Montreal-Toronto AI Startups Have Wide Range of Focus

Includes Healthcare, Biomed, Text Analysis, Legal Research, Image Analysis, Drug Discovery, Education Canada has made a commitment for many years to the study of AI at universities across the county, and today robust business incubation programs supported by Canada’s state and regional governments work to transform research into viable businesses. This AI ecosystem has produced […]

Includes Healthcare, Biomed, Text Analysis, Legal Research, Image Analysis, Drug Discovery, Education

Canada has made a commitment for many years to the study of AI at universities across the county, and today robust business incubation programs supported by Canada’s state and regional governments work to transform research into viable businesses. This AI ecosystem has produced breakthrough research and is attracting top talent and investment by venture capital. Here is a look at a selection of Montreal- and Toronto-based AI startups.

TandemLaunch, Technology Transfer Acceleration

TandemLaunch is a Montreal-based technology transfer acceleration company, founded in 2010, that works with academic researchers to commercialize their technological developments. CEO and General Partner Helge Seetzen was the founder and directs the company’s strategy and operations. TandemLaunch has raised $29.5 million since its founding, according to CrunchBase. The firm has spun out more than 20 companies and has been recognized for supporting women founders.

Seetzen was a successful entrepreneur who co-founded Sunnybrook Technologies and later BrightSide Technologies to commercialize display research developed at the University of British Columbia. BrightSide was sold to Dolby Laboratories for $28 million in 2007.

TandemLaunch provides startups with office space, access to IT infrastructure, shared labs for electronics, mechanical or chemical prototyping, mentoring, hands-on operational support and financing.

Asked by AI Trends to comment, CEO Seetzen said, “TandemLaunch has a long history of building leading AI companies based on technologies from international universities. Example successes include LandR – the world’s largest music production platform – and SportlogiQ which offers AI-driven game analytics for sports. Many younger TandemLaunch companies are at the brink of launching game-changing products onto the market such as Aerial’s AI for motion sensing from Wi-Fi signals which will be released in several countries as a home security solution later this year. With hundreds of AI developers across our portfolio of 20+ companies, TandemLaunch is well positioned to capitalize on AI opportunities of all stripes.”

Other companies in the TandemLaunch portfolio include: Kalepso, focused on blockchain and machine learning; Ora, offering nanotechnology for high-fidelity audio; Wavelite, aiming to increase the lifetime of wireless sensors used in IoT operations; Deeplite, providing an AI-driven optimizer to make deep neural networks faster; Soundskrit, changing how sound is measured using a bio-inspired design; and C2RO, offering a robotic SaaS platform to augment perception and collaboration capabilities of robots.

Learn more at TandemLaunch.

BenchSci for Biomedical Researchers

BenchSci offers an AI-powered search engine for biomedical researchers. Founded in 2015 in Toronto, the company recently raised $8 million in a series A round of funding led by iNovia Capital, with participation including Google’s recently-announced Gradient Ventures.

BenchSci uses machine learning to translate both closed-and open-access data into recommendations for specific experiments planned by researchers.  The offering aims to speed up studies to help biomedical professionals find reliable antibodies and reduce resource waste.

“Without the use of AI, basic biomedical research is not only challenging, but drug discovery takes much longer and is more expensive,” BenchSci cofounder and CEO Liran Belenzon stated in an account in VentureBeat. “We are applying and developing a number of advanced data science, bioinformatics and machine learning algorithms to solve this problem and accelerate scientific discovery by ending reagent failure.” (A reagent is a substance used to detect or measure a component based on its chemical or biological activity.)

In July 2017, Google announced its new venture fund aimed at early-stage AI startups. In the year since, Gradient Ventures has invested in nine startups including BenchSci, the fund’s first known health tech investment and first outside the US.

“Machine learning is transforming biomedical research,” stated Gradient Ventures founding partner Ankit Jain. “BenchSci’s technology provides a unique value proposition for this market, enabling academic researchers to spend less time searching for antibodies and more time working on their experiments.”

BenchSci told VentureBeat is tripled its headcount last year and plans to add 16 new hires throughout 2018.

Learn more at BenchSci.

Imagia to Personalize Healthcare Solutions

Imagia is an AI healthcare company that fosters collaborative research to accelerate accessible, personalized healthcare.

Founded in 2015 in Montreal, the company in November 2017 acquired Cadens Medical Imaging for an undisclosed amount, to accelerate development of its biomarker discovery processes. Founded in 2008, Cadens develops and markets medical imaging software products designed for oncology, the study of tumors.

Venture-backed Imagia acquired Cadens Medical Imaging.

“This strategic transaction will significantly accelerate Imagia’s mission of delivering AI-driven accessible personalized healthcare solutions. Augmenting Imagia’s deep learning expertise with Cadens’ capabilities in clinical AI and imaging was extremely compelling, to ensure our path from validation to commercialization,” stated Imagia CEO Frederic Francis in a press release. “This is particularly true for our initial focus on developing oncology biomarkers that can improve cancer care by predicting a patient’s disease progression and treatment response.”

