Strategy & Tactics

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• Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
• Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
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• Moat
• Perspective ... Context ... In-Context Learning (ICL) ... Transfer Learning ... Out-of-Distribution (OOD) Generalization

Strategy

• Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
• Architectures for AI ... Generative AI Stack ... Enterprise Architecture (EA) ... Enterprise Portfolio Management (EPM) ... Architecture and Interior Design
• Exploring Enterprise AI - Insights, Use-Cases, and Best-Practices for Business Leaders | Emerj.com

Determine Best Strategy: There are several steps an enterprise can take to use signals, analyze trends, itemize uncertainties, and develop future scenarios to better understand opportunities and threats within the organization:

1. Identify and analyze signals: including associated risks: Signals are important to identify, but there is always a risk of overlooking or misinterpreting relevant signals. The enterprise should conduct a comprehensive environmental scan and consider the risk of missing critical signals or interpreting them incorrectly.

• While trend analysis can provide valuable insights, there is a risk of assuming that past trends will continue in the future. The enterprise should consider the risks associated with potential changes in the market, customer behavior, or other factors that may impact the trends.
• Itemize uncertainties and associated risks: Itemizing uncertainties is crucial to developing effective contingency plans, but there is a risk of overlooking important uncertainties or underestimating their potential impact.
• The enterprise should consider the risks associated with potential uncertainties and develop contingency plans that are flexible enough to adapt to changing circumstances.

2. Develop future scenarios; Goal: Scenario planning can help the enterprise anticipate potential outcomes, but there is a risk of becoming too focused on a specific scenario and failing to consider other possibilities. The enterprise should consider the risks associated with each scenario and develop strategies that are adaptable to a range of potential outcomes.

3. Plan and perform actions: Developing a plan of action is critical, but there is a risk of implementing actions that do not address the underlying issues or that have unintended consequences. The enterprise should consider the risks associated with each action and develop contingency plans in case the actions do not have the desired impact.

By considering the risks associated with each step, the enterprise can develop a more comprehensive understanding of the opportunities and threats within the organization and increase the probability of best outcomes while minimizing the risks of negative consequences. This helps the enterprise to stay ahead of the competition, adapt to changes in the market, and achieve its strategic objectives while effectively managing risks.

You Exec

How do you develop an Artificial Intelligence Strategy for a business? In this video I explain in very simple terms how a business can develop an AI or machine learning. Here is the link to the AI strategy template If you would like more information on this topic, please feel free to visit my website and sign up for content updates! I write articles every week on various different topics such as Big Data, Artificial Intelligence & Machine Learning.

Amy Webb Launches 2023 Emerging Tech Trend Report | SXSW 2023 Join Amy Webb for the launch of the Future Today Institute's 16th edition of its Tech Trends Report and a deep dive into all the tech trends you'll need to follow in 2023. In this provocative session, futurist Amy Webb, CEO of the Future Today Institute and professor at NYU Stern School of Business, will provide a data-driven analysis for the emerging tech trends that need to be on your radar this year––and she’ll show you scenarios that will change your perspective on the futures. For those who have attended this session at previous SXSW festivals and already know what to expect from this session, you'll be in for an extra-special treat this year. At the end of the session, you will receive a special SXSW-edition of the Tech Trend Report, which is downloaded 1 million times every year.

How to Realize Your AI Strategy? Raj Ramesh Knowing about AI is one thing, but integrating it into the business could be a lot more challenging because there are many factors to consider. Here I explore some of the major things to consider. www.topsigma.com www.linkedin.com/in/rajramesh

Does Your Company Need an AI Strategy? Raj Ramesh Everyone in the business world is talking about AI. Companies are starting to implement AI without understanding where it’d give the business the biggest bang for the buck. How do you prioritize where to implement AI?

Why do you need an AI Framework and an AI Strategy? Raj Ramesh The terms framework and strategy are often confusing. If your organization is ready to use AI in its digital transformation (and it better be because the future of the digital organization will have lots of AI), then it needs a great strategy. Here is discuss how you can think about AI strategy and AI frameworks.

So you want AI for your business. Where do you start? Raj Ramesh AI is an important technology that will fundamentally shift how your business operates. But many companies are scrambling with AI without a focus. What’s a good way to think about it?

Strategy Example: Writing a Joke

• AI Humor Research
  • Witscript | Joe Toplyn

The Three-Part Joke Algorithm:

1. Topic: This is the statement that the joke is based on. It usually involves a common or familiar situation, observation, or fact that the audience can relate to or understand. For example, a possible topic could be “I hate going to the dentist.”
2. Angle: These are words that smoothly connect the topic to the punch line. They usually involve adding some details or context to the topic that make it more interesting or specific, but also hint at the direction of the punch line. For example, an angle using the topic above could be “I hate going to the dentist. Especially when he asks me questions while he’s working on my teeth.”
3. Punch Line: These are words that result in a laugh. They usually involve revealing something unexpected or absurd about the topic or the angle that contradicts or exaggerates them. For example, a punch line using the angle above could be “I hate going to the dentist. Especially when he asks me questions while he’s working on my teeth. Like, ‘Do you floss regularly?’ or ‘How do you feel about polygamy?’”

Take these steps to write a three-part joke:

1. Get a topic: This is the general subject or theme of the joke, such as politics, sports, animals, etc. It helps to narrow down the scope and context of the joke.

• Select two topic handles: These are two specific words or phrases related to the topic that can be used to create a contrast or a connection. For example, if the topic is politics, the topic handles could be "president" and "election".
• Generate associations of the two topic handles: These are words or ideas that come to mind when thinking of the topic handles. They can be positive, negative, neutral, or absurd. For example, some associations of "president" could be "power", "speech", "scandal", etc. Some associations of "election" could be "vote", "campaign", "fraud", etc.

2. Create a punch line: This is the final sentence or phrase of the joke that delivers the surprise or twist. It usually involves combining or contrasting two associations of the topic handles in an unexpected or humorous way. For example, a possible punch line using the associations above could be "The president gave a speech about how he won the election by a landslide...of fraud allegations."

3. Generate an angle between the topic and punch line: This is the intermediate sentence or phrase that sets up the punch line and connects it to the topic. It usually involves introducing one of the topic handles and one of its associations in a way that seems normal or logical at first, but leads to the punch line. For example, an angle using the punch line above could be "It's been a tough week for the president." This introduces the topic handle "president" and its association "scandal", but also creates a contrast with the punch line.

Tactics

• AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
• Data Science ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
• Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
• Development ... Notebooks ... AI Pair Programming ... Codeless ... Hugging Face ... AIOps/MLOps ... AIaaS/MLaaS
• Building Your Environment
• Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
  • ChatGPT Integration
• Predictive Analysis 101 | Ravi Kalakota
• Machine Learning in Real Life | Rebecca Vickery - Towards Data Science
• Machine Learning Algorithms: Which One to Choose for Your Problem | Daniil Korbut
• Automating business processes, Gaining insight through data analysis, Engaging with customers and employees
• Machine Learning Algorithms
• Neural Network Zoo | Fjodor Van Veen
• Insights | McKinsey&Company
• Outline_of_machine_learning | Wikpedia
• The 5 Rules Of Product-Driven Data Science | David Foster - Medium

1. Think backwards
2. Build a data pipeline before a model
3. Deliver actions over accuracy
4. Modularise and abstract
5. Brand the solution

• Downloadable: Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science PDF | Stefan Kojouharov - Being Human

These methods can be employed to plan and execute actions identified in the last step of your AI strategy. organizations can benefit greatly from implementing these methods and utilizing generative AI to plan and perform actions. By effectively managing risks associated with AI initiatives, organizations can achieve better outcomes, stay ahead of the competition, and achieve their strategic objectives in a more efficient and effective manner. By utilizing these techniques, organizations can increase the likelihood of success in their AI initiatives and gain a competitive edge.

• One example is predictive analytics, which can help organizations identify potential risks and outcomes associated with various actions. However, it's important to note that there is a risk of inaccurate predictions. To mitigate this risk, organizations can utilize generative AI to explore different outcomes and develop contingency plans in case predictions do not have the desired impact.

• Another method is simulation modeling, which can help organizations identify potential risks and opportunities. However, there is a risk of relying too heavily on simulated data and making decisions that do not reflect the real world. Generative AI can assist in simulation modeling by generating a wide range of data inputs and outputs, enabling organizations to explore different scenarios and potential outcomes. Organizations should validate simulations with real-world data and use generative AI to develop contingency plans in case simulated results do not have the desired impact.

