A new Forrester Research report, Predictions 2018: The Honeymoon For AI Is Over, predicts that in 2018 enterprises will finally move beyond the hype to recognize that AI requires hard work—planning, deploying, and governing it correctly.
But Forrester also promises improvements: Better human and machine collaboration due to improved interfaces; enhancing business intelligence and analytics solutions by moving resources to the cloud; new AI capabilities facilitating the redesign of analytics and data management roles and activities and driving the emergence of the insights-as-a-service market.
As a result, 70% of enterprises expect to implement AI over the next 12 months, up from 40% in 2016 and 51% in 2017. Here’s my summary of what Forrester predicts will happen in 2018:
25% of enterprises will supplement point-and-click analytics with conversational interfaces.
Querying data using natural language and delivering resulting visualizations in real time will become standard features of analytical applications.
20% of enterprise will deploy AI to make decisions and provide real-time instructions.
AI will suggest what to offer customers, recommend terms to give suppliers, and instruct employees on what to say and do — in real time.
AI will erase the boundaries between structured and unstructured data-based insights.
The number of global survey respondents at enterprises with more than 100 terabytes of unstructured data has doubled since 2016. However, because older-generation text analytics platforms are so complex, only 32% of companies have successfully analyzed text data, and even fewer are analyzing other unstructured sources. This is about to change, as deep learning has made analyzing this type of data more accurate and scalable.
33% of enterprises will take their data lakes off life support.
Without a clear connection to change-the-business outcomes, many early adopters will pull the funding plug on their data lakes to see if they pay for themselves or die.
50% of enterprises will adopt a cloud-first strategy for big data analytics.
Forrester expects 50% of enterprises to embrace a public-cloud-first policy in 2018 for data, big data, and analytics, as they look for more control over costs and more flexibility than on-premises software can deliver.
66% of enterprises will deploy insight centers of excellence as a remedy for organizational misalignments.
With firms bringing the voice of the customer into every business decision in a unified way, 56% of enterprises already report creating customer insight centers of excellence rather than centralized or purely distributed models to accomplish this.
The majority of Chief Data Officers (CDOs) will move from defense to offense.
Business-oriented CDOs will explore opportunities to innovate with data, either through analytics embedded in internal business processes or through new external data-enabled products and services. In 2018, more than 50% of CDOs will report to the CEO , up from 34% in 2016 and 40% in 2017.
Data engineer will become the hot new job title.
13% of data-related job postings on Indeed.com are for data engineers, versus less than 1% for data scientists, reflecting the trend of big data initiatives becoming mission-critical and the need to provide broader support to the business analyst.
The insights-as-a-service market will double as insight subscriptions gain traction.
66% of enterprises already outsource between 11% and 75% of their Business Intelligence applications. Forrester predicts that up to 80% of firms will rely on insights service providers for some portion of their insights capabilities in 2018.
Academia will become the new insights partner for enterprises.
And not just academia—new research labs like the nonprofit Open AI help solve the most challenging analytic and AI problems for firms that submit requests.
Another great article about AI from the DailyMail below:
AI detective learns to solve crimes by binge watching 39 episodes of CSI: Las Vegas and picking out plot patterns (but it’s still not as good as a human)
- Scientists mapped the footage, script and background sounds from 39 episodes
- This data was fed into a computer model that learned to process the plot
- Through each episode the AI said if it thought the perpetrator was on screen
- Results showed it successfully guessed the criminal 60% of the time
Investigators trying to solve complex crimes could be given a hand from AI computers in the future.
Scientists have trained a computer to solve crimes by binge-watching 39 episodes of the TV programme, Crime Scene Investigation.
It can still only get the answer right 60 per cent of the time, and is not as good as humans who get it right on average 85 per cent of the time.
The researchers hope their findings will aid the development of machines that can take on board – and make sense of – large streams of information in real time.
Investigators trying to solve complex crimes could be given a hand from AI computers in the future. Scientists have trained a computer to solve crimes by binge-watching episodes of Crime Scene Investigation.
Researchers from the University of Edinburgh have developed an artificially intelligent machine that can identify a fictional killer.
The machine does this by pulling together information from images, audio, transcribed dialogue and scene descriptions. Scientists taught machines to approach solving the crimes in the same way that people would – by considering which characters might be responsible from their behaviour.
To teach the machine, the researchers mapped the footage, script and background sounds from 39 episodes of the TV programme CSI into a machine-readable format. This data was fed into a computer model that learned to process the plot as each episode unfolded, continually revising the criminal’s identity.
Throughout each episode, the AI had to say whenever it thought the perpetrator was on screen. The researchers then tweaked the neural network’s output as it watched and guessed, then tested it on an unseen episode.
Results showed that the computer correctly identified the perpetrator during the final part of an episode 60 per cent of the time. In comparison, people who watched the same shows were able to identify who was responsible 85 per cent of the time.
Dr Lea Frermann, lead author of the study, said: ‘Pinpointing the perpetrator in a TV show is a very difficult task for computers, but our model performed encouragingly well.
‘We hope our findings will aid the development of machines that can take on board – and make sense of – large streams of information in real time.
Bill Gates says big data can help solve the Alzheimer’s puzzle
Through his foundation, Bill Gates has focused on reducing global poverty, finding cures for infectious diseases, and promoting education and sustainable energy. Now Gates is getting into an area that’s new for him: Alzheimer’s disease. Today, the philanthropist and co-founder of Microsoft announced he is investing $50 million in the Dementia Discovery Fund to accelerate research and progress in tackling the disease, which affects more than five million Americans. The investment is a personal one, not part of the foundation’s work.
Gates spoke with Marketplace Morning Report host David Brancaccio about why he’s optimistic about a breakthrough. Below is an edited transcript of their conversation.
David Brancaccio: I think it’s fair to say we’ve all been touched by this medical issue at one level or another. Why did it grab your attention personally?
Bill Gates: Well, the mystery of why the various drugs haven’t worked drew me in. Some of the men in my family have had this, and you know, I’ve seen the human tragedy of the disease. The idea that the more people we get engaged in this, the faster the research is going to go, including, I hope, to get some of the data organized so it’s easier to see what’s going on. You know it’s just a huge problem.
Brancaccio: Let’s talk about the role of data. You think this whole area is a little bit opaque?
Gates: Yeah, if you look at the success in things like heart disease, these data sets of understanding — you know, who is more susceptible and who wasn’t — they made a big difference. With Alzheimer’s we have a lot of trial data including the failed trials, we have long-term studies, really understanding the different factors that contribute, you know how cognition goes along with various biological markers, a great data set will help the researchers……, Read the full interview with Bill Gates here
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