2018 AI/ML Predictions (Part 2)
2018 AI/ML Predictions (Part 2)
AI will be a core technology in 2018 — alongside the bigger guys like cloud, big data, analytics, networking, storage, and security.
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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 second of three articles sharing what they told us about their predictions for artificial intelligence/machine learning. Stay tuned for more predictions.
Machine learning will go from “in vogue” to “in production.” Increasingly, machine learning will be seen as a normal part of business rather than being unusual, especially as more enterprises begin to reap the benefits of machine learning systems in terms of real business value. AI will continue to get a lot of buzz but it will be a much broader set of machine learning approaches that deliver valuable insights across many enterprises in different sectors.
Additionally, people are likely to see that the most successful systems occur where people focus more on the problem than the tool. They will recognize how important it is to frame the question correctly, have realistic goals, have access to appropriate data at scale, and have a realistic plan to convert machine learning results into action.
Organizations will recognize that 90% of machine learning Success is in the logistics (rather than the algorithm or the model). It may sound less exciting or cool, but being able to effectively manage data is essential to running successful machine learning systems in the real world. This is true for the complete lifecycle — from managing input data to the development of machine learning models to their ongoing maintenance in production. The good news is that with effective architecture and good planning, much of this can be handled at the platform level rather than the application level — and that cuts across many systems handled by different machine learning tools. In other words, you don’t have to come up with a new plan for logistics with every different project.
Because we think people will increasingly recognize the need for efficient machine learning logistics, we also think there will be a trend toward stream-based architectures and a global data fabric as part of their overall organization.
Relationship intelligence emerges as a category.Despite ongoing rumblings about AI and machine learning meshing with CRM software, we haven’t seen much come of the hype. But as AI finds more practical use cases inside the lead-to-cash lifecycle — and especially given that we currently live in a subscription economy (more on that later) — I believe we will see the emergence of a separate category, “relationship intelligence,” which is somewhat separate from core CRM.
Big data, AI and machine learning are coming together to drive insights in new and exciting ways for customer-facing employees. These insights can be delivered as SaaS/cloud services. While they might leverage some CRM data, the real gems are found in the oceans of data outside the corporate firewalls. By adding algorithms to this data and feeding it to employees, we can make anyone in the organization a relationship expert. This holds a lot of value in B2B, but also in more retention-focused B2C worlds.
Also, the fact that these tools can act as an “overlay” opens up a new competitive landscape for the players involved, where a new best-of-breed scenario might emerge and where some CRM leaders may see their customers opting for Relationship Intelligence tools provided by other vendors.
I recently saw that an analyst declared that we have reached “peak phone.” The theory is that innovations in hardware have peaked and any future leaps forward will be driven by software and, in particular, AI. Companies that traditionally depended on hardware or basic software for differentiation in the market will now have to shift focus to AI if they want to stay competitive. To this end, I think we will see the major players like Apple, Google, Microsoft, Amazon, and Facebook shift their R&D budgets to creating AI platforms. The term platform is key because this is where other companies can plug into this revolution. Like the public cloud, consolidation is happening quickly and it doesn’t make sense to build your own platform. Enterprises are evaluating their options and are looking for the companies with the strongest AI and ML platforms to build upon. The next wave of innovation in customer-facing and workforce solutions will leverage these AI platforms for their own new ground-breaking features. To sum it up, AI will become democratized in 2018 and everyone from small businesses to global enterprises will have access to powerful AI/ML capabilities.
“Horizon” is such a great word when we think about machine learning and AI since both are truly a journey. This coming year, I see people starting that journey more aggressively by beginning to use next-generation analytics and other AI technologies. I also believe that while these foundational pieces are established, there is a great opportunity for business-level discussions around how to use these technologies, as well as the potential benefits and pitfalls of using AI. These conversations are currently lagging behind the technology today.
There are two things I see happening in 2018. First, the big question will be, "How do you properly enable the users of analytics platforms to take advantage of AI/ML without jumping through hoops?" The evolving expectation is that analytics tools need to be data scientist-friendly, and must be easy to incorporate and integrate the analytics platforms into the tools and platforms that they use (for example, R or Jupyter Notebooks).
Second, AI/ML will continue to be less of the preserve of the Ph.D. data scientist and more accessible to developers, product managers, and others across the IT department. The only way for AI/ML to truly penetrate more of the software market is to reduce the complexity of using it.
Artificial intelligence is still in its infancy but enterprises across different industries are making great strides in adopting the technology to improve external processes — from chatbots and virtual assistants to sentiment analysis, natural language processing, and facial recognition — artificial intelligence brings capabilities that can drive a better customer experience. And it's not only about the customer experience; AI is also improving internal operations which is driving efficiency and cost savings.
In 2018, we’ll see an increase in artificial intelligence interactions from both users and trainers. Those who train AI systems to understand dialogues and intents within specific industries will, in turn, increase the volume and effectiveness of AI user interactions. This will become an increasingly in-demand skill set. Further, we’ll see AI used in the cyber security domain to protect against cyber attacks, and again, the training of systems to support these use cases will be critical.