• Agile methodology is also a valuable tool for organizations, allowing for continuous monitoring and adjustment of actions based on feedback. However, there is a risk of losing sight of overall strategic objectives. Generative AI can assist in agile methodology by generating insights that inform the plan of action and help identify potential risks and opportunities. By incorporating generative AI into agile methodology, organizations can more effectively manage risks and align decisions with strategic objectives.

• A/B testing is another method that can help organizations identify which version of a product or service is more effective. However, there is a risk of making decisions based on incomplete or biased data. Generative AI can assist in A/B testing by generating a wide range of data inputs and outputs, enabling organizations to better understand the impact of each version. By incorporating generative AI into A/B testing, organizations can more effectively manage risks and make decisions based on a more comprehensive understanding of the impact of each version.

• Finally, continuous monitoring and evaluation can help organizations identify potential risks and opportunities in real-time. However, there is a risk of relying too heavily on short-term trends and making decisions based on incomplete data. Generative AI can assist in continuous monitoring and evaluation by generating a wide range of potential scenarios based on historical and real-time data, allowing organizations to better understand the risks and uncertainties associated with different actions. By incorporating generative AI into continuous monitoring and evaluation, organizations can more effectively manage risks and make decisions based on a more comprehensive understanding of the impact of each action.

Application	Kaggle Competition
Translate a business question into a data question
Think about how the model is going to be consumed
Validate assumptions and methods
Consider how a machine learning model can connect to a (existing) tech stack
Determine data sources
Extract Data; SQL queries (sometimes across multiple databases), third-party systems, web scraping, API’s or data from partners	Download some data (probably one or several CSV files)
Transforming and cleaning data; removing erroneous data, outliers and handling missing values	Perhaps do a little cleaning, or chances are the data set may already be clean enough
Exploratory data analysis & feature extraction
Feature engineering (vast range of variables)	Feature engineering (a finite number of variables)
Perform preprocessing such as converting categorical data into numerical data	Perform preprocessing such as converting categorical data into numerical data
Version models, Feature selection, Hyperparameter Tuning, and Web Service Endpoints
Model selection; best model that can be integrated into the existing tech stack with the least amount of engineering	Model selection
Build the model	Build the model
Set up training and deployment pipelines
Train the model	Train the model
Validate the model	Validate the model
Test the model	Test the model
Optimise the model until it is ‘good enough’ considering the business value; perform hyperparameter tuning and compare results	Run the data through a variety of suitable models until you find the best one; perform hyperparameter tuning and compare results
Make experiments reproducible
If new tech stack…
- Review prior art and available libraries, services, scafolding tools
- Implement and test tech stack
Deploy the model
Monitor the model performance in production
Set up monitoring alert systems
Retrain when/if necessary
Mature to automatically retrain and redeploy models

The 7 steps of machine learning How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning. The 7 Steps of Machine Learning article: https://goo.gl/XEo6i2

Nuts and Bolts of Applying Deep Learning (Andrew Ng) The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do).

Strategies for Implementing AI and Machine Learning Techniques Speaker: Daniel Protz, Founder and CEO, FlavorWiki. How can traditional F&B companies adopt new approaches while maintaining organisational support, comparability of data and continuity of product understanding? In the session Daniel outlined three change management approaches to stimulate teams, win organisational buy-in and secure budget for non-traditional tools using Big Data, Machine Learning and AI. What you will learn about: Use AI and Machine Learning to enhance existing systems and practices, Gain support for the use of such methods within their teams, Explain to management how investment in emerging tools can drive project ROI and increase competitiveness. Organised by IFST's Sensory Science Special Interest Group.

Your choice of Deep Net - Ep. 4 (Deep Learning SIMPLIFIED) Deep Nets come in a large variety of structures and sizes, so how do you decide which kind to use? The answer depends on whether you are classifying objects or extracting features. Let’s take a look at your choices. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/

Lessons learned from 100 deep learning models Summary of tips for getting higher accuracy on your CNN deep nets.

