The internet is always speculating and wondering about what’s to come, in terms of access modes, once mobile and screens have ceased to be the latest shiny new thing.
The theory is that artificial intelligence (AI) is going to be the next evolution; and precisely because of that prediction about AI (plus its other uses), we witnessed an unprecedented technology gold rush in 2017.
The craze about AI had people investing millions of dollars into new products and services, and hand-wringing skeptics forming think tanks to postulate about the end of the world. But, for all of the speculation, what did AI actually do in 2017? What’s it doing now? And, what’s it poised to do in the coming year?
First, AI broke through the legitimacy barrier.
AI had its first victory as an up-and-coming technology when it broke through a crucial legitimacy barrier, proving to everyone that it is not only viable but also a competitive advantage when deployed well.
A report by PwC estimated that by 2030, AI could contribute upwards of $15 trillion to the global economy – more than the outputs of China and India today, combined. PwC’s statistic is one of many that suggest that our future with this technology is almost unimaginable. AI will destroy and create jobs, invent new industries, accelerate innovation to a new level and fundamentally change the way business is done across the board.
En route to that point, there are questions being posed: How does AI actually create value? What applications of the technology have worked so well that we believe it will change the future in such radical terms?
Interestingly, applications for sophisticated tasks like smoothing out supply chains or detecting risks are still maturing. What AI already does well and what we can expect to see a lot more of in the coming years is helping humans to interface with increasingly complex computers.
Sean Nolan, founder and CEO of Blink, weighed in on these issues during a chat we had the other day.“Humans in the workplace experience ‘technology fatigue’ because the computer they interact with all day is robotic — oddly organized, clunky, impersonal, and slow,” Nolan told me. “The reality is that computers are so powerful and store so much information, and we need them to do so much, that our ability to utilize them with just a mouse and a keyboard is becoming insufficient.
“AI comes along and opens up the possibility of creating a new interface between the complex, impersonal nature of the computer, and the intelligent, complex nature of its human operator.”
Next, AI is poised to take on immensely complex tasks.
By utilizing simple AI applications, like chatbots and micro applications, immensely complex tasks will — one day soon — be able to be reduced to simple commands. For example, instead of spending an hour searching a company’s servers, shared folders and cloud drives for every relevant document pertaining to a specific task, you’ll be able to simply ask a smartbot to do it for you. The task will be completed in seconds, saving valuable time — which PwC estimates will add up to $6.6 trillion in added efficiency by 2030.
While the benefits of added human productivity carry an obvious financial upside for the market, reducing AI to the role of human-computer mediator seems like a demotion for a technology that is supposed to change the world. This view does not properly account for the magnitude of that accomplishment. In fact, AI’s role as a mediator between humans and technology is perhaps its greatest triumph.
The myth that humans use only 10 percent of their brain power has been soundly debunked, but we know for a fact that we use only a fraction of our computers’ potential at any given moment. Afterall, our interactions with them are analog in nature; and, as a result, fall far short of achieving maximum potential.
AI’s untapped potential
AI as a user interface opens up vast untapped potential. This year, it’s chatbots and voice recognition. In 10 years it will be some version of Elon Musk’s Neuralink (one of his new companies, which is working on linking the human brain to a computer).
Today — right now — there are available a number of tools to help you interface with customers and users by implementing AI. They include:
1. Chatbots. Chatbots can provide the additional layer between you and your consumers to answer their questions, and to interact with potential or current customers.
2. Website builders. There are now new platforms like Grid, which use AI to build your entire website.
3. Smartbots. Smartbots are another form of machine, which can be used internally, like a personal assistant, to handle tasks like scheduling your calendar. This is how X technology works.
“What businesses need to know about AI is that the application they need to use it for first is workforce productivity,” said Nolan. “This single competitive advantage will determine which companies survive the first round of AI and which ones do not. Thinking about AI as UI [user interface] and a means to access the untapped potential of our computers is what we can expect to see in 2018.”
