2018 AI/ML Predictions (Part 1)
Artificial intelligence will begin making a positive impact on people's lives — but it may be a while before it's recognized for doing so.
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Given how fast technology is changing, we thought it would be interesting to ask IT executives to share their thoughts on the biggest surprises in 2017 and their predictions for 2018.
Here's the first of three articles sharing what they told us about their predictions for artificial intelligence/machine learning. Stay tuned for more predictions.
AI offers something for everyone, and as a result, we’re going to see AI incorporated across the entire technology spectrum. It’s going to be a boon to the software development community as tools become smarter but it’s also going to find its way into a great many controversies, such as “fake news” filters and other social and political topics where people can’t seem to find any common ground. Another thing that’s going to come out of the AI explosion: As more organizations look to implement bots and other conversation-driven AI solutions with customers, structured text is going to regain its value among people trying to give direct answers to customers asking specific questions.
AI has officially become a buzzword — companies in all types of industries are touting AI capabilities to promote smarter, more efficient technology. In 2018, I think we'll begin to see the real leaders continue to differentiate themselves in the market through their ability to clearly communicate what they really do, as well as their ability to process data smarter than the wannabes.
AI will go deep into the “trough of disillusionment.” The largest users (i.e. Facebook, Google) make it look easy, but companies without deep experience aren’t seeing the same results.
Conversational interaction is the next big thing for 2018. Artificial Solutions would argue that personas within conversational interfaces are as important as your domain content, the knowledge your digital employee uses to function. It’s the personality that elicits the most reaction, good or bad, from the customer. NLI overcomes many of the difficulties brands face in building a closer relationship with digital customers — and one of the key differentiators from other natural language technology is its conversational ability. The future of AI in customer engagement needs to be much more conversational if enterprises are to meet consumer demands for a sophisticated, intelligent experience. Humanlike understanding, including context and sentiment, must be combined with back-end data to enable deeper personalization to pave the way for organizations to reconnect with their digital customers.
If we park the paranoia and focus on the potential upside, I believe the advances we’re starting to see will result in some very near-term benefits — things that we always knew were possible but never thought we could achieve in a practical time frame. Thankfully, there are researchers working round the clock to tackle these problems — everything from solving world hunger through delivering an abundance of food to finding cures or vaccines for major diseases like cancer, HIV, and the common cold, all the way to delivering clean power by optimizing energy creation and storage. All of these challenges appear to be physically and mathematically solvable if we can just process enough data and run enough tests.
Even better, with the accessibility of massive, on-demand compute power from tech titans like Microsoft, Amazon, Google, and IBM, this work is no longer restricted to a small group of people with the necessary government funding, but is open to all, and inspired by many of the visionaries who’ve brought us major technological advances over the last two to three decades. Looking back ten years from now, I expect we’ll remember names like Bill Gates and Elon Musk — not for their work in technology but for taking their gains made along the way, applying them to bringing an end to disease and suffering, and finding a way to take our civilization beyond the boundaries of planet Earth.
AI will earn its first Nobel prize in 2018, but it won’t be awarded until 20 years later. For what? Healthcare. A major disease will be outright cured or a prescriptive treatment identified by AI that will then eliminate the disease. That’s putting the AI at the forefront (not replacing someone who answers the phone at the drug company with AI) and truly using AI to lead that same company in terms of R&D.
AI will replace white-collar jobs. AI has the greatest potential to segment society for the first time, and this means that white-collar jobs will be partitioned in 2018. Corporations and research groups will replace their best and brightest with artificial intelligence. AI won’t initially be augmenting careers for professionals lower on the corporate ladder — AI first will affect the upper-echelon of a corporation.
Cannibalize the AI industry. The first industry to get cannibalized by AI is the AI industry — because AI companies know how to use it. Data scientists know the domain space, so they will inevitably create engines that are better than the those previously used. This will cannibalize and push the data scientists to augmented roles. To illustrate, AI companies are at the forefront of productivity and efficiency so much so that they are in jeopardy of replacing themselves with the technology they create.
Cybersecurity/warfare in general. AI needs to be on the offense and defense. In the near future, we’ll be looking at an AI vs. AI combat zone for the first time. Warfare (in general) will move from state actors, hackers, and humans engaging in the process to AI. AI will be directed to attack foreign states and corporations at a veracity that humans can not defend, so now's the time to discuss purpose-driven AI for good, and regulations that should be put in place. Elon Musk will continue upping the ante and pointing to specific instances of AI ruining society. In 2018, Musk will further his predictions of global doom and will up the ante by pointing to specific cases where AI has damaged society. Musk will continue to perpetuate negativity in the industry.
Only those companies that truly have AI expertise and understand that building ML models requires proper data will be able to truly claim that they have a solution powered by AI and ML.
Enterprises will move from AI science experiments to truly operationalizing it. Enterprises have spent the past few years educating themselves on various artificial intelligence frameworks and tools. But as AI goes mainstream, it will move beyond just small-scale experiments run by data scientists in an ad hoc manner to being automated and operationalized. The complexity of technologies used for data-driven machine and deep learning has meant that data scientists spend less time coding and building algorithms and more time configuring and administering databases and data management systems. And as enterprise move forward with operationalizing AI, they will look for products and tools to automate, manage, and streamline the entire machine learning and deep learning lifecycle. Data scientists need to focus on the code and algorithms, and not automating and operationalizing the process. In 2018, investments in AI lifecycle management will increase and technologies that house the data and supervise the process will mature.
Building safer artificial intelligence with audit trails. Artificial intelligence is increasingly being used for applications like drug discovery or connected cars that can have a detrimental impact on human life if an incorrect decision is made. Many AI frameworks are a black box with many layers of computation built within as the framework learns from various data points. Detecting exactly what caused a final incorrect decision leading to a serious problem is something enterprises will start to look at in 2018. This might be a result of a serious AI blunder, which is unfortunately bound to happen eventually. Auditing and tracking every input and score that a framework produces will help detect the human-written code that ultimately caused the problem.
Marketers will be using AI to enhance and even create content. The Associated Press (AP) is already using machine-generated content to keep up with the demand for information. We can expect more of the same in the coming year.
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