CS231n Lecture 12 - Deep Learning libraries Overview of Caffe/Torch/Theano/TensorFlow

What is a Deep Learning Library? - Ep. 16 (Deep Learning SIMPLIFIED) Deep Learning libraries provide pre-written, professional-quality code that you can use for your own projects. Given the complexity of deep net applications, reusing code is a wise choice for a developer. A library is a set of functions and modules that you can call through your own programs. Library code is typically created by highly-qualified software teams, and many libraries bring together large communities that support and extend the codebase. If you’re a developer, you’ve almost certainly used a library at one point in time. For a commercial-grade deep learning application, the best libraries are deeplearning4j, Torch, and Caffe. The library Theano is suited for educational, research, and scientific projects. Other available libraries include Deepmat and Neon.

Comparing machine learning models in scikit-learn We've learned how to train different machine learning models and make predictions, but how do we actually choose which model is "best"? We'll cover the train/test split process for model evaluation, which allows you to avoid "overfitting" by estimating how well a model is likely to perform on new data. We'll use that same process to locate optimal tuning parameters for a K-Nearest Neighbors (KNN) model, and then we'll re-train our model so that it's ready to make real predictions.

Approaching (almost) Any Machine Learning Problem | by Abhishek Thakur | Kaggle Days Dubai | Kaggle Abhishek Thakur is the chief data scientist at https://boost.ai building state-of-the-art chatbots primarily for banking and insurance industries. His passion lies in solving difficult world problems through data science. He is the co-organizer of the Berlin Machine Learning Meetup and not long ago was ranked no. 3 worldwide on the Data Science Platform Kaggle. He is a Competitions and Discussions Grandmaster. Abhishek did his Bachelors in Electronics Engineering from India and moved to Germany for pursuing MSc from University of Bonn, Germany with a focus on image processing and computer vision. He dropped out of PhD in 2015 and since then has been working in industries. Kaggle Days Dubai was held May 30 - April 1 2019 as a part of AI Everything. Participants came to meet, learn and code with Kaggle Grandmasters, and compete in a full-day offline competition. This edition is presented by LogicAI with sponsorship from Kaggle and Google Cloud. Kaggle Days are a global series of offline events for seasoned data scientists and Kagglers created by LogicAI and Kaggle.

Building Robust Machine Learning Models Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Real-world machine learning requires hard work, discipline and rigor. Development of robust models requires due diligence during data acquisition phase and an obsession with data quality. Feature engineering, choice of evaluation metrics and an understanding of the model bias/variance trade-off is often more important than the choice of tools. Experienced machine learning engineers spend most of their time dealing with data-related issues, model evaluation and parameter tuning while spending only a fraction of their time in actual model building. This is the 80/20 rule. Unlike most talks these days, this talk is not about deep learning. We will ignore the hype and strictly focus on fundamentals of building robust machine learning models.

Guide to choose right deep Learning framework for your AI project Rishikesh As world rolling around Artificial Intelligence (AI), demand for the AI-based product seen exponential growth, so the AI research. Deep learning algorithms and techniques are widely used for research and development of these products. Good news is that year by year Deep Learning has seen its glory in the release of many open source frameworks which ease the pain to develop and implement these algorithms. As there are many deep learning frameworks out there and it can lead to confusion as to which one is better for your task. And choosing a deep learning framework for an AI project is as important as choosing a programming language to code product, Data science project coupled with the right deep learning framework has truly amplified the overall productivity. In this talk, I will discuss the common points which help developers to understand which framework will be the perfect fit for solving given business challenges. Also, we will look into some of the most widely used frameworks and comparing with standard benchmarks. Following deep learning/machine learning frameworks will be discussed: 1. Tensorflow, 2. PyTorch, 3. **Chainer** and/or **MXNET** Key Highlight of this talk: define key points to judge any deep learning framework. hardware dependencies. anatomy of widely used open source frameworks. comparison of above-mentioned frameworks as per defined key points. Who is the audience? Anyone who inspired to code deep learning algorithms. pyconza2018 python

How to Realize Your AI Strategy? Raj Ramesh Knowing about AI is one thing, but integrating it into the business could be a lot more challenging because there are many factors to consider. Here I explore some of the major things to consider. www.topsigma.com www.linkedin.com/in/rajramesh

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Artificial Intelligence Center of Excellence (AI CoE)

• How to Set Up an AI Center of Excellence - Harvard Business Review
• How To Create an Enterprise 'AI Center of Excellence' | Pure AI
• Artificial Intelligence Center of Excellence (AICoE) - WordPress | UD
• Four Steps For Building An AI Center Of Excellence | Forbes