Imagia co-founder and CTO Florent Chandelier said “Our combined team will build upon the long-term outlook of clinical research together with healthcare partnerships, and the energy and focus of a technology startup with privileged access to deep learning expertise and academic research from Yoshua Bengio’s MILA lab. We are now uniquely positioned to deliver AI-driven solutions across the healthcare ecosystem.”

In prepared remarks, Imagia board chair Jean-Francois Pariseau stated, “Imaging evolved considerably in the past decade in terms of sequence acquisition as well as image quality. We believe AI enables the creation of next generation diagnostics that will also allow personalization of care. The acquisition of Cadens is an important step in building the Imagia platform and supports our strategy of investing in ground breaking companies with the potential to become world leaders in their field.”

Learn more at Imagia.

Ross Intelligence: Where AI Meets Legal Research

Ross Intelligence is where AI meets legal research. The firm was founded in 2015 by Andrew Arruda, Jimoh Ovbiagele and Pargies Dall ‘Oglio, machine learning researchers from the University of Toronto. Ross, headquartered in San Francisco, in October 2017 announced an $8.7 million Series A investment round led by iNovia Capital, seeing an opportunity to compete with the legal research firms LexisNexis and Thomson Reuters.

The platform helps legal teams sort through case law to find details relevant to new cases. Using standard keyword search, the process takes days or weeks. With machine learning, Ross aims to augment the keyword search, speed up the process and improve the relevancy of terms found.

“Bluehill [Research] benchmarks Lexis’s tech and they are finding 30 percent more relevant info with Ross in less time,” stated Andrew Arruda, co-founder and CEO of Ross, in an interview with TechCrunch.

Ross uses a combination of off-the-shelf and proprietary deep learning algorithms for its AI stack. The firm is using IBM Watson for some of its natural language processing as well. To build training data, Ross is working with 20 law firms to simulate workflow example and test results.

Ross has raised a total of $13.1 million in four rounds of financing, according to Crunchbase.

The firm recently hired Scott Sperling, former head of sales at WeWork, as VP of sales. In January, Ross announced its new EVA product, a brief analyzer with some of the power of the commercial version. Ross is giving it away for free to seed the market. The tool can check the recent history related to cited cases and determine if they are still good law, in a manner similar to that of LexisNexis Shepard’s and Thomson Reuters KeyCite, according to an account in LawSites.

EVA’s coverage of cases includes all US federal and state courts, across all practice areas. “With EVA, we want to provide a small taste of Ross in a practical application, which is why we are releasing it completely free,” Arruda told LawSites. “We’re deploying a completely new way to doing research with AI at its core. And because it is based on machine learning, it gets smarter every day.”

For more information, go to Ross Intelligence.

Phenomic AI Uses Deep Learning to Assist Drug Discovery

Phenomic AI is developing deep learning solutions to accelerate drug discovery. The company was founded in Toronto in June 2017 by Oren Kraus, from the University of Toronto, and Sam Cooper, a graduate of the Institute of Cancer Research in London. The aim is to use machine learning algorithms to help scientists studying image screenings to learn which cells are resistant to chemotherapy, thus fighting the recurrence of cancer in many patients. The AI enables the software to comb through thousands of cell culture images to identify those responsible for being chemo-resistant.

Phenomic AI founders Oren Kraus, left, and Sam Cooper. Photo by Olympus Digital Camera

“My PhD at U of T was looking at developing deep-learning techniques to automate the process of analyzing images of cells, so I wanted to create a company looking at this issue,” stated Kraus in an account in StartUp Here Toronto.  “There are key underlying mechanisms that allow cancer cells to survive in the first place. If we can target those underlying mechanisms that prevent cancer coming back in entire groups of patients, that’s what we’re going for.”

Cooper is working towards his PhD with the department of Computational Medicine at Imperial College, London, and also with the Dynamical Cell Systems team at the Institute of Cancer Research. His research focuses on developing deep and reinforcement learning solutions for pharmaceutical research.

An early research partner of Phenomic AI is the Toronto Hospital for Sick Children, in a project to study a hereditary childhood disease.

The company has raised $1.5 million in two funding rounds, according to Crunchbase.

Learn more at Phenomic AI.

Erudite.ai Aims at Peer Tutoring

Erudite.ai is marketing ERI, a product that aims to connect a student who needs help on a subject with a peer who has shown expertise in the same subject. The company was founded in 2016 in Montreal and has raised $1.1 million to date, according to Crunchbase. The firm uses an AI system to analyze the content of conversations and specific issues the student faces. From that, it generates personalized responses for the peer-tutor. ERI is offered free to students and schools.

Erudite.ai is competing for an IBM Watson XPrize for Artificial Intelligence, being one of three top 10 teams announced in December, from 150 entrants competing for $5 million in prize money. President and founder Patrick Poirier was quoted in The Financial Post on the market opportunity, “Tutoring is very efficient at helping people improve their grades. It’s a US $56 billion market. But at $40 an hour, it’s very expensive.” Erudite.ai is giving away its product, for now. The plan is to go live in September and host 200,000 students by year-end. By mid-2019, the company plans to sell a version of the platform to commercial tutoring firms, to help them speed teaching time and reduce costs.

