Predicting the Future With AI

C O N T E N T S:


  • Predicting the future is hard but at least we can consider the past and present AI, and by understanding them, hopefully be better prepared for the future, whatever it turns out to be like.(More…)
  • To put it in simpler terms, it is the Lyapunov time which sets the horizon regarding predicting the future.(More…)
  • Any industry that cares about predictions, that wants to know something about the future, that uses data to inform its decisions, that needs to make some kind of diagnosis about the health of humans or machines – all these industries will be affected by AI and many of them will be transformed utterly. (Caveat: I?m not saying that will happen tomorrow, but it will happen in the next 10-20 years, spreading much as the Internet did.)(More…)
  • View the recording to learn how AI-powered social data is predicting the future.(More…)


  • The AI industry is already responding to the concerns of regulators with countless organizations to assess potential AI issues and to promote the safe use of AI technology.(More…)
  • Both machine learning and deep learning are applications of artificial intelligence (AI), and the evolving use of AI technologies like these will deeply impact the practice of data science.(More…)


Predicting the Future With AI
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description: Facebook Uses Artificial Intelligence to Predict Your Future …


Predicting the future is hard but at least we can consider the past and present AI, and by understanding them, hopefully be better prepared for the future, whatever it turns out to be like. [1] This webinar, hosted by social intelligence leader, Converseon, and in4mation insights, one of the most innovative marketing analytics companies, will share the results of a new groundbreaking study that provides definitive proof of how AI powered social data is predicting the future in one global industry — and lessons on how it can be applied in other areas. [2]

It’s an exciting time for AI in marketing and–with AI technology evolving daily–the future appears to hold both exciting and unpredictable outcomes. [3] Some promise us a utopian future with exponential growth and trillion-dollar industries emerging out of nowhere, true AI that will solve all problems we cannot solve by ourselves, and where humans don?t need to work at all. [1] While working at MIT, he created an AI laboratory where he developed LISP (Full List Processing), a computer programming language for robotics designed around offering expansion potential as technology improved in the future. [4] You may be disappointed to hear this, but we don’t have a crystal ball that would show us what the world will be like in the future and how AI will transform our lives. [1] In fact, we claim that anyone who claims to know the future of AI and the implications it will have on our society, should be treated with suspicion. [1] There are a number of reasons why both of the above scenarios are extremely unlikely and belong to science fiction rather than serious speculations of the future of AI. [1]

Although hard skills will always be in high demand, with AI in the picture we are increasingly hurtling towards a future where soft skills, like data interpretation and the ability to build human relationships and identify problems, will be what separates us from machine workers. [5] Organizations are more able to utilize strong business analysis than ever before, all facilitated by IoT and AI. This focus on data analytics as the core feature of business intelligence is at the heart of the future of IT. [6] The use of AI is creating new predictive features that can filter through past data to more accurately predict future behaviors and impact. [6] “We really control the future of ways of AI and machine learning will be built into work,” said Jason Jackson, assistant professor in the MIT Department of Urban Studies and Planning. [7] By changing the way businesses function, making certain processes faster, more accurate, and requiring less hands-on work, AI is driving the future of the way these entities operate. [8]

Read on to learn how the IoT, AI, and machine learning are shaping businesses and the future of IT. [6]

We expect AI, robots, and automation to continue to drive interest in jobs and privacy into the future. [9] As noted in the recent book, ” The Future of Work: Robots, AI, and Automation,” there are exciting advances in finance, transportation, national defense, smart cities, and health care, among other areas. [9]

To put it in simpler terms, it is the Lyapunov time which sets the horizon regarding predicting the future. [10] We will start by addressing what is known to be one of the hardest problems of all: predicting the future. [1] Predicting the future is almost as difficult as figuring out how to prosper from it. [11]

Any industry that cares about predictions, that wants to know something about the future, that uses data to inform its decisions, that needs to make some kind of diagnosis about the health of humans or machines – all these industries will be affected by AI and many of them will be transformed utterly. (Caveat: I?m not saying that will happen tomorrow, but it will happen in the next 10-20 years, spreading much as the Internet did.) [12] As much as we view AI may as the wave of the future, it needs access to a tremendous amount of data to “learn.? [13]

View the recording to learn how AI-powered social data is predicting the future. [14] AI uses machine learning, meaning that the more data it has at is disposal, the better it becomes at predicting successful outcomes. [15] AI is already advancing so quickly that computers can learn specific tasks without being programmed to do so, and many experts are predicting it will become integral in marketing and marketing automation systems. [13]

Machine learning is about predicting the future based on the past. [15] We?re only at the beginning of the development of future predicting software, which is likely to become extremely powerful in the future. [16]