Meanwhile, the urgency to adopt AI is very real. Companies that successfully incorporate it as UI in their businesses will reap enormous dividends while their competitors will struggle to modernize. In a new report titled AI is the New UI, Accenture made the following observation, summing up my point perfectly:
“Getting started can be as simple as using AI to bring more human-like interactions into existing interfaces,” the report stated. “But if businesses want to do more than just keep pace, there’s no time to waste . . . The early adopters are already pulling ahead, but many of the necessary tools are openly being shared. The question to answer is simple: What could a company accomplish if every interaction with technology was an intelligent one?”
How AI Can Make Customer Service More Efficient
Humanity has come a long way in teaching technology how to appear more human. Now, technology is teaching itself.
4 min read
Opinions expressed by Entrepreneur contributors are their own.
With the rise of the artificial neural network, which mirrors the interconnected nodes of the human brain, artificial intelligence is ready to graduate to a new level.
Artificial neural networks (ANN) are programmed to be cognizant of patterns. ANN can read human speech and synthesize the use of specific words to identify subtle meanings in human communication. The implications are particularly impressive once processing speed is taken into account.
The (until now) uniquely human ability to understand the complexities of language, multiplied by the speed of computational efficiency, results in a very effective response program.
I know, I know: Your mind is blown; (yes, AI can now write puns like that). But, on a practical note, did you know that AI can also improve customer service?
AI’s benefits for customer service
“When we give our machine-learning algorithm access to historical customer service data, it begins to identify patterns and learn in a human-like way,” Mikhail Naumov told me. He’s co-founder and president of DigitalGenius (a frontrunner in the AI and customer service industry), and we were speaking about the future of AI. “What is created by this process,” Naumov said, “is an AI model that is trained on a company’s specific customer-service data-set.
“This intelligence generates automated-response suggestions to customer queries and gives human customer-service professionals a partner to help handle a growing volume of requests.”
The implications for the customer service industry are profound. Data can be fed to a machine-learning program, which creates the neural network, or the “intelligence” behind AI. That intelligence helps humans to better understand customers and take care of their needs with greater speed and precision.
Industry experts agree that intelligence-backed digital assistants represent the change that’s needed for the customer-service sector. As Dan Miller, the founder of Opus Research, commented in Medium: “The future of personalized customer experience is inevitably tied to ‘Intelligent assistance.'”
The benefit for entrepreneurs
Communication is one of the top factors in quality customer service, a fact reflected by this SurveyMonkey study. Companies want to take advantage of software that will give them a platform to address the needs of their clients more efficiently. This is especially true now that customer service has migrated to texting, where mass communications can become bottlenecked. Support agents cannot process the workload.
But, alternately, instead of a handful of people working to respond to an entire client base, there are AI programs that can filter communications and suggest appropriate responses, cutting down the time it takes for those agents to address inquiries.
The evolution of AI
All of this is magnified by the fact that these systems are continuously improving, learning in real time from collected data. Like the ideal employee, because of deep learning algorithms, they just keep getting better at their job. AI would be nowhere without ANN.
“The deep learning methodology allows companies to unlock value from their historical customer service data,” Naumov explained. “By scouring through mountains of historical data and watching how human customer-service representatives responded to thousands of different queries, deep learning can create the intelligence necessary for the AI to be useful.
“In customer service,” Naumov continued, “that means it can detect sentiment, urgency, type of request, details about the case and so on. It can also recommend answers to agents, saving them valuable time. [These things] help companies scale their contact center while responding to a growing volume of requests.”
Current customer-service departments are bogged down by requests that result in an hour or longer queue times for customers. According to Alexandre Lebrun of Facebook’s artificial intelligence division, quoted by Business Insider, “The better we get [at artificial intelligence], the less time you spend talking to customer service. It’s a gain for companies, but it’s also a gain for personal life.”
Many customer service requests are repetitive and easily could be handled by an AI-based response system. Indeed, data and the neural network will make AI the best coworker the support desk has seen yet.
Too Much of Today's AI Is a Novelty Without a Clear Plan to Make Money
As with any business, it’s time to start listening to customers.