The company hopes to extend beyond algebra to geometry, then the sciences, in two years. “The AI will continue to improve,” states Poirier. “In five years, I hope we will be helping 50 million people.”

Learn more at Erudite.ai.

Keatext Comprehends Customer Communication Text

Keatext’s AI platform interprets customers’ written feedback across various channels to highlight recommendations aimed at improving the customer experience. The firm’s product is said to enable organizations to audit customer satisfaction, identify new trends, and keep track of the impact of actions or events affecting the clients. Keatext’s technology aims to mimic human comprehension of text to deliver reports to help managers make decisions.

Keatext Team, founder Narjes Boufaden in foreground.

The company was founded in 2010 in Montreal by Narjes Boufaden, first as a professional services company. From working with clients, the founder identified a gap in the text analytics industry she felt the firm could address. In 2014, Keatext began offering a SaaS product offering.

Boufaden holds an engineering degree in computer science and a PhD in natural language processing, earned with the supervision of Yoshua Bengio and Guy Lapalme. Her expertise is in developing algorithms to analyze human conversations. She has published many articles on NLP, machine learning, and text mining from conversational texts.

Keatext in April announced a new round of funding, adding CA$1.72 million to support commercial expansion, bringing the company’s funding total to CA$3.32 million since launching its platform two years ago. “This funding will help us gain visibility on a wider scale as well as to consolidate our technological edge,” stated Boufaden in a press release. “Internet and intranet communication allows organizations to hold ongoing conversations with the people they serve. This gives them access to an enormous amount of potentially valuable information. Natural language understanding and deep learning are the keys to tapping into this information and revealing how to better serve their audiences.”

Learn more at Keatext.

Dataperformers in Applied AI Research

Founded in 2013 in Montreal, Dataperformers is an applied research company that works on advanced AI technologies. The company has attracted top AI researchers and engineers to work on Deep Learning models to enable E-commerce and FinTech business uses.

Calling Dataperformers “science-as-a-service,” co-founder and CEO Mehdi Merai stated, “We are a company that solves problems through applied research work in artificial intelligence,” in an article in the Montreal Gazette. Among the first clients is Desjardins Group, an association of credit unions using the service to analyze large data volumes, hoping to discover hidden patterns and trends.

Dataperformers is also working on a search engine for video called SpecterNet, that combines use of AI and computer vision to find specific content. Companies could use the search engine to identify videos where their products appear, then market the product to the video’s audience. The company is using reinforcement learning to help the video search AI to learn on its own.

Learn more at Dataperformers.

Botler.ai Bot Helps Determine Sexual Harassment

Botler.ai was founded in January 2018 by Ritika Dutt, COO, and Amir Moraveg, CEO, as a service to help victims of sexual harassment determine whether they have been violated. The bot was created following a harassment experienced by cofounder Dutt.

Left to right: Cofounders Amir Moravej and Ritika Dutt with advisor Yoshua Bengio. Photo by Eva Blue

She was unsure how to react after the experience, but once she researched the legal code, she gained confidence. “It wasn’t just me making things up in my head. There was a legal basis for the things I was feeling, and I was justified in feeling uncomfortable,” she stated in an account in VentureBeat.

The bot uses natural language processing to determine whether an incident could be classified as sexual harassment. The bot learned from 300,000 court cases in Canada and the US, drawing on testimony from court filings, since testimony aligns most closely with conversational tone. The bot can generate an incident report.

This is Botler.ai’s second product, following a bot made last year to help people navigate the Canadian immigration system.

Yoshua Bengio of MILA is an advisor to the startup.

Next in AI in Canada series: AI in Edmonton

 

  • By John P. Desmond, AI Trends Editor

 

What Machine Learning Practitioners Actually Do, by Rachel Thomas, fast.ai

By Rachel Thomas, founder, fast.ai and Asst. Professor, Data Institute, University of San Francisco There are frequent media headlines about both the scarcity of machine learning talent and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether. In his recent keynote at the TensorFlow DevSummit, Google’s head of AI Jeff […]

By Rachel Thomas, founder, fast.ai and Asst. Professor, Data Institute, University of San Francisco

There are frequent media headlines about both the scarcity of machine learning talent and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether. In his recent keynote at the TensorFlow DevSummit, Google’s head of AI Jeff Dean estimated that there are tens of millions of organizations that have electronic data that could be used for machine learning but lack the necessary expertise and skills. I follow these issues closely since my work at fast.ai focuses on enabling more people to use machine learning and on making it easier to use.

Rachel Thomas, founder, fast.ai

In thinking about how we can automate some of the work of machine learning, as well as how to make it more accessible to people with a wider variety of backgrounds, it’s first necessary to ask, what is it that machine learning practitioners do? Any solution to the shortage of machine learning expertise requires answering this question: whether it’s so we know what skills to teach, what tools to build, or what processes to automate.

This post is the first in a 3-part series. It will address what it is that machine learning practitioners do, with Part 2 explaining AutoML and neural architecture search (which several high profile figures have suggested will be key to decreasing the need for data scientists) and Part 3 will cover Google’s heavily-hyped AutoML product in particular.

Building Data Products is Complex Work

While many academic machine learning sources focus almost exclusively on predictive modeling, that is just one piece of what machine learning practitioners do in the wild. The processes of appropriately framing a business problem, collecting and cleaning the data, building the model, implementing the result, and then monitoring for changes are interconnected in many ways that often make it hard to silo off just a single piece (without at least being aware of what the other pieces entail). As Jeremy Howard et al. wrote in Designing Great Data Products, Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing.

A team from Google, D. Sculley et al., wrote the classic Machine Learning: The High-Interest Credit Card of Technical Debt, about the code complexity and technical debt often created when using machine learning in practice. The authors identify a number of system-level interactions, risks, and anti-patterns, including:

  • glue code: massive amount of supporting code written to get data into and out of general-purpose packages
  • pipeline jungles: the system for preparing data in an ML-friendly format may become a jungle of scrapes, joins, and sampling steps, often with intermediate files output
  • re-use input signals in ways that create unintended tight coupling of otherwise disjoint systems
  • risk that changes in the external world may make models or input signals change behavior in unintended ways, and these can be difficult to monitor

The authors write, A remarkable portion of real-world “machine learning” work is devoted to tackling issues of this form… It’s worth noting that glue code and pipeline jungles are symptomatic of integration issues that may have a root cause in overly separated “research” and “engineering” roles… It may be surprising to the academic community to know that only a tiny fraction of the code in many machine learning systems is actually doing “machine learning”. (emphasis mine)

When machine learning projects fail

In a previous post, I identified some failure modes in which machine learning projects are not effective in the workplace:

  • The data science team builds really cool stuff that never gets used. There’s no buy-in from the rest of the organization for what they’re working on, and some of the data scientists don’t have a good sense of what can realistically be put into production.
  • There is a backlog with data scientists producing models much faster than there is engineering support to put them in production.
  • The data infrastructure engineers are separate from the data scientists. The pipelines don’t have the data the data scientists are asking for now, and the data scientists are under-utilizing the data sources the infrastructure engineers have collected.
  • The company has definitely decided on feature/product X. They need a data scientist to gather some data that supports this decision. The data scientist feels like the PM is ignoring data that contradicts the decision; the PM feels that the data scientist is ignoring other business logic.
  • The data science team interviews a candidate with impressive math modeling and engineering skills. Once hired, the candidate is embedded in a vertical product team that needs simple business analytics. The data scientist is bored and not utilizing their skills.

I framed these as organizational failures in my original post, but they can also be described as various participants being overly focused on just one slice of the complex system that makes up a full data product. These are failures of communication and goal alignment between different parts of the data product pipeline.

So, what do machine learning practitioners do?

As suggested above, building a machine learning product is a multi-faceted and complex task. Here are some of the things that machine learning practitioners may need to do during the process:

Understanding the context:

  • identify areas of the business that could benefit from machine learning
  • communicate with other stakeholders about what machine learning is and is not capable of (there are often many misconceptions)
  • develop understanding of business strategy, risks, and goals to make sure everyone is on the same page
  • identify what kind of data the organization has
  • appropriately frame and scope the task
  • understand operational constraints (e.g. what data is actually available at inference time)
  • proactively identify ethical risks, including how your work could be mis-used by harassers, trolls, authoritarian governments, or for propaganda/disinformation campaigns (and plan how to reduce these risks)
  • identify potential biases and potential negative feedback loops

Read the source post at fast.ai.

How to Map Out a Career Path in This Early Stage of AI

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and […]

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology

Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and for years to come.

Stephanie Glass

Yet despite all the AI hype and initial successes, it remains in its infancy. That makes now the ideal time for young people to build the knowledge, skill sets and connections they need to capitalize on the fast-growing market for AI jobs and build a strong AI career.

One reason is simply practical. Gartner predicts that AI may eliminate 1.8 million jobs by 2020, yet is on track to create 2.3 million new positions. It clearly makes sense to map out an AI career path as new roles emerge that focus on problem-solving, collaboration and strategic decision-making.

The second reason is that AI offers a truly exciting opportunity to make an impact on our world. AI is an ideal career path for undergraduates and recent graduates with a passion for technology and an entrepreneurial spirit who relish a challenge and a front-line role in game-changing innovation.

AI has been called “the next Industrial Revolution,” and I think that analogy is spot on. The convergence of machine learning, big data and internet-scale cloud compute power is opening opportunities to transform healthcare, grow the economy, reduce waste, save energy, improve education, strengthen security and decrease poverty.

There’s virtually no area of society that will be untouched by AI. Given the myriad possibilities, how does a young person chart his or her course in the AI field?

Practical steps to an AI career

One first step is to simply narrow down key interests and strengths. AI offers technological roles in data science, machine learning development and architecting an AI technology stack. And it offers plenty of positions as a user or analyst with AI software, or in sales and marketing, HR, customer support and more.

Company type is another important consideration, as B2B and B2C can be distinctly different work environments. Candidates should also target industries they gravitate toward, be it technology, manufacturing, retail, higher education, services or others. To narrow down interests, hands-on research and learning is available through avenues such as:

AI and data science courses. Many free and low-cost online learning opportunities in AI can be found at providers such as Udacity, Coursera, Codeacademy and fast.ai. These courses are a great way to build knowledge and enrich a resume.

Industry and company conferences. AI experts and thought leaders discuss trends at conferences hosted by NIPS, Re-Work, O’Reilly, Gartner, AI World and others, while AI vendors also sponsor conferences. Students and young professionals are often able to gain free or discounted admission.

Meetup.com. The website lists many regional in-person meetups among AI enthusiasts, as well as online groups and discussions on topical areas including machine learning, Python, R, data science and advanced statistics.

General immersion. Keeping up-to-date on fast-moving AI news and trends pays off. So does using AI products, sharing code on GitHub or entering a code contest on the global Kaggle community. In short, would-be AI professionals should learn as much as they can, as fast as they can.

With this knowledge, young people are better able to address two key questions that can guide AI careers:

  1. What’s my prospective employer’s technology stack?
  2. What are the employer’s leadership motivations and potential social impact?

As a Stanford graduate now working in artificial intelligence, those were critical considerations for me as I looked to move from traditional software companies into the AI space. I had decided to pursue an AI career because of the positive change that AI promises to unleash. It was a matter of finding an employer that aligned with my interests and skills.

Read the source article in Entrepreneur.

How to Land a Data Scientist Job at Your Dream Company — My Journey to Airbnb

By Kelly Peng, Data Scientist, Airbnb I just started my new job at Airbnb as a data scientist a month ago, and I still feel that I’m too lucky to be here. Nobody knows how much I wanted to join this company — I had pictures of Airbnb office stuck in front of my desk; I had […]

By Kelly Peng, Data Scientist, Airbnb

I just started my new job at Airbnb as a data scientist a month ago, and I still feel that I’m too lucky to be here. Nobody knows how much I wanted to join this company — I had pictures of Airbnb office stuck in front of my desk; I had my iPhone wallpaper set as a photo of me standing in front of the Airbnb logo; I applied for positions at Airbnb four times, but only heard back from the recruiter in the last time…

Kelly Peng, data scientist, Airbnb

In the past, when people asked me, “Which company do you want to work for the most?” I dare not to say “Airbnb” because when I said that, they replied to me, “Do you know how many people want to work there? How many of them eventually got in? Come on, be realistic.”

The result proves that nothing is impossible. Since many of my friends asked me to share about my job search experience, I think it might be helpful to write it down and share with people.

Some data…

To provide an overview of my job search process:

  • Applications: 475
  • Phone interviews: 50
  • Finished data science take-home challenges: 9
  • Onsite interviews: 8
  • Offers: 2
  • Time spent: 6 months

As you probably can see from the data, I’m not a strong candidate because, otherwise, I would just apply for a few positions and receive a bunch of offers. Yes, I used to be super weak; I used to be the kind of candidates who are wasting interviewers’ time. But “who you were months ago doesn’t matter, what matters is who you are becoming.”

The road less traveled to a data scientist job

A little bit about my background, I obtained a Bachelor’s degree in Economics from a university in China and a Master’s degree in Business Administration from the University of Illinois at Urbana-Champaign. After graduated, I worked as a data analyst for two years, with 7 months as a contractor at Google, and another 1 year 4 months at a startup. My work was mostly about writing SQL queries, building dashboards, and give data-driven recommendations.

After realizing that I was not learning and growing as expected, I left my job and applied for Galvanize Data Science Immerse program, which is a 12-week boot camp in San Francisco. I failed the statistics interview to enter the boot camp program for 4 times, got admitted after taking the statistics interview for the fifth time.

The content taught at Galvanize was heavy on Python and machine learning, and they assume you already have a strong foundation in statistics. Unsurprisingly, I struggled a lot in the beginning, because I didn’t know much about programming, nor was I strong in statistics. I had no choice but to work really hard. During my time at Galvanize, I had no break, no entertainment, no dating, nothing else but more than 12 hours study every day. And I got much more comfortable with the courses later on.

However, I still embarrassed myself for uncountable times in interviews when I first started the job search process. Because the gap between a real data scientist and I was so huge that even though I was hardworking, the 12-week study was far from enough to make a career transformation. So I applied, interviewed, failed, applied again, interviewed again, failed again. The good thing is, each time I got to learn something new, and became a little bit stronger.

In March 2018, I have been unemployed for almost a year since I quitted my previous job. With only ~$600 in my bank account, I had no idea how to pay for the next month’s rent. What’s even worse, if I couldn’t find a job by the end of April 2018, I have to leave the U.S. because my visa will expire.

Luckily, after so much practice and repetition, I’ve grown from someone who doesn’t know how to introduce herself properly, doesn’t remember which one of Lasso and Ridge is L1, knows nothing about programming algorithms, into someone who knows she is ready to get what she wants.

When I entered the final interview at Airbnb, I had one data scientist offer in hand; thus, I was not nervous at all. My goal for the final interview was to be the best version of myself and leave no regret. The interview turned out to be the best one I have ever had. They gave me the offer, and all the hard work and sleepless nights paid off.