The AI industry is already responding to the concerns of regulators with countless organizations to assess potential AI issues and to promote the safe use of AI technology. [3] The company’s AI technology is already embedded into robots manufactured by other companies, such as SoftBank’s concierge and sales associate robot, Pepper. [17] In 2018, AI isn?t a nice-to-have feature in your marketing technology stack. [3] For more information about how AI is changing the face of marketing, download 8 Ways Artificial Intelligence Is Supercharging Marketing. [3] Reaching a level where AI could be conversational was a challenge; it meant that the AI interface had to not only understand what the user said, but also predict what the user wanted and then respond in a convincing way. [3] AI solution providers, as well as marketers, will have to navigate more stringent regulations with an eye toward using AI in a way that enables customers to perform tasks while not overstepping the bounds expected by users and regulators. [3] Tesla CEO Elon Musk referenced the action movies as a way of illustrating his fears about the worst possible outcomes of development of AI technology. [17] A couple weeks later, Facebook released the designs to Big Sur, the computer server that runs AI algorithms the company uses. [17] We sat down with Dr. Sid Reddy, chief scientist at Conversica, to learn more about what could happen in the coming years for AI in marketing. [3] Conversica provides an AI platform that automates the engagement and qualification of marketing and sales leads. [3] “If it doesn’t have an element of AI in it, it’s going to be considered, like, kind of dumb technology.” [17] It’s not a one-time response–the AI must continue conversing for as long as the user interacts. [3] A conversational AI solution can draw information about a customer from a firm’s CRM application or other data sources (such as Facebook or other vendors). [3] As advances in the field make the technology even better, the use of conversational AI will continue to grow. [3] Once conversational AI adoption increases, Dr. Reddy predicts a rise in hyper-personalized marketing. [3] Although marketing automation software has enabled organizations to customize some elements of marketing communication, there is a tremendous opportunity to take this even further with conversational AI and the integration of additional systems. [3] Conversational AI chatbots are so popular, in fact, that when you ask marketers at enterprise-level organizations about where they are investing their departmental budget, there is a debate going on between pouring resources into conversational AI versus apps for smartphones and tablets. [3] Conversational AI is easier to use–all you have to do is either type or speak, and the AI will quickly find the answer to your question or perform a particular task. [3]

