Artificial Intelligence has transformed dramatically in a very short time. Over time the increasing penetration of AI has resulted in a wide-scale growth of the potential this tech could offer. With the ever-growing volume of data, we can now make almost anything intelligent. Now we can also augment the capabilities of this data using artificial intelligence. Thus, gaining access to a much more useful data that upon analysis results in dependable insights. But how exactly will the changing artificial intelligence impact what we can do with big data? To know the answer, we reached out to 8 industry experts. Here are their thoughts on the future of artificial intelligence and data science.
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1. Using AI to Comply with Big Data Regulations:
It's humanly impossible to manage and analyze all the data businesses now have to collect in a way that is economical, as fast or as high quality as AI. AI can add the equivalent of millions of data scientist man-hours to a business burdened with the 3Vs of "big data" (volume, variety, and velocity). Moreover, new EU legislation coming into force in May - The General Data Protection Regulation (GDPR) - means many businesses collecting large amounts of customer data are likely to look to AI to help them comply with the new laws. The GDPR requires companies collecting data to enable consumers to opt-in and out of communications easily; provide reports to consumers on what data is being collected about them, and provide easy ways to delete that data. Without AI tech, that will be very time consuming and costly for businesses.
- Dr. Jurgen Galler, ex-Google director, now CEO of an AI tech company -1plusX. @1plusX
2. Increased Role of Ethicists in Managing Exceptions:
Artificial intelligence will require many businesses to gain a far deeper understanding and appreciation of ethics because that is the evolution of quality control under the application of AI. Good software developers practice "defensive programming," which is planning for contingencies that their program should not create, but would be very bad if it did. As AI drives software to become more complex, more capable of creative extrapolations, the ways in which bugs can manifest include equally higher levels of abstraction.
This means that AI that is directed to carry out knowledge work with fuzzy boundaries, such as optimizing business strategies, could have buggy emergent behavior that could come out at the level of, say, ordering an unwanted part, canceling a supply contract, or using inappropriate language in a report. At this point, the way in which such software will be configured will resemble not so much programming as training a dog and "getting it right" will require trained ethicists or even philosophers on staff.
- Peter J. Scott, Author of "Crisis of Control: How Artificial Super Intelligences May Destroy or Save the Human Race" @peterjscott
3. AIOps for Change Tolerant Algorithms:
Modern IT environments are incredibly (and increasingly) complex and ever-changing, leading to large amounts of time and resources devoted to monitoring, troubleshooting, and course correcting. It’s a reactive position for most companies, but when teams use AIOps technology they can leverage change-tolerant algorithms and access indexed information. This allows them to spend more time focused on proactive, meaningful work rather than fixing the same problems repeatedly or spending time managing rules and filters.
- Phil Tee, Co-founder and CEO, Moogsoft Inc. @moogsoft
4. AI for Data Analytics:
Artificial intelligence will change data analytics by learning (using results from past analytic tasks) so that over time, results will come faster with much more accuracy. With enormous amounts of data to pull from, along with analytic results from past queries, artificial intelligence will be able to provide accurate future predictions based on current events, and one could argue that weather prediction models are a form of artificial intelligence. Further, machine learning will power business analytics by identifying potential problems or issues that might not be detected by humans. Organizations that do not deploy artificial intelligence for data analytics will fall behind competitors that use artificial intelligence for data analytics.
- Marcel Shaw, Federal Sales Engineer with Ivanti
5. Augment Data Science Capabilities with ML:
While human, specially trained professionals such as a data scientist has the power of intuition to aid in identifying meaningful correlations, machine learning provides unrivaled scalability in comparison. Billions upon billions of combinations can be evaluated for correlations. Further, machine learning can augment data science capabilities, and I expect the growing demand for data scientists to continue.
Their skills will be needed to develop training models for AI and to analyze the results, and analysis of unstructured data continues to be one of the biggest promises and challenges of machine learning. The commercial analysis of unstructured data that I am aware of still requires a significant amount of human intervention to yield practical value for unstructured use cases. Machine learning will become ubiquitous in business analytics very quickly and will transform the field. I suspect many of the applications will be subtle to consumers but grow more visible in time.
- Sean Waddell, Senior Product Manager, Ivanti.
6. AI as an Assistive Tool to Improvise Expert Methods:
As AI and Machine Learning move into the standard programmer’s toolkit, we’ll quickly see an increase in assistive technologies to augment existing expert’s methods. Where much of the writing on this topic has centered on “lost jobs” what I actually see occurring is a new generation of intelligent assistants that are beyond helping you purchase things (see: Alexa) and more in line with letting you know that you could add some interesting information to the email that you’re writing, or that your designs are more or less similar to a brand identity than you intended.
- David Evans, the CTO of Uncorked Studios, a product design and development studio in Portland. @spaceLenny
7. Data-Backed Decision Making:
Companies benefit from AI by making smarter, more informed decisions, in any industry, by collecting, measuring, and analyzing data to prevent fraud, reduce risk, improve productivity and efficiency, accelerate time to market and mean time to resolution, and improve accuracy and customer experience (CX).
Unlike before, companies can now afford the time and money to look at the data to make an informed decision. You cannot do this unless you have a culture to collect, measure, and value data. Achieving this data focus is a huge benefit even without AI since a lot of businesses will continue to operate on gut feel rather than data. They view data as a threat versus an opportunity, and ultimately these businesses will not survive.
Employee engagement and CX can be improved in every vertical industry, and every piece of software can benefit. AI can replicate day-to-day processes with a greater level of accuracy than any human, without downtime. This will have a significant impact on the productivity, efficiency, margins, and the risk profile of every company pushing savings and revenue gains to the bottom line. Companies will be able to get to market faster and cheaper, with greater customer satisfaction and retention.
- Tom Smith, Research Analyst, DZone Inc. @ctsmithiii
8. Enabling Machines to Understand Human Communication:
One of the more exciting applications around AI is in natural language understanding and speech recognition. These are AI tools that make it possible for individuals to speak with services by having their naturally spoken phrases translated into commands. With these tools, companies can open up customer service and support backend services to reduce customer wait times and increase satisfaction. It also makes it possible for companies to open up their services to access through voice devices such a Google Home or Amazon Echo.
The breakthrough that's coming to these services is that speech recognition is hitting human levels of accuracy and will soon surpass the best human-based transcription. In addition, sentiment analysis and emotion detection will enable companies to have much more information on their customers to provide them with a tailored response to their queries.
Another upcoming AI-based technology is natural language generation. This will allow for retrieved information from a query to be spoken back to users in a much more natural sounding way and without required a human to pad query results with the appropriate wording.
- Leor Grebler, co-founder & CEO of Unified Computer Intelligence Corporation (UCIC) - a company dedicated to bringing voice interaction to hardware.
Over time we at NewGenApps have developed deep expertise in artificial intelligence and big data analysis. Our certified experts can handle any level of challenges involved in dealing with these new age technologies. Contact us today for a consultation or POC.