6 min read
Opinions expressed by Entrepreneur contributors are their own.
The 2018 artificial intelligence landscape looks an awful lot like the Sharper Image catalog. It’s chock full of products that were built merely because we can build them, and because they’re marketable.
Much like these products, too much of the AI on the market today is disposable novelty technology. Nobody conducted market research to determine the total addressable market for bacon toasters. They didn’t have focus groups with likely customers. They built a novelty that was good for a chuckle and had just enough utility to convince a few people to part ways with a small amount of cash and give it a spin. If that doesn’t sound like a lot of the AI for sale these days, I don’t know what does.
In a half-hearted defense of the industry, AI experts like to remind us that “it’s still early.” Others explain that the first wave of enterprise AI is “doomed to fail,” and somehow that’s both preordained and acceptable. Given it’s well-understood power and potential impact on society, shouldn’t AI be held to a higher standard?
As a result, smart customers are asking: Why is there so much hedging and so little accountability in AI?
Researchers run amok
I love visiting research labs as much as the next nerd, but we need to be careful about researcher-led AI implementations in business scenarios. With a huge talent shortage in AI, many companies are poaching PhD’s from universities across the globe. Facebook boasts an AI research team of over 100 researchers on staff, a luxury few other tech companies can claim, yet the Facebook Messenger AI group was shut down soon after achieving a 70 percent failure rate.
Some might argue the platform failed despite the massive investment of capital and academic talent, but we need to be honest with ourselves: It failed because of it.
Money and talent matter. They matter a lot. But, the failure rates we’re experiencing in this industry look a lot more like scientific research than IT implementations. Nature recently reported, “More than 70 percent of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments.”
The AI industry has imported droves of academic research scientists, and the result is a ton of experimentation with customers’ businesses. Don’t get me wrong — I value research, experimentation and even failure as a technology entrepreneur. But, any entrepreneur would agree that it’s unacceptable to ask customers to shoulder all of the risks.
Meanwhile, researchers, by necessity, are focused on the technology and its inner workings. They’re not trained for, nor are they typically very good at, ensuring optimal business outcomes. Consider for a second that AI failures are not a result of a shortage of PhD’s in artificial intelligence; they’re a result of the absence of business analysts and customer success specialists on their teams.
If you were in this far over your head in terms of business savvy, you’d be hedging too.
The collateral damage of navel gazing
Researchers are one crucial part of the AI ecosystem. But, thousands of developers and technologists have flooded into the space as it gained steam over the past decade. If you’ve ever spent time on sites like Stack Exchange or Hacker News, you’ll find devoted communities of talented technologists debating the merits of new technologies, arguing over the finer points of programming languages and tools, platforms and standards. This is how the technology industry advances itself, one step at a time.
Since AI is still in a relatively nascent stages, discussion and debate around all of these topics is at a fever pitch. As an industry, we’re still working to establish best practices and standards, and the process requires that our technical leaders look inward at the technology itself.
The good news is that we’ve done this for decades — this is how we ironed out digital transformation and the transition to cloud computing, then mobile, and now we’re doing it for AI.
The bad news is that most in the industry have spent little time and energy understanding their customers and their business needs. Silicon Valley has a long history of building glamorous new technologies that fail on the first try because they don’t have product/market fit. Building the best technology is not the same thing as building the best technology for my business.
This exact phenomenon is what we’re seeing in AI right now, at least with developers that haven’t obsessed over their customers.
Customers, customers, customers
The next great breakthrough in AI isn’t going to come from a lab at Stanford. It’s not going to happen in code with a customer. It’s going to happen in HR departments, where recruiting teams will enact strategies to hire business people that have the capacity to bridge the gap between business technologies and business outcomes.
We need to obsess about the business of the AI buyer, and we need to obsess about their customers, too. AI is not a one-off technology — it affects the entire value chain from start to finish. These technologies need to fit the business, not the other way around.
Ever since the days of “digital transformation” we’ve taken to lecturing businesses about how their IT should work. That’s not going to work anymore. AI reaches too far into a business and touches too many processes for any sane executive to let technology companies tell them how to run their business.