AI offers the unprecedented ability to leverage technology and data at a level of speed, responsiveness and depth that no human could have reached before. [18] The key source of sustained competitive advantage from data is through we call feedback data, which is the ability to continually improve your AI. And that means you need to invest in learning. [19] What fraction of spending on “big data” will imply the use of machine learning or other AI applications? What portion of “predictive analytics” inherently implies training AI algorithms, as opposed to merely permitting clearer forecasting and visualization? It’s hard to tell. [20] In our first four chapters, we?ve cut through the hype of AI by not only sharing our expertise on the subject, garnered from over 10 years experience in the industry; but highlighting what it is, ways that marketers can put it to work for them, and the importance of clean data. [18] Combined with data at scale, AI provides forward-thinking marketing teams with timely and actionable insights about an entity’s intent–a sea change for this industry. [18] AI will only have a major impact on marketing strategy when it is combined with human insight and creativity. [18] The continuing collaboration between AI and human marketers, with AI-assisted precision, measurability, and productivity, will lead to more relevant and meaningful marketing. [18] It is this lack of emotional intelligence within AI that gives humans the edge over robots. [4] While the idea of “artificial intelligence” had been speculated about in fiction for centuries — as far back as Mary Shelley’s Frankenstein or Karel ?apek’s R.U.R. (Rossum’s Universal Robots) — it was not until Alan Turing in 1950 that the concept of AI first became more than a fantasy. [4] Since Turing’s Test, AI was limited to basic computer models — with MIT professor John McCarthy coining the phrase “artificial intelligence” in 1955. [4] Modern AI is bypassing grand questions about meaning of intelligence, the mind, and consciousness, and focusing on building practically useful solutions in real-world problems. [1] In the 1960s neural networks were widely believed to solve all AI problems by imitating the learning mechanisms in the nature, the human brain in particular. [1] The human society still has the power to decide what we use technology, even AI technology, for. [1] The marketers who will win are those who quickly understand where that point is and use both AI and smart humans to complement each other. [18] In the second alternative scenario, the robot army is controlled by an intelligent but not conscious AI system that is in principle in human control. [1] Despite some base model machines showing promise, from the “first robotic person” Shakey the Robot in 1966, to anthropomorphic androids WABOT-1 and WABOT-2 from Waseda University – the field of AI started to plateau in the 1980’s. [4] Instead of creating machines that could carry out ever-more advanced singular “top-down” tasks — from playing the piano to calculating math problems — AI should be a machine-based relationship with the world around it or “bottom-up”. [4] So, in understanding why there’s been this excitement around AI in the last 10 years, if not the last two years, it’s all driven by machine learning, which is prediction technology. [19] Much of this power is indeed given to us by technology, so that every time we make progress in AI techonology, we become more powerful and better at controlling any potential risks due to it. [1] Each time an all-encompassing, general solution to AI has been said to be within reach, progress has ended up running into insurmountable problems, which at the time were thought to be minor hiccups. [1] Modern AI methods tend to focus on breaking a problem into a number of smaller, isolated and well-defined problems and solving them one at a time. [1] To repeat what we are saying: superintelligence will not emerge from developing narrow AI methods and applying them to solve real-world problems. (Recall the narrow vs general AI from the section on the philosophy of AI in Chapter 1.) [1] We are at a unique moment that presents a corresponding unique opportunity: we are swimming in rich consumer data, and the AI tools needed to make that data actionable are mature and readily available. [18] The following two figures detail how many companies are using these AI technologies beyond lab experiments (i.e., those developing applications based on it or deploying it across the company). [20] We help leaders gain insight on the applications and implications of AI in their industry. [20] AI has applications and use cases in almost every industry vertical and is considered the next big technological shift.” [20] The business value of artificial intelligence (AI) will more than triple globally by 2022, according to a new report from industry analysts Gartner. [21] Many of the recent advances of AI have originated in the tech environments of companies with advertising as a primary business model. [18] AI disruption will open up new vistas for opportunity, creating new business paradigms and making others extinct. [20] The idea that a superintelligent, conscious AI that can outsmart humans emerges as an unintended result of developing AI methods is naive. [1] Forming the background to AI measurements ever since its introduction in Turing’s paper, it was only in 2014 that a Russian-designed chatbot programme, Eugene, was able to successfully convince 33% of human judges. [4] One of the favorite ideas of those who believe in superintelligent AI is the so called singularity : a system that optimizes and “rewires” itself so that it can improve its own intelligence at an ever accelerating, exponential rate. [1] AI helps marketers be more nimble in when and how they engage consumers, unlocking a new wave of more effective, hyper-dynamic marketing. [18] There’s no denying that traditionally labor-intensive and non-data-driven marketing services will be enhanced by AI and automation. [18] It is interesting to observe that AI and marketing have a particularly symbiotic relationship. [18] What does investing in learning mean? It means making your product potentially worse in order to improve the AI. [19] The group learned close to a handful of results that were promising that predated the AI deep learning revolution. [10] There’s a strong case AI should be called Assisted Intelligence instead of Artificial Intelligence. [18] Definition: Artificial Intelligence (AI) refers to IT systems that sense, comprehend, act and learn. [20] It might sound obvious to us now, thanks to a lifetime rooted in the advances of AI, but back in the early 90s, the suggestion that artificial intelligence should be reactive to its surroundings was revolutionary. [4] To fulfill the promise of AI as a new factor of production that can reignite economic growth, relevant stakeholders must be thoroughly prepared–intellectually, technologically, politically, ethically, socially–to address the challenges that arise as artificial intelligence becomes more integrated in our lives. [20] In some sense, advertising is not only a primary benefactor of AI and machine learning, but advertising money has financed much of the AI revolution. [18] As you recall, we started by motivating the study of AI by discussing prominent AI applications that affect all ours lives. [1] Tractica forecasts that the revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025. [20] Others make even more extraordinary statements according to which AI marks the end of humanity (in about 20-30 years from now), life itself will be transformed in the “Age of AI”, and that AI is a threat to our existence. [1] The history of AI, just like many other fields of science, has witnessed the coming and going of various different trends. [1] The real threat the Terminator poses is the diversion of attention from the actual problems, some of which involve AI, and many of which don?t. [1] We?ll discuss the problems posed by AI in what follows, but the bottom line is: forget about the Terminator, there are much more important things to focus on. [1] Think about using Waze — it’s for driving directions — and if they learn that there’s an accident on the road somewhere and there’s a backup, the app, the software, the AI will take you a different route. [19] I take a practical approach to the definition of AI and present an analysis based on self-identified businesses that claim to be using or building AI.” [20] With the advent of AI, marketers ultimately will be looking for ways to discover more intelligent patterns for audience identification, as well as optimization of content, channel, nurturing and sales strategies. [18] Whether the history will repeat itself, and the current boom will be once again followed by an AI winter, is a matter that only time can tell. [1] Companies also initiate and expand their efforts in AI in the fear of missing out (FOMO). [1] That means less than one percent of all medium-to-large companies across all industries are adopting AI. [20] As software has radically transformed workflows everywhere, AI is transforming how software works for us, yielding real signals within a universe of digital noise. [18] Someone has to invest in improving the AI sometimes even at the expense of the immediate customer experience. [19] Study context: Accenture, in association with Frontier Economics, modeled the potential impact of AI for 12 developed economies that together generate more than 50 percent of the world’s economic output. [20]

