“Artificial Intelligence/Machine Learning field is getting a lot of attention right now, and knowing where to start can be a little difficult.
I’ve been dabbling in this field, so I thought of curating the best resources in one place. All of these are curated based on if it’s an inspiring read or a valuable resource. I hope this curated list help you get started on what you need to know about AI/Machine Learning on a technical level.”
“The primary encumbrance to this ensuring that AI systems have a quality data set to draw from. The report points to the importance of a mature and well-developed information architecture in order to effectively create and leverage AI in the enterprise.
Naturally, developing an information architecture and the AI systems to leverage it will require human expertise. Forrester cites a talent shortage in this field, indicating that “Two-thirds of AI decision makers struggle with finding and acquiring AI talent, and 83% struggle with retention,” adding that the shortages extend beyond hard technology and science fields, as ‘firms need industry, social, legal, customer experience, and operational expertise to train, manage, and trust AI systems.'”
“Google debuts AI Hub to help enterprises get their artificial intelligence projects off the ground faster, as its bid to make the technology accessible to everyone gathers pace
The blog post further reveals that Google now has more than 15,000 organisations paying to use its machine learning services across a range of industries, including manufacturing, e-commerce and healthcare.
“Our goal is to put AI in reach of all businesses, but doing that means lowering the barriers to entry. That’s why we build all our AI offerings with three ideas in mind: make them simple, so more enterprises can adopt them; make them useful to the widest range of organisations; and make them fast, so businesses can iterate and succeed more quickly,” said Mehanna.”
“In fact, Domino’s has traditionally been an early adopter of technology, integrating the latest gadgets into their marketing strategy. Hungry customers have been able to track their pizzas for a decade now. The brand introduced “Dom,” an AI pizza ordering assistant in 2014 and earlier this year, began testing it as a replacement for human phone orders.
Other efforts have included a Facebook chatbot and its AnyWare initiative, which allows users to order a pizza just by texting a pizza emoji.”
“Our [technique] can be immensely helpful, as out of 284,807 samples we can safely rule out 139,220 [transactions],” they wrote.
If you’ve purchased or sold something on eBay recently, you might have encountered the system in action. The researchers coyly noted that it was successful in picking out fraudulent transactions in data from an “ecommerce platform”
“According to a recent Gartner study, 30% of digital commerce revenue growth will be attributable to artificial intelligence technologies by 2020. With the growing demand for breakthrough technology solutions to improve the shopping experience, the alliance brings together two leading AI innovators to create new opportunities for retail and beyond. Clarifai’s visual AI provides deep insights into the visual similarity of products in a catalog. These algorithms and insights will be available to RichRelevance Xen AI customers to enable real-time personalization strategies based on image similarity and visual concepts, blending with each customer’s history, preferences, context and real-time behavior. “
“The fourth edition of The Maddies Awards 2018 held on October 31 in Mumbai also hosted Screenage, a mobile marketing conference. The first panel discussion held at the conference spoke on the topic: How AI is going to unleash a new wave of marketing revolution, how enterprises need to adapt for being competitive?
Bansal added to demystifying AI, ‘As marketers, there is so much data generated every day, it is important for us to understand the importance of AI. Today there are 6 or 8 billion connected devices /objects in the world, in less than 10 year there will be 27 billion connected devices. From that perspective, everything is generating data. So for us to harness that data to make intelligent decision will be super critical. We have to prepare ourselves for a future when AI will literally be like electricity around us, it will be so ubiquitous and omnipresent.'”
“Here are the steps you should follow to build out an AI center of excellence and begin to strengthen your brand’s AI muscle.
- Suppress the urge to post an opening for an AI expert to take this all on.And don’t just punt this over to your CIO or CTO.
- Start with a strategy. Winning strategies take a practical approach and start small.
- Find narrow use cases that are relevant to the overall corporate strategy. The most successful AI use cases live at the intersection of business objectives, data differentiation, and readily available artificial intelligence models.
- Leverage third-party expertise on a project or part-time basis which can objectively help you build your strategy. Starting with a hire-first strategy may take 6 to 9 months to complete your AI vision. A tiger team of experts can help you build your AI roadmap in 6 weeks.
- Once you build the roadmap, ID narrow use cases that solve the biggest problems with the quickest implementations scenarios. Build or implement an MVP version of the solution. An MVP (minimum viable product) should provide the opportunity to deploy your solution quickly and also provide an interface for training employees on the AI model to confidently execute its task.
- Once confidence is achieved (both algorithmically and institutionally), build toward the full-scale solution.”
“Let’s examine the 2019 strategic predictions. I’ll look at the 2019 technology trends next time.
Good, Silly and Weird Predictions
On October 16, Gartner published its “top strategic predictions for 2019 and beyond.” It leads with the prediction that “AI skills don’t scale.” I love the way they describe the challenge: “through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization.” This may be the most important observation-turned-prediction on the list. There’s no question there’s a shortage of AI talent. Demand is way outstripping supply, which means scalability, interoperability, standardization and best practices are in danger of atrophy. The only beneficiaries of the current shortage are the few with genuine skills and competencies whose compensation will continue to skyrocket. It’s time for everyone to invest in the education and training necessary to exploit the potential of AI, especially colleges and universities. Gartner got this one right.”