Become an AI Company in 90 Days

“Researchers are continually publishing effective AI models. Hardware is a commodity. But build the highest-quality proprietary dataset and you’ll crush the competition every time.”

I had a day off and the electrician took down my power for the day so I had some much anticipated time on my hands. And just as any other geek would do…I used the time to tear through an unsolicited but gratis book I received in the mail over the holidays.

The book titled, “Become An AI Company in 90 Days,” was written by Kevin Dewalt and produced by Russ Rands.

The book is broken into 4 parts including:

  • AI Fundamentals
  • Discovering AI Opportunities
  • Building a Winning Strategy
  • Launching Your First AI Product

Here’s the scoop:

    Author is legitimate and knows his stuff (Stanford AI experience)
    He has clearly worked with some large corporations based on some of his anecdotes (policy and cross functional alignment will be some of the toughest challenges)
    The instructions are specific and practical (top of line system for under $5K and reasonable data set size…good rule of thumb is 50,000 labeled examples)
    Book length of 132 pages is easily consumable in an afternoon
    Content is well written and understandable without an advanced degree in mathematics
    Includes examples that help bring the material to life (automated claims processing for the AI Canvas tool)

My Review: It’s a solid “A” and I recommend you check it out.

Get a free copy: https://prolego.io/book.html

Note: I do not have any personal or professional connection to the authors, and I do not have any financial interest in the company or the book

Explore the future of retail at Digital Retail Forum 2019

“JOHANNESBURG, SOUTH AFRICA – Media OutReach – 18 January 2019 – There is so much more in store for the African retail industry. According to research conducted by PWC, many sub-Saharan African countries have emerged among the world’s fastest-growing economies. This kind of development is due, in part, to a great deal of economic and social change, but also because of the rapid advancement in technology and changing consumer trends.

Key topics to be discussed:

• Leveraging data to understand customer segments, manage risks and enhance decision making.

• Mapping your journey from brick-and-mortar to eCommerce.

• Leveraging mobile technology to enhance your in-store experience.

• Making Mobile Payment for Everything a reality.

• Leveraging AI to impact your bottom line and CX.

• Real-World retail digital transformation stories.

• Order Fulfillment: Improving efficiency and speed of delivery.

• Understanding the key drivers within the South African retail sector.

• Understanding the digital customer’s journey.

• Utilizing technology to identify customer pain-points.

• Optimizing inventory management and supply chains with technology.

• Closing the gap between technology and personalization.

• Customer engagement with interactive digital signage and point-of-sale (POS) solutions.

• Delivering a technology platform that empowers the business and delights customers.

• Assessing emerging retail models and technologies that will define 2019 and beyond.

• Adapting to Generation Z — “the digital shopper”.

• The store of the future.”

https://vietnamnews.vn/media-outreach/484095/explore-the-future-of-retail-at-digital-retail-forum-2019.html#D2ipJMFejHe0FELU.97

An executive’s guide to AI – McKinsey

  • One of the highest rated McKinsey articles of the year is titled, “An executive’s guide to AI.” It’s a quick overview including key elements and terminology.
  • Artificial Intelligence
    • AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving.
    • Timeline
  • Machine Learning
    • Machine- learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction.

    Types of analytics

    • Descriptive, predictive, prescriptive
  • Major Types
    • Supervised, unsupervised, reinforcement

    Deep Learning

    • Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches.
  • Major Models
    • Convolutional neural network CNN
    • Recurrent neural network

    Business Cases

      Predict Call Center Volume
    • Predict power usage in an electrical grid distribution
    • Detect fraudulent activity for credit cards
    • Simple low-cost image classification
    • Forecast product demand and inventory
    • Predict the price of cars
    • Predict probability of patient joining healthcare
    • Predict prices that will be paid for a product

    https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-aihttps://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai

    Microsoft, Google Use Artificial Intelligence to Fight Hackers

    • “Machine learning is a very powerful technique for security—it’s dynamic, while rules-based systems are very rigid,” says Dawn Song, a professor at the University of California at Berkeley’s Artificial Intelligence Research Lab. “It’s a very manual intensive process to change them, whereas machine learning is automated, dynamic and you can retrain it easily.”
    • “We will see an improved ability to identify threats earlier in the attack cycle and thereby reduce the total amount of damage and more quickly restore systems to a desirable state,” says Amazon Chief Information Security Officer Stephen Schmidt.
    • A Microsoft system designed to protect customers from fake logins had a 2.8 percent rate of false positives
    • To do a better job of figuring out who is legit and who isn’t, Microsoft technology learns from the data of each company using it, customizing security to that client’s typical online behavior and history. Since rolling out the service, the company has managed to bring down the false positive rate to .001 percent. “

    https://www.google.com/amp/amp.timeinc.net/fortune/2019/01/05/microsoft-google-artificial-intelligence

    Follow the leaders: Machine learning and AI in the retail sector

    “So where should retailers start? The initiative needs to come from the top, Choudhary said.

    “C-suite executives need to be front and centre when driving AI projects for their organisations. They need to have a vision and plan for enterprise-wide AI strategies before they begin to be implemented,” he said.

    Evanna Kearins, VP global field marketing at DataStax, agreed.

    “It tends to be the CEO or COO that makes this happen. They are the ones that have to meet their targets, and they have to be able to demonstrate how they will compete with the global e-commerce players out there.”

    While AI may garner the most attention at the consumer end, the efforts most likely to get signed off are those with the greatest RoI, which will generally be about automating back-end systems and business processes. These will also be among the most complex to deliver.”

    https://www.computing.co.uk/ctg/analysis/3068752/follow-the-leaders-machine-learning-and-ai-in-the-retail-sector

    The Essence of Machine Learning

    “Tom Mitchell

    A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.1

    Ian Goodfellow, Yoshua Bengio & Aaron Courville

    Machine learning is essentially a form of applied statistics with increased emphasis on the use of computers to statistically estimate complicated functions and a decreased emphasis on proving confidence intervals around these functions[.]2

    Ian Witten, Eibe Frank & Mark Hall

    [W]e are interested in improvements in performance, or at least he potential for performance, in new situations.

    Things learn when they change their behavior in a way that makes them perform better in the future.

    Learning implies thinking and purpose. Something that learns has to do so intentionally.

    Experience shows that in many applications of machine learning to data mining, the explicit knowledge structures that are acquired, the structural descriptions, are at least as important as the ability to perform well on new examples. People frequently use data mining to gain knowledge, not just predictions.3

    Christopher Bishop

    The result of running the machine learning algorithm can be expressed as a function y(x) which takes a new digit image x as input and that generates an output vector y, encoded in the same way as the target vectors. The precise form of the function y(x) is determined during the training phase, also known as the learning phase, on the basis of the training data. Once the model is trained it can then determine the identity of new digit images, which are said to comprise a test set. The ability to categorize correctly new examples that differ from those used for training is known as generalization. In practical applications, the variability of the input vectors will be such that the training data can comprise only a tiny fraction of all possible input vectors, and so generalization is a central goal in pattern recognition.4″

    https://www.kdnuggets.com/2018/12/essence-machine-learning.html

    HBR’s 10 Must Reads on AI, Analytics, and the New Machine Age (Paperback + Ebook)

    “Machine learning and data analytics are powering a wave of groundbreaking technologies. Is your company ready? If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We’ve combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can’t ignore them. In this book you’ll learn how: Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insights; Blockchain has the potential to restructure the economy; Drones and driverless vehicles are becoming essential tools; 3-D printing is making new business models possible; Augmented reality is transforming retail and manufacturing; Smart speakers are redefining the rules of marketing; Humans and machines are working together to reach new levels of productivity. This collection of articles includes “Artificial Intelligence for the Real World,” by Thomas H. Davenport and Rajeev Ronanki; “Stitch Fix’s CEO on Selling Personal Style to the Mass Market,” by Katrina Lake; “Algorithms Need Managers, Too,” by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; “Marketing in the Age of Alexa,” by Niraj Dawar; “Why Every Organization Needs an Augmented Reality Strategy,” by Michael E. Porter and James E. Heppelmann; “Drones Go to Work,” by Chris Anderson; “The Truth About Blockchain,” by Marco Iansiti and Karim R. Lakhani; “The 3-D Printing Playbook,” by Richard A. D’Aveni; “Collaborative Intelligence: Humans and AI Are Joining Forces,” by H. James Wilson and Paul R. Daugherty; “When Your Boss Wears Metal Pants,” by Walter Frick; and “Managing Our Hub Economy,” by Marco Iansiti and Karim R. Lakhani. Save 30% off the individual component prices when you order this paperback + ebook collection.”