Machine learning can help humans to predict long-term future. [10] FixStream AIOps platform helps hybrid IT teams by using machine learning algorithms to predict future application issues and outages. [22] Say you?re a warehouse, and you?re sitting on data on past inventories and your warehouse, OK? That’s going to help you predict future inventories, and you?ll now have a better model if future inventories, and you can make money on it. [19] They will help humans to predict the future in a more reliable manner. [10] After the initial training, they found that the network could easily predict the precise future values of the above-mentioned three variables. [10]

Every year, the possibility of an “intelligent technology” future becomes more and more of a reality — as algorithms and machine learning improve at a lightning-fast rate. [4] When attempting to chart the future, it’s always essential to know the past. [4]

It’s very expensive to train people up, and machines are very good at predicting tenure in the job much better than humans are. [19] We know humans are pretty bad at predicting which applicants for a job are going to perform best. [19] Deep learning programs at a diagnostic imaging lab in Cleveland, USA, now routinely defeat their human counterparts in diagnosing heart failure, detecting various cancers and predicting their strength. [21]

“FixStream’s multi-layer correlation, visualization and machine learning capabilities across business KPI’s, applications and infrastructure is a game-changer, reducing the complexity of how enterprises identify issues across their IT infrastructure and saving millions in revenue by predicting outages in the future.” [22]

Both machine learning and deep learning are applications of artificial intelligence (AI), and the evolving use of AI technologies like these will deeply impact the practice of data science. [5] We are in the midst of an “AI awakening,” as artificial intelligence technologies can now match or surpass humans in fundamental skills like image recognition, Erik Brynjolfsson, director of the MIT Initiative on the Digital Economy, said in a panel discussion session at the 2018 MIT Sloan CIO Symposium. [7]

With this increased ability to leverage data analytics has come huge demand for tech professionals with the skills to use the IoT and Artificial Intelligence (AI) to improve business. [6] AI, in turn, is the key to unlocking the potential of the IoT. As a part of AI initiatives, machine learning identifies patterns and anomalies in the large data sets that IoT provides in order to create more accurate predictions. [6] A wide variety of tech professionals with skills in development, hardware, analytics, and security are required to fill these positions, and it has become difficult for many employers to fill positions for IT Engineers, AI Specialists, and other critical roles that contribute to their data and machine learning initiatives. [6] AI tools take the journey from data gathering to data processing, and go one step further, by synthesizing data into designs and solutions. [5] AI tools can not only do the work of the human mind, but in many cases they can do it more effectively than we ever could. [5] Take, for instance, the company Affectiva, which uses AI equipment to recognize human emotions–anger, joy, surprise–as effectively as human leaders in commercial focus groups. [5] AI is also incredibly useful for enhancing online security because of its ability to analyze and adapt faster than a human being tasked with doing the same. [8] Johnson predicts that the AI will be able to fill all of those blanks in on its own, which helps business create a perfectly tailored experience for each customer they are trying to serve. [8] Is this particular person willing to purchase your product? What are they looking for concerning customer support, switching services, or just looking around? A stronger AI will be able to predict these things to maximize the efficacy you have in meeting a customer’s needs. [8] Gartner predicts that by 2022, more than 80% of IoT projects will incorporate AI, a significant rise from only 10% in 2017. [6] We evaluated how well our AI could predict the next move in 20 Ticket To Ride two-player games. [23] The gaze model was able to predict the correct destination city earlier than the basic model, with the AI that used gaze recognising intentions a minute and a half earlier than the one without gaze. [23]

AI is currently being used in this way, but the changes that are coming up in the world of artificial intelligence are going to add depth to what AI can do for a business in this regard. [8] Here are ten of the ways AI is bringing business to new horizons. [8] The scope of AI is continuously expanding, and it has come to simplify or eliminate the work that some employees have to do to keep a business running. [8] When a potential client begins to look for or talk about something online, an AI could comb through that content and decide to start pushing them advertisements about a particular business based on what they’re talking about or interacting with. [8] As Wired already noted in their article, Get Ready for the Robot Propaganda Machine (2015) and in Berit Anderson and Brett Horvath’s article, The Rise of the Weaponized AI Propaganda Machine (2017) they’re pretty much there already. [24] AI has already proved to be pretