Not every SaaS can make use of machine learning right now. Mikhail Naumov, Co-founder & CSO of venture-backed, AI company DigitalGenius, succinctly explained in Forbes what a business needs to start using machine learning today. First, you need large volumes of historical data. You can train a puppy with a bag of treats. To train a machine learning algorithm, you need reams and reams of human-corrected data.
The other thing you need is a business case for machine learning. Building an algorithm and training it isn’t cheap. So you need a plan for making it pay for itself before you start. Will your machine learning algorithm find you ways to cut costs or ways to provide more value? For example, can your bot reduce your customer service department’s average time to resolution? Or could it replace human insurance assessments?
On the “create more value” side, could AI help upsell your customers? Or could it make your marketing more effective at generating leads?
Leveraging AI techniques, companies can move customer relationships beyond superficial to deeper, more meaningful interactions and experiences that engage customers at unprecedented and hyper-personalized levels such as proactively delivering an ad to a consumer on a smartphone that is of high interest to them.”
A great example of AI-powered personalization is Amazon’s “Just Ask” feature on Echo. The Echo is the device powered by Amazon’s bot, Alexa. Because Alexa knows your buying history, delivery address, and shipping and payment preferences she can offer you daily promotions and special deals based on your needs. Customer Service Speaker and Author Richard Shapiro calls the “Just Ask” feature, “a game changer.”
At CES this year, voice-controlled AI assistants were “everywhere,” according toJamie Condliffe, Associate Editor of news and commentary for MIT Technology Review. Consumers love devices with built-in speech-powered bots, and Condliffe writes that companies in 2017 are trying to put conversational interfaces “into as many pieces of hardware as possible.”
Salesforce Einstein takes all your CRM data to make predictions about what’s likely to happen and recommendations on what you should do next. Naumov offers the example of Einstein using email, calendar, and social data to send your email during the 20-minute time slot when your prospect is statistically most likely to open an email from you and respond positively.
Japanese company Fanuc sells robots to factories that can learn new skills on their own. In eight hours a Fanuc robot can learn how to complete a new task with 90 percent accuracy. Fanuc is the world’s largest industrial robot producer, according to MIT Technology Review. And by partnering with a Japanese machine-learning company, it’s been able to produce robots that come with artificial intelligence powered by machine learning algorithms. And you can even download apps into its robots.
But robots aren’t content to just take factory jobs. Data science is in AI’s crosshairs too. A Los Angeles-based startup called Bottlenose is aiming at automating data science, As investor Nova Spivack explained to the Wall Street Journal, this company is meta as heck. Because if you can use AI to automate data science, suddenly AI becomes much cheaper.
Every year, vehicle crashes kill almost 1.3 million people, an average of 3,287 preventable deaths per day. Young adults between the ages of 15 and 44 make up more than half of these deaths. Vehicle crashes injure an additional 20-50 million.
The Toyota Research Institute is using artificial intelligence to make automobiles “safer, more affordable, and more accessible to everyone, regardless of age or ability.” But machine learning and deep neural networks can do more than create self-driving vehicles. TRI is also working on robot assistants to help the elderly and differently abled stay healthy, and at home, longer. And is working to develop stronger, thinner, lighter, more flexible materials.