    https://hbr.org/product/hbr-s-10-must-reads-on-ai-analytics-and-the-new-machine-age-paperback-ebook/1073BN-BUN-ENG

    Everything You Need To Know About AI In Healthcare

    • “A study by Accenture has predicted that growth in the AI healthcare space is expected to touch $6.6 Bn by 2021 with a CAGR of 40%.
    • A report by Juniper Research states that chatbots will be responsible for saving $8 Bn per annum of costs by 2022 for Retail, ecommerce, Banking, and Healthcare
    • The same research study also predicts that the success of chatbot interactions where no human interventions take place will go up to 75% in 2022 from 12% in 2017.
    • In 2017, Scanadu developed doc.ai. The application takes away one task from doctors and assigns it to the AI – the job of interpreting lab results.
    • Medical image diagnosis is another AI use case in healthcare. One of the most significant issues that medical practitioners face is sifting through the volume of information available to them, thanks to EMRs and EHRs.
    • Artificial Intelligence in Healthcare also talks about deep learning. Researchers are using deep learning to train machines to identify cancerous tissues with an accuracy comparable to a trained physicist.
    • Machine learning in healthcare can help enhance the efforts in pathology often traditionally left to pathologists as they often have to evaluate multiple images in order to reach a diagnosis after finding any trace of abnormalities.
    • Another similar solution is Moon developed by Diploid which enables early diagnosis of rare diseases through the software, allowing doctors to begin early treatment.
    • Cybersecurity has become a significant concern for healthcare organizations, threatening to cost them $380 per patient record.
    • The AiCure app developed by The National Institutes of Health helps monitor medication by a patient.”

    https://www.google.com/amp/s/inc42.com/resources/everything-you-need-to-know-about-ai-in-healthcare/amp/

    Interview with Nathaniel Gates, CEO & Co-Founder at Alegion

    How should young technology professionals train themselves to work better with AI and virtual assistants?

    Honestly, I think there’s little likelihood that we’ll need to learn to work with bots. As I said earlier, we already interact with AI in countless ways, without realizing it.

    I actually think the big challenge ahead of us lies in teaching our AIs and virtual assistants and bots how to work with each other. Integrating software programs has always been hard, and we’ve solved the problem by forcing the use of inflexible, hard-wired APIs.

    The Good, Bad and Ugly about AI that you have heard or predict –

    We are witness to the ugly every day. The lack of good training data is a major obstacle to the deployment of AI systems everywhere.”

    https://aithority.com/interviews/interview-with-nathaniel-gates-ceo-co-founder-at-alegion/

    The Amazing Ways TD Bank, Canada’s Second-Largest Bank, Uses Big Data, AI & Machine Learning

    “The team established that this came down to

    1 What is the data?

    2 Who can access it?

    3 Under what circumstances can they access it?

    • TD Bank Group is a leading Canadian bank and the sixth largest bank in North America by branches, employing more than 85,000 people. The bank provides its services to around 25 million customers, and its guiding philosophy is “legendary customer service.”
    • By moving to a data-lake infrastructure, and switching to providing data-as-a-service functions, TD Bank effectively democratized access to the information it gathers and stores as part of its business.
    • TD Bank’s Hadoop private cloud was built around Cloudera’s solution, and the bank looked to Talend as its integration partner 
    • The infrastructure has also made it possible to create customer-centric digital services such as its MySpend app, which allows customers to track their monthly spending.
    • This capability to act on data-driven insights received a boost with the acquisition this year of Toronto machine learning experts Layer 6. This investment in smart, self-learning technology will help it build systems that can more accurately predict customer needs.”

    https://www.forbes.com/sites/bernardmarr/2018/12/18/the-amazing-ways-td-bank-canadas-second-largest-bank-uses-big-data-ai-machine-learning/#588f7e7a65be