We need business people that are good at listening to their customers, and their customer’s customers — because that’s where AI has a real impact.
Thanks to AI’s capacity to transform businesses, the customer is, once again, always right.
Step by Step, AI Is Accelerating the Search for a Cancer Cure
AI already is delivering breakthroughs in cancer diagnosis, but even optimists believe a cure is a decade away.
5 min read
Opinions expressed by Entrepreneur contributors are their own.
For about a million years, the human race has been on a curve of innovation, an upward arc that has progressed from controlling fire, to inventing the mechanical movable-type printing press, to creating artificial intelligence (AI) systems that can defeat mankind’s most accomplished game players. Today, AI systems are bending the human innovation curve ever further skyward, accelerating the pace of progress and putting major breakthroughs within reach — such as ending terrorism or curing cancer.
Indeed, some researchers believe that an AI-assisted cancer cure is less than a decade away. However, even with the accelerant of AI, the journey toward a cancer-free world will be iterative, built on small steps — just as past innovations led to today’s cutting-edge technologies.
AI already is delivering breakthroughs in cancer diagnosis, but the technology will undergo multiple iterations, solving a plethora of smaller problems before taking on the ultimate challenge. This process will present copious opportunities for AI technology providers to contribute to the monumental challenge.
But to participate in this effort, technology providers need to understand the sequence of innovations that have led us to where we are today and to where we will eventually arrive in the future. Let’s look at a timeline of select AI innovations that potentially ends with the conquest of cancer:
- 1952 — Marvin Minsky unveils the Stochastic Neural Analog Reinforcement Calculator (SNARC), the first connectionist neural network learning machine — and possibly the first self-learning machine.
- 1975 — Backpropagation algorithm is developed that solves challenges with computational machines, allowing the training of multilayer neural networks and leading to the widespread usage of neural networks in the 1980s.
- Circa 2000 — The first use of the expression “deep learning” to describe a type of machine learning that creation of networks capable of learning from unstructured data in an unsupervised fashion.
- 2011-2012 — Convolutional neural network AlexNet achieves unprecedented levels of accuracy in visual recognition, paving the way for deep learning to enter the mainstream.
- January 2017 — Researchers at Stanford University develop deep-learning technology that can visually identify cancerous skin moles and lesions with the same level of accuracy as a human dermatologist.
- February 2017 — Microsoft establishes Healthcare NExT, an initiative designed to apply AI and machine-learning technologies to health issues, including cancer treatment.
- March 2017 — Google’s GoogleNet deep-learning technology detects cancerous tumors with higher accuracy than human clinicians.
- October 2017 — Intel announces first silicon for its Nervana Neural Network Processor (NNP) chip, which can accelerate deep learning tasks, including diagnosing cancer.
- Circa 2021 to circa 2026 — Microsoft is projected to release an AI-powered computer that operates inside the human body to detect and reprogram cancerous cells, rendering them harmless.
As this timeline shows, the pace of innovation in deep-learning and AI-based cancer research is accelerating. However, progress at this stage still involves relatively small steps leading up to the ultimate goal in the future.
This situation reflects the status quo in AI innovation, which involves using single-task-specific cognitive engines to perform mundane and repetitive tasks that are challenging for people, such as examining large numbers of images of tissue samples to detect signs of cancerous lesions.
These technologies are collectively called artificial narrow intelligence (ANI). Today’s most successful AI technologies are leveraging these engines for a wide range of specific purposes, from the object-recognition technology that powers Amazon’s DeepLens video camera to the face-recognition algorithms that control the Face ID authentication on Apple’s iPhone X.
These solutions are called “one-time” (1x) AI transformations. They represent pragmatic tools that satisfy immediate needs while promoting strategic objectives.
Such 1x transformations are playing a critical role the development of AI. Businesses that successfully integrate 1x AI innovations into their operations are expected to expand their workforce by 10 percent and revenues by 38 percent during the next five years, according to a report from Accenture.