The data-driven organization. A top priority for any company embarking on their digital transformation journey in 2018 will be quality data — more specifically, access to real-time data in which end-users can pull actionable results from. Internal, external, structured, or unstructured data will provide crucial insights for any organization. Data-driven analytics will prove to be advantageous in 2018. With this in mind, companies will need the tools to manage, govern, analyze, and harvest data accordingly.
Artificial intelligence as the fuel for new value creation. In 2018, AI will start to free up resources by automating monotonous tasks through end-to-end process automation. By enabling employees to spend their time on the tasks that create the most value, organizations will reap the benefits of improved customer service. As a result, AI will augment and may even completely remove human-machine interaction in the enterprise.
Conversational user interface: advanced human-machine interaction. According to Gartner, the next generation of consumers will expect self-service options and a level of customer engagement that allows them to accomplish tasks with little-to-no human interaction. Regardless of generation, consumers have become accustomed to digital assistants in every dimension of our lives, from our home to our car.
In 2018, we will see this consumer experience extend to the enterprise. For example, Gartner predicts that by 2020, 30% of web browsing sessions will be done without a screen. In 2018, companies will leverage the capabilities of natural language processing (NLP) and machine learning to offer a cognitive and conversational user experience. Digital assistants will help employees with everything from recognizing and learning situations to providing insights and suggestions for next steps to guiding complex workflows.
Machine learning use will increase exponentially, powered by open-source projects like Amazon DSSTNE (pronounced “Destiny”). “If you want your project to grow, making the code open-source will ensure its development,” says Amazon as it gives away DSSTNE, an open-source machine learning framework, developed initially to power its product recommendation systems. Because of frameworks like this being released as open-source, organizations will continually find more use for machine learning, from analyzing network traffic for malicious code and actors to improved diagnostics in medicine.
More open-source “unicorns” in 2018. A fairly easy prediction, given that open-source unicorns MongoDB and Cloudera IPO’d this year, although Cloudera isn’t the unicorn it once was. However, I’ll go out on a limb and guess that a unique open-source unicorn will be born based on technology made available through U.S. government code repository: code.gov. With the mandate that all government agencies, from the Department of Defense to the Food & Drug Administration, release at least 20% of their custom code to the public, I’m betting we’ll see the launch of a non-government-affiliated open source “unicorn” from technology made available through this initiative.
The rise of the drones as they become an essential part of natural disaster relief efforts. The drone community has been sharing its code on the web for all to use, share, and improve. Sites like dronecode.org, px4.io, diydrones.com, and GitHub all host professional open-source autopilot solutions. The increased use of open-source code in these unmanned aerial vehicles can potentially aid those who have been affected in a natural disaster. Medical kits, provisions (up to 5 lbs. in drones such as those planned for Amazon Prime Air), and information can all be safely transported to those in need without risking first responders’ safety in hazardous conditions.
AI is making IT organizations smarter, faster, and more efficient than ever before — and before we know it, AI will be a core technology alongside cloud, big data, analytics, networking, storage, and security. In the world of wireless networking, AI is already showing enormous value, proving that machine learning and neural networks can simplify operations, expedite troubleshooting, and provide unprecedented visibility into the wireless user experience. But we are just on the cusp of its true potential.
In 2018, I foresee AI accelerating across more industries and making major advancements to the already thriving wireless industry. I predict we’ll see jobs done faster and more efficiently with the introduction of new virtual assistants, which will allow users to proactively identify and fix problems and predict future events quickly and reliably. In the very near future, virtual assistants will be on par with wireless domain experts and it will be increasingly more difficult to distinguish virtual AI assistants vs. humans.
At the current rate of adoption, it’s not about if AI will disrupt every industry, but when. From self-driving cars to industrial robots and digital assistants, AI will reinvent every software company in the coming decades. And paired with other new technologies like AR/VR, AI-backed devices and systems will optimize automation and redefine traditional means of distributing information. For example, students will deploy AR/VR to simulate real-life applications with their curriculum and AI will deliver all of the relevant materials (content, videos, etc.) to augment the experience. Even more, networks powered by artificial intelligence will give homes a brain/nervous system to support all connected and smart devices. With artificial minds of their own, the smart devices we own will feed into computers that will emulate a cortex, sensing our presence, learning our habits, and self-regulating accordingly. AI is here, and it’s only a matter of time until it becomes a part of our everyday lives.
Simulated dataset providers to power hungry AI/ML companies will become highly sought after. This will be key to breaking down barriers for applications that today only the largest companies have the ability to deliver on.
Alexa will start conversations, not just react to commands. Alexa is a passive tool right now — you have to ask it for information in order for it to be useful. In 2018, Amazon will likely continue to release and connect to more sensors around the home and in life, letting Alexa proactively help with tasks — reminding you of appointments, letting you know about traffic, and instructing you to put on a warmer coat before going outside.
Machines will save the day in 2018. It will be the first year where machine learning and AI plays its first meaningful role in our ability to manage the cloud complexity we have unleashed over the recent years. We will start to see high-speed algorithmic automation transform management, with machine-driven knowledge and automation driving our monitoring, incident management, cost management and configuration management. This will ultimately result in reduced costs, higher security, improved service level agreements and better performance. The good thing is, we still need the humans — at least for now.
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