Establishing an AI Center of Excellence is a strategic move that can significantly enhance your organization's AI capabilities. Remember that AI adoption is an ongoing journey. Continuously monitor progress, iterate on your strategy, and adapt to changing technology and business landscapes. By following this plan, your organization can establish a robust AI CoE that drives innovation, efficiency, and value across the board. Here's an outline to guide your organization in adopting AI effectively and maximizing its value:

• Create the AI Vision:
  • Define a clear and compelling vision for AI adoption within your organization. Understand how AI aligns with your business goals, mission, and long-term strategy.
  • Engage key stakeholders, including executives, business leaders, and technical experts, to ensure buy-in and alignment.
• Identify Use Cases:
  • Conduct a thorough assessment of your organization’s processes, pain points, and opportunities.
  • Identify specific use cases where AI can add value. Prioritize use cases based on their potential impact, feasibility, and alignment with strategic objectives.
• Determine Ambition Level:
  • Define the level of ambition for your AI initiatives. Consider factors such as budget, resources, and risk tolerance.
  • Decide whether you want to start with small-scale pilots or aim for broader, organization-wide AI adoption.
• Create a Data Architecture:
  • Establish a robust data infrastructure to support AI initiatives.
  • Ensure data quality, security, and accessibility. Consider data governance, privacy, and compliance requirements.
  • Explore cloud-based solutions for scalability and flexibility.
• Manage External Partnerships:
  • Collaborate with external partners, such as AI vendors, research institutions, and industry experts
  • Leverage their expertise, tools, and technologies to accelerate AI development
  • Establish clear communication channels and expectations
• Identify AI Champions:
  • Identify individuals within your organization who are passionate about AI and can drive its adoption.
  • These champions can be from various departments—data science, IT, business units, etc.
  • Empower them to lead AI initiatives, advocate for AI adoption, and share best practices.
• Use AI Technology:
  • Explore AI tools, frameworks, and platforms
  • Choose technologies that align with your organization’s needs and goals
  • Start with a small-scale project or proof of concept
  • Validate AI concepts and demonstrate feasibility
• Share Success Stories:
  • Communicate wins and achievements related to AI projects - perhaps sharing via a wiki
  • Showcase how AI has improved processes, efficiency, customer experiences, or revenue.
  • Use success stories to build momentum and encourage broader adoption.
• Plan for Change Management:
  • Communicate the AI roadmap to stakeholders.
  • Address concerns, manage expectations, and ensure smooth implementation.

Business Case

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• How To Build A Strong Business Case For AI | Insights Team - Forbes
• Making a persuasive business case for bigger AI investment | Mike Miliard - Healthcare IT News HIMSS
• Building a business case for an AI or machine learning project using first principles, the 80/20 principle and more | Bhalchander Vishwanath - Ideas2IT
• Build the AI Business Case | Whit Andrews - Gartner

Artificial Intelligence: Building the Business Case for AI (CXOTalk #246) Artificial intelligence can make companies dramatically more efficient, but investing in the technology can come with risks and complications. Tiger Tyagarajan, CEO of Genpact, tells Michael Krigsman of CXOTalk about the best strategies for buying AI to improve your business.

Building a Business Case for your Machine Learning Idea This presentation will discuss building a business model for your machine learning idea. Our presenter, Neeti Gupta, will provide a 10-step checklist with examples for the audience to build their own business model.

Business Strategy/Consulting

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• Strategy & Tactics ... Project Management ... Best Practices ... Checklists ... Project Check-in ... Evaluation ... Measures
• Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
• Architectures for AI ... Enterprise Architecture (EA) ... Enterprise Portfolio Management (EPM) ... Architecture and Interior Design
• Generative AI ... Conversational AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's Bing ... You ...Google's Bard ... Baidu's Ernie
• Video/Image ... Vision ... Enhancement ... Fake ... Reconstruction ... Colorize ... Occlusions ... Predict image ... Image/Video Transfer Learning
• Client Engagement .. Afiniti
• AI May Soon Replace Even the Most Elite Consultants | Barry Libert and Megan Beck - Harvard Business Review
• Can Artificial Intelligence Generate Corporate Strategy? | Daniel Shapiro - Forbes
• Rainbird enables enterprises to build systems with human-like decision making abilities
• AI Consulting: In-depth Guide with Top AI Consultants of 2020 | AI Multiple
• Crafting an AI strategy for government leaders | Deloitte Insights

