Artificial intelligence—essentially, the science of making computers smart—is being added to almost every aspect of our lives; the transportation industry, factories, supply chain, healthcare, government – right down to the new additions to the family and soon to be robots – Alexa, Google Assistant, Siri, and Cortana. A subcategory of AI machine learning is known as deep learning. Deep learning enables many practical applications of machine learning, breaking down tasks in a way which makes machine assists not only more possible, but more likely. Although a full 85 percent of executives currently believe artificial intelligence technologies and deep learning could help their company gain a significant competitive advantage, only about 10 percent are actually using the technology.

How is AI Impacting Transportation, Factories and the Medical Industry

In the transportation industry, artificial intelligence can assist in issues where predictable patterns are affected by such things as traffic, accidents or human errors. Specifically, AI will improve public safety, assist corporate decision-making, enable self-driving cars, and predict traffic patterns which significantly affects the transport business. According to a P&S Market Research Report, global adoption of artificial intelligence in the transportation market is projected to reach $3.5 billion by 2023, primarily from autonomous vehicles and the focus on reducing transportation operating costs.

As far as factories and artificial intelligence goes, robots have occupied the factory floor for some time, however today, factory robots no longer only perform monotonous, mechanical tasks, rather factories have been linked with AI virtual reality. Artificial intelligence is crucial to the manufacturing world from real-time equipment maintenance to a smart supply chain, the creation of new business models and virtual design. While AI machines can already see things a human’s eye could not, the next step of sending these images to a human being to make necessary judgments and corrections will soon also be handed over to a machine.

In the medical world, artificial intelligence is now able to perform clinical diagnoses, and even suggest treatments. Telemedicine, the use of AI to give clinical healthcare from any distance via the Internet is a goal of medical artificial intelligence which will make healthcare more accessible and affordable for all. AI will potentially be used in the discovery of drugs, patient risk analysis, diagnostics, hospital management and even patient lifestyle monitoring.

In each of these areas, AI and machine learning will significantly change how businesses target consumers. You see it already with the ads that follow you around the internet. Imagine a time when Alexa, Google Assistant, Siri, or Cortana will know your shopping habits by scanning QR codes on the products you bring home and then being able to tell you which grocery store will be the best on your pocket book not only for the regular items on your shopping list but also due to gas mileage, current traffic conditions and weather.

Amazon is already using AI to simplify the retail process in their Go stores in Seattle and Chicago. Using computer vision, deep learning algorithms and sensor fusion, much like you’d find in self-driving cars, Amazon is changing the face of retail. Overtime this technology will make its way into your home.

AI and Deep Learning’s Significance in Creating Personalized Marketing Campaigns

Increased effectiveness of marketing campaigns—Human beings are not always able to recognize connections and patterns in the same way a machine can, making it more difficult for humans to predict user behaviors and purchase trends. The artificial intelligence implemented across the industries can now use machine learning to analyze data, then use that data to target specific audiences.

● Improved customer experiences—Chatbots are almost a staple in current business interactions. These chatbots answer questions, troubleshoot problems, and even assist customers in choosing products based on their history and preferences.

● Allowing Key Performance Indicators and analytics to measure customer growth goals and revenue goals more fully and accurately.

● Dynamic pricing strategies—Dynamic pricing strategies allow businesses to respond more quickly to the fluctuations of supply and demand. As an example, eBay and Amazon currently “tweak” prices each day, using complex algorithms which assess customer loyalty level, then change the price accordingly.

● Targeted marketing—To this point, targeting requires that demographics and content be analyzed, resulting in ad placement. “Old” targeting methods focused almost exclusively on demographics, while artificial intelligence targets customers based on intent—as in, what is a specific person in the market for, and, based on that, what will he or she do next.

● Content generated by artificial intelligence—This is a relatively new area for artificial intelligence and machine learning, and, at present, there are only a few companies which offer basic content-generation tools which quickly change numeric data into coherent content.

● Content and campaign personalization—Both Amazon and Netflix use algorithms which consider specific customer behaviors, re-engaging the customers with discounts and offers. Machine learning allows advertising campaigns to be customized differently for every single customer segment. This personalization, in turn, garners customer loyalty.

● Combining long-term interests with immediate wants and needs to increase marketing results.

● Monitoring of the media presence—Artificial intelligence can help companies track and measure brand exposure, whether on social media, television, events or commercials.

● An increasingly-educated marketing process—Although marketing has always included the use of data, many ad campaigns continue to involve educated guessing on the part of marketers. The growth in artificial intelligence and machine learning allows an even more data-driven marketing campaign which takes into consideration the habits, desires, activities and purchasing preferences of each individual.

Where Artificial Intelligence and Deep Learning Are Taking Us

Artificial intelligence and machine learning include Amazon’s product recommendation feature, and Google’s new smart-display campaigns. Marketing is optimized by a determination of which sales offers, programs and incentives are presented to which customer. Amazon recently announced the formation of a new healthcare company which would use technology to provide high-quality healthcare to patients in a simpler manner and at a more reasonable cost. Amazon is modeling its AI decisions—related to this healthcare company—after other tech giants which have been experimenting with employee healthcare software in China for a number of years.

Google has been a champion of deep learning for many years, because when deep learning is put to work, data scientists are able to crack difficult, challenging cases such as natural language generation and speech and image recognition. In 2012, Google announced its neural network—called the Google Brain—which was designed to simulate human cognitive processes, and since that time the company has continued to be on the cutting edge of artificial intelligence technology.

The Deep Learning Summit and the AI for Government Summit in Toronto that just occurred (Nov 2018) featured an array of incredible speakers, companies and unique learning opportunities. An amazing couple of days of being surrounded by extraordinary thinkers.

A few takeaways from the Toronto Deep Learning Summit:

Q: What is something you wish you knew when you were younger?

A: In 1986 I wish I knew this stuff was going to work! There were so many people who said it was nonsense. I wish I could have told them, you wait, but that would have been a stupid thing to say, because it seemed impossible. What I wish I knew now, is whether the brain uses back propagation!

Geoffrey Hinton, Professor, University of Toronto

When you’re on a plane and you hear ‘there’s a technical difficulty’, the pilot will be having a more technical discourse, and the ground staff may be having an even more nuanced discussion. Does this mean that the interpretability should be the same across all parties? Probably not, we don’t want to panic the passengers with an overly technical explanation. – Sara Hooker, Google Brain

From Raw Data to Actionable Clinical Insights, National Healthcare, Microsoft Canada

The end-to-end analysis of large-scale genomics data is complex and expensive. The workshop explained how Microsoft and the Databricks Unified Analytics Platform for Genomics simplify the process of turning raw sequencing data into actionable insights. “When you identify what part of the genome is responsible, you can come up with a treatment or prevention.”

Challenges & Opportunities of Investing in AI – VC Panel, Q&A & Networking Session

“The most difficult part of fundraising for a start-up is finding that investor to put the first cheque into it” – Saroop Bharwani

If you have an opportunity to go to one of the AI – Deep Learning events – don’t miss it. Go. It’s like spending the weekend watching all your favourite movies/shows on the SyFy Channel but a million times better; a real glimpse of things to come. https://www.re-work.co/events

Can you see it now? It’s only a matter of time before your two year old starts playing dress-up with Alexa and family photos start to include your in-home robot. I’m calling it now. A whole new industry of costumes and accessories for our in-home robot companions.

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