These types of innovations will lead to the next generation of AI: two-fold (2x) transformations. Such 2x transformations take things a step further by using ANI to look at the bigger picture. For example, they can combine large amounts of data from a variety of sources, processing it and analyzing it to make it useful for specific tasks.
At the next level are 10x transformations, where AI technologies become powerful enough to solve major challenges. The 10x transformations will be enabled by the future development of two technologies: artificial general intelligence (AGI) and artificial superintelligence (ASI).
AGI is defined as a machine that can perform any intellectual task as well as a human does. Artificial superintelligence goes beyond AGI by delivering machines with intellectual capabilities that are superior to humans’.
The road to cancer’s cure will progress from today’s ANI-enabled 1x transformations, through 2x solutions, to the AGI- and ASI-driven technologies of the future. In order to participate in this process, providers and users of medical AI technology will have to participate in the iterative process of AI innovation, taking small steps toward the ultimate goal.
AI in Marketing: 4 Crazy Myths You Need To Stop Believing
Artificial intelligence will empower marketers, not replace them.
4 min read
Opinions expressed by Entrepreneur contributors are their own.
Lately, AI-powered marketing has been a buzzword across the world. And while the whole marketing world is talking about it, a recent study finds that AI adoption in marketing is limited by marketers. Why? Because it is still quite new within the marketing landscapes and in all the buzzing excitement, many marketers are still suspicious of it.
Here we will break down the misconceptions many marketers have that keep them from adopting artificial intelligence to enhance every step of their customer journey.
1. It will kill marketer’s job.
No, on the contrary, it will enhance it. What a customer expects from businesses these days is personalization. They want every interaction with brands to be more personalized. To get there, AI is a must. By harnessing the power of artificial intelligence, marketers can deliver fast, personalized service to their shoppers, taking their shopping experience to whole new level. But AI won’t work on its own. Crafting marketing strategies and the right messages for every customer will still be the marketers’ job. So, instead of killing jobs as some anticipate, what’s more plausible is marketers working alongside AI, which will help them achieve unprecedented levels of personalization and deliver the most immersive and seamless experiences for the consumers. Just like AI won’t kill tech jobs, it won’t kill marketer jobs either — machines will always need humans.
AI-powered tools are not the replacement for marketers but can help CMOs take their marketing to another level.
2. You must use AI everywhere.
Nope. This thinking that you have to apply AI-powered solutions in every marketing channel is a big reason why many businesses aren’t leveraging AI. Not only is it expensive to implement artificial intelligence everywhere, but it needs a vast amount of data be effective.
Adopting AI is difficult, but it doesn’t have to be. The best way to get started is to find one or two areas where AI can make the most impact in your marketing and result in the highest ROI. Add them to your marketing plan and commit to them. Once you’ve been able to implement AI successfully in these channels, add to it. This way an even small-size business can take advantage of AI.
A lot of marketers make the mistake of thinking they have to use AI everywhere. The truth is, you don’t.
3. It’s for big businesses.
This is the biggest misconception among small businesses. They think artificial intelligence isn’t for them. The truth is, it’s not just corporate giants that can benefit by implementing AI — it can help small businesses make their marketing more targeted and cut their costs in the long run too.
Though with small businesses, you must have a plan and stick to that plan as much as possible. Understand what’s crucial for your business and prioritize specific applications for artificial intelligence technology.
If you want to get the most out of your marketing spend, put AI in the driver seat.
4. To use AI, CMOs need a technical background.
Anybody can use Siri, drive a Tesla and use Echo. Do they need a technical background to do so? Of course not. Similarly, every marketer can use artificial intelligence even if they don’t have technical skills. The only thing CMOs need to make AI work for them is an AI-enabled marketing platform.
I am not saying technical skills doesn’t matter at all. Minimal technical competency is required to get the most value from an AI engine. But in most cases, learning to use the technology is not that tough. Marketers who refrain from artificial intelligence because they don’t have a technical background are missing out on opportunities that smart marketers are already mastering and driving ROI from.
Like marketing is critical for a business’ success, AI-powered tools become indispensable for marketing success.
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