How to Apply AI in Business Raj Ramesh AI is a cool technology, but how do you use it effectively in your business operations. Solving the AI problem is only a small part of the whole solution, and you have to consider other things as well. www.topsigma.com www.linkedin.com/in/rajramesh In this video, I walk through an example of using an AI system to automate a parking lot business operations

How to Insert AI into business processes and what are the critical factors to consider? Raj Ramesh Today many businesses are trying to transform to adapt to the new realities of the world. Future business models are often dependent on available technology. For example, the Uber business model is made possible because of smartphones. Technology will be a major component of those future state of any business. One of the main technologies is artificial intelligence (AI) and businesses that master AI will establish themselves in a leading and competitive position. Many studies have indicated that the faster the business integrates AI, the faster it will move forward. This means that if you are late to the game, it is almost impossible to catch up. So the question to ask is how do you establish that lead? Having gone through large scale multi-billion dollar transformations myself, and also being deeply familiar with AI, I offer some secrets on successful AI-based transformations. www.topsigma.com www.linkedin.com/in/rajramesh

Meet Sheldon, the Future of Consulting By Emmanuel Jusserand, Partner at Accenture Digital Strategy

How AI is Changing Consulting Harvard Business Review Facebook Live Presentation

AI for Business: How Should We Frame It? Boston Consulting Group BCG Partner and Managing Director Philipp Gerbert presents on the benefits of AI to a group of senior leaders at MIT. Welcome to the official YouTube channel for The Boston Consulting Group. Since 1963, BCG’s experts have been helping businesses, governments, and non-profit organizations build lasting advantage. As a leading management consulting firm, BCG empowers clients to make the changes needed to seize the competitive advantage and win. The independent spirit handed down from Bruce Henderson, BCG's founder—always challenging the status quo—has given the firm the courage to look beyond the obvious in pursuit of lasting solutions for our clients. Subscribe to BCG’s YouTube channel: https://goo.gl/hsFsVT Visit us at https://www.bcg.com

How do you develop an Artificial Intelligence Strategy for a business? Bernard Marr In this video I explain in very simple terms how a business can develop an AI or machine learning. If you would like more information on this topic, please feel free to visit my website and sign up for content updates! I write articles every week on various different topics such as Big Data, Artificial Intelligence & Machine Learning.

The Future of Consulting Recently did a Q&A jam session with CMC-Manitoba on the future of management consulting (one of my favourite topics). Like most industries, we are also being disrupted from all sides --- the proliferation of the gig economy, automation, start-ups, the commoditization of information, etc. One of the ways to remain competitive is to hire and train "next-generation consultants". Biography: Shawn Kanungo is a strategist and keynote speaker who operates at the intersection of creativity, business and technology. He has been recognized nationally and globally for his work in the innovation space after 12 years working at Deloitte. Shawn’s mandate at the firm was to help corporate executives to better understand and plan for the opportunities and threats associated with disruptive innovation. Now as General Partner of Queen & Rook Capital, he is focused on applying exponential technologies and new business models to mature businesses. Shawn is a practitioner who has worked hand-to-hand with hundreds of organizations on their journey to digital transformation. He has adopted the concepts of behavioural economics, user-centered design, crowdsourcing, artificial intelligence, drones and film to help create world class client experiences. His work and interviews have been featured in Forbes, The Globe & Mail, The Guardian, CBC and CTV. In 2016, he was recognized as Avenue Magazine's Top 40 Under 40. He has spoken at TEDx in 2017, and named to Inc's 100 Most Innovative Leadership Speakers in 2018. His podcast, The Remix with Shawn & Wang, can be currently found on iTunes, Google Play and Stitcher. Website: https://www.shawnkanungo.com/

How AI revolutionises business strategy For centuries, the decisions made in a company were the responsibility of the top managers. But when firms harness AI and big data, algorithms can make many more decisions in the same time, and probably better ones. Kenneth Cukier explores how this affects the ways that companies are organized and how they compete and set strategy (as opposed to just execution) as he outlines what is happening now and what to expect in the future.