Read the source blog post at Toward Data Science.

The Mathematics of Machine Learning

By Wale Akinfaderin, PhD candidate in Physics at Florida State University In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually […]

By Wale Akinfaderin, PhD candidate in Physics at Florida State University

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post.

Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

WHY WORRY ABOUT THE MATHS?

There are many reasons why the mathematics of Machine Learning is important and I’ll highlight some of them below:

1. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

2. Choosing parameter settings and validation strategies.

3. Identifying underfitting and overfitting by understanding the Bias-Variance tradeoff.

4. Estimating the right confidence interval and uncertainty.

WHAT LEVEL OF MATHS DO YOU NEED?

The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques. The answer to this question is multidimensional and depends on the level and interest of the individual. Research in mathematical formulations and theoretical advancement of Machine Learning is ongoing and some researchers are working on more advance techniques. I’ll state what I believe to be the minimum level of mathematics needed to be a Machine Learning Scientist/Engineer and the importance of each mathematical concept.

1. Linear Algebra: A colleague, Skyler Speakman, recently said that “Linear Algebra is the mathematics of the 21st century” and I totally agree with the statement. In ML, Linear Algebra comes up everywhere. Topics such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigendecomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learning. The amazing thing about Linear Algebra is that there are so many online resources. I have always said that the traditional classroom is dying because of the vast amount of resources available on the internet. My favorite Linear Algebra course is the one offered by MIT Courseware (Prof. Gilbert Strang).

2. Probability Theory and Statistics: Machine Learning and Statistics aren’t very different fields. Actually, someone recently defined Machine Learning as ‘doing statistics on a Mac’. Some of the fundamental Statistical and Probability Theory needed for ML are Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.

3. Multivariate Calculus: Some of the necessary topics include Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution.

4. Algorithms and Complex Optimizations: This is important for understanding the computational efficiency and scalability of our Machine Learning Algorithm and for exploiting sparsity in our datasets. Knowledge of data structures (Binary Trees, Hashing, Heap, Stack etc), Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods are needed.

5. Others: This comprises of other Math topics not covered in the four major areas described above. They include Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits), Information Theory (Entropy, Information Gain), Function Spaces and Manifolds.

Read the source post at Dataconomy.

AI Influencing Emerging Education Tech Companies

AI technology is being incorporated in solutions offered by some of the 11 startups or newcomers to education chosen from a field of more than 40 companies as “emerging partners” by the State Educational Technology Directors Association (SETDA) for the 2018-19 school year. The program introduces companies to state-level digital education leaders, where they can gain insights into […]

AI technology is being incorporated in solutions offered by some of the 11 startups or newcomers to education chosen from a field of more than 40 companies as “emerging partners” by the State Educational Technology Directors Association (SETDA) for the 2018-19 school year.

The program introduces companies to state-level digital education leaders, where they can gain insights into the K-12 market. For ed-tech directors, the focus is on locating companies that are “innovative and creative in solving problems that exist in the market,” said Melissa Greene, the director of strategic partnerships for SETDA, in an interview.

Among the newly selected emerging partners are Wonder Workshop, a company that built a national consumer brand, and is now looking to make inroads in K-8 coding instruction; a startup called Loose Canon launched by an author and former English teacher who wants to encourage educators to offer students free choice in the books they read for credit in classes, and Ask School Data, founded by a 35-year district technologist who wants teachers to be able to access student data by speaking to a device driven by artificial intelligence.

This is the sixth year that the collaboration has spotlighted new technologies for ed-tech leaders. “It opens the door to important relationships and conversations while providing valuable opportunities for these growing companies to get in front of nearly all 50 states at the same time,” said Tracy Weeks, SETDA’s executive director.

At the same time, the national organization benefits when the emerging partners “help teach us about what’s up and coming, what’s new,” said Greene. More companies are returning for year two of the program than ever before, she said.

Of the newcomers that applied, Greene said there were more niche ed-tech solutions this year than ever before. Where learning management systems and video education programs predominated in the past, this group of applicants represented providers aimed at more specific needs.

Companies Making State Connections

Returning for the third year of partnering with SETDA are Classcraft, LeaRn and MIDAS Education. Second-year returnees will be CatchOn, Cignition Inc., Readorium, Streamable Learning, and Vital Insight.

Here’s a look at the first-year cohort:

  • Ask School Data, based on Amazon’s Alexa platform, retrieves student data on voice command and recites it aloud to an educator;
  • Blending Education advances the idea of “microlearning” as a way of delivering content in small, manageable units, as an avenue to personalized learning;
  • GreyEd Solutions markets FilterED, an adaptive, cloud-based tool for school leaders to view the current technology landscape with the evidence, data, and context needed to prioritize, implement, measure, and monitor ongoing technology initiatives;
  • GoEnnounce offers a platform where students can build a positive digital image in their own learning e-portfolios;
  • Kiddom provides assessment, curriculum development, messaging, and analytics in one collaborative learning platform;
  • Leaderally is a learning platform that provides professional development;
  • Loose Canon is a web service designed to encourage English teachers to allow students to freely choose what they will read for credit;
  • RFPMatch.com is a source for locating and filtering RFP opportunities;
  • Tresit Group specializes in active threat response and risk management for schools and other organizations;
  • WISEDash Local is a Wisconsin nonprofit consortium connecting districts to data dashboards;
  • Wonder Workshop offers curriculum and professional development to teach coding in K-8 with its robots.

