenue and is attracting the most venture capital investment in all areas of AI. Venture Scanner found that ML raised $3.5B to date (from 400+ companies), far ahead of the next category, Natural Language Processing, which has seen just over $1Bn raised to date (from 200+ companies). Venture Scanner believes that Machine Learning Applications and Machine Learning Platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.
www.cowen.com
Artificial Intelligence: Entering A Golden Age For Data Science
Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future. Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behavior, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.
www.cowen.com
Artificial Intelligence: Entering A Golden Age For Data Science
According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.Machine Learning-based Applications and Deep Learning Neural Networks are experiencing the largest and widest amount of investment attention in the enterprise.
Accenture leverages machine learning in 40% of active Analytics engagements, and nearly 80% of proposed Analytics opportunities today.Cowen found that Accenture’s view is that they are in the early stages of AI technology adoption with their enterprise clients. Accenture sees the AI market growing exponentially, reaching $400B in spending by 2020. Their customers have moved on from piloting and testing AI to reinventing their business strategies and models.
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