Read the source article at EdWeek.

Here Are 10 Free Must-Read Books for Machine Learning and Data Science

By Matthew Mayo, KDnuggets Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started. It’s time for another collection of free machine learning and data science books to kick off […]

By Matthew Mayo, KDnuggets

Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.

1. Python Data Science Handbook
By Jake VanderPlas

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it’s a fast-paced introduction to the Python language aimed at researchers and scientists.

  1. Neural Networks and Deep Learning

By Michael Nielsen

Neural Networks and Deep Learning is a free online book. The book will teach you about:

– Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data

– Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

  1. Machine Learning & Big Data

By Kareem Alkaseer

This is a work in progress, which I add to as time allows. The purpose behind it is to have a balance between theory and implementation for the software engineer to implement machine learning models comfortably without relying too much on libraries. Most of the time the concept behind a model or a technique is simple or intutivei but it gets lost in details or jargon. Also, most of the time existing libraries would solve the problem at hand but they are treated as black boxes and more often than not they have their own abstractions and architectures that hide the underlying concepts. This book’s attempt is to make the underlying concepts clear.

5. Statistical Learning with Sparsity: The Lasso and Generalizations
By Trevor Hastie, Robert Tibshirani, Martin Wainwright

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. This book descibes the important ideas in these areas in a common conceptual framework.

  1. Statistical inference for data science

By Brian Caffo

This book is written as a companion book to the Statistical Inference Coursera class as part of the Data Science Specialization. However, if you do not take the class, the book mostly stands on its own. A useful component of the book is a series of YouTube videos that comprise the Coursera class.

The book is intended to be a low cost introduction to the important field of statistical inference. The intended audience are students who are numerically and computationally literate, who would like to put those skills to use in Data Science or Statistics. The book is offered for free as a series of markdown documents on github and in more convenient forms (epub, mobi) on LeanPub and retail outlets.

Read the complete source list at KDnuggets.com.

High School Grads: Now You Can Major in AI and Become a Very Hot Job Candidate

It’s a college major that sounds straight out of science fiction: Starting this fall, at least two U.S. schools are offering degrees focused on artificial intelligence. Carnegie Mellon University in Pittsburgh announced on May 10 that the school will launch a bachelor of science program in artificial intelligence this fall. “Specialists in artificial intelligence have never been more important, in shorter supply or in greater […]

It’s a college major that sounds straight out of science fiction: Starting this fall, at least two U.S. schools are offering degrees focused on artificial intelligence.

Carnegie Mellon University in Pittsburgh announced on May 10 that the school will launch a bachelor of science program in artificial intelligence this fall.

“Specialists in artificial intelligence have never been more important, in shorter supply or in greater demand by employers,” said Andrew Moore, dean of the School of Computer Science, in a statement.

Students in the computer-science school can enter the degree program in their second year. The course of study will include the same computer science and math courses as other students in the school, but will “focus more on how complex inputs — such as vision, language and huge databases — are used to make decisions or enhance human capabilities,” the statement says. Additional course work will focus on “AI-related subjects such as statistics and probability, computational modeling, machine learning, and symbolic computation.”

And good news for those who have seen The Terminator a few too many times.  Reid Simmons, research professor of robotics and computer science and director of the new AI degree program, says the program will emphasize ethics and social responsibility. It will include “independent study opportunities in using AI for social good, such as improving transportation, health care or education.”

Carnegie Mellon isn’t alone: In January, the Milwaukee School of Engineering announced it will offer a computer science degree focused on artificial intelligence beginning in fall 2018. (Hat tip to Engadget for noticing.)

“Artificial intelligence and deep learning have reached a golden age thanks to recent hardware and software advancements,” Dr. Derek Riley, program director of the new computer science degree, said in a statement. “Our students will apply computer science theory, machine learning algorithms, and software engineering practices to produce computing solutions for a wide variety of problems in many industries.”

“Like all other MSOE programs, computer science students will be taught an industry-driven curriculum in an application-oriented environment, ensuring that they are prepared to hit the ground running upon graduation,” said Dr. John Walz, president of MSOE.

No question, it’s a timely degree. The fast-growing field needs trained employees, Microsoft Learning group program manager Matt Winkler told Indian news agency PTI in an article published Sunday.

“A lot of folks are very, very excited about (AI) and then they want to go and make that real,” Winkler said. “And when they go to make that real, there’s a really large skills shortage.”

Artificial intelligence goes well beyond the in-home digital assistance such as Google Assistant and Amazon’s Alexa. The concept is pushing beyond the tech industry into firms of all types. From aiding in recruiting to fraud detection to inventory control, the use of AI in business is expected only to continue to grow.

Read the source article at Inc.com.

Here are 22 Selected Top Papers on Deep Learning

By Asif Razzaq, Digital Health Business Strategist, cofounder MarkTechPost 1. Deep Learning, by Yann L., Yoshua B. & Geoffrey H. (2015) Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object […]

By Asif Razzaq, Digital Health Business Strategist, cofounder MarkTechPost

1. Deep Learning, by Yann L., Yoshua B. & Geoffrey H. (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.

2. Visualizing and Understanding Convolutional Networks, by Matt Zeiler, Rob Fergus

The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery.

3. TensorFlow: a system for large-scale machine learning, by Martín A., Paul B., Jianmin C., Zhifeng C., Andy D. et al. (2016)

TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research.

4. Deep learning in neural networks, by Juergen Schmidhuber (2015)

This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

5. Human-level control through deep reinforcement learning, by Volodymyr M., Koray K., David S., Andrei A. R., Joel V et al (2015)

Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games.

6. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, by Christian S., Sergey I., Vincent V. & Alexander A A. (2017)

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. With an ensemble of three residual and one Inception-v4, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.

Read the source post at MarkTechPost.com.

This High School Student is Shaking Up AI With OpenAI Project

Since its founding by Elon Musk and others nearly two years ago, nonprofit research lab OpenAI has published dozens of research papers. One posted online recently is different: Its lead author is still in high school. The wunderkind is Kevin Frans, a senior currently working on his college applications. He trained his first neural net—the kind of system that tech […]

Since its founding by Elon Musk and others nearly two years ago, nonprofit research lab OpenAI has published dozens of research papers. One posted online recently is different: Its lead author is still in high school.

The wunderkind is Kevin Frans, a senior currently working on his college applications. He trained his first neural net—the kind of system that tech giants use to recognize your voice or face—two years ago, at the age of 15. Inspired by reports of software mastering Atari games and the board game Go, he has since been reading research papers and building pieces of what they described. “I like how you can get computers to do things that previously you would think were impossible,” Frans says, flashing his ready smile. One of his creations is an interactive webpage that automatically colors in line drawings, in the style of manga comics.

Frans landed at OpenAI after taking on one of the lab’s list of problems in need of new ideas. He made progress, but got stuck and emailed OpenAI researcher John Schulman for advice. After some back and forth on the matter of trust region policy optimization, Schulman checked out Frans’s blog and got a surprise. “I didn’t expect from those emails that he was in high school,” he says.

Frans later met Schulman when he interviewed for an internship at OpenAI. When he turned up for work in San Francisco’s Mission District this summer, Frans was the only intern without a degree or studying in grad school. He started working on a tricky problem that holds back robots and other AI systems—how can machines tap what they’ve previously learned to solve new problems?

Humans do this without a second thought. Even if you’re making a recipe for the first time, you don’t have to re-learn how to caramelize onions or sift flour. By contrast, machine-learning software generally has to repeat its lengthy training process for every new problem—even when they have common elements.

Frans’s new paper, with Schulman and three others affiliated with the University of California Berkeley, reports new progress on this problem. “If it could get solved it could be a really big deal for robotics but also other elements of AI,” Frans says. He developed an algorithm that helped virtual legged robots learn which limb movements could be applied to multiple tasks, such as walking and crawling. In tests, it helped virtual robots with two and four legs adapt to new tasks, including navigating mazes, more quickly. A video released by OpenAI shows an ant-like robot in those tests. The work has been submitted to ICLR, one of the top conferences in machine learning. “Kevin’s paper provides a fresh approach to the problem, and some results that go beyond anything demonstrated previously,” Schulman says.

Frans grapples with challenging motion problems away from computers, too, as a black belt in Tae Kwon Do. Some of his enthusiasm for AI may come just from inhaling the air on his way to Gunn High School in Palo Alto, California, the heart of Silicon Valley. Frans says he works on his AI projects without help from his parents, but he isn’t the only computer whiz in the house. His father works on silicon-chip design at publicly listed semiconductor company Xilinx.

As you may have guessed, Frans is an outlier. Olga Russakovsky, a professor at Princeton who works on machine vision, says making research contributions in machine learning so young is unusual. In general, it’s harder for school kids to try machine learning and AI than subjects such as math or science with a long tradition of extra-curricular competitions and mentoring, she says. Access to computing power can be a hurdle as well. When Frans’s desktop computer wasn’t powerful enough to test one of his ideas, he pulled out his debit card and opened an account with Google’s cloud-computing service to put his code through its paces. He advises other kids interested in machine learning to give it a shot. “The best thing to do is to go out and try it, make it yourself from your own hands,” he says.

Russakovsky is part of a movement among AI researchers trying to get more high schoolers tinkering with AI systems. One motivation is a belief that the field is currently too male, well-off, and white. “AI is a field that’s going to revolutionize everything in our society, and we can’t have it be built by people from a homogenous group that doesn’t represent society as a whole,” Russakovsky says. She co-founded AI4ALL, a foundation that organizes camps that give high-school students from diverse backgrounds a chance to work with and learn from AI researchers.

Read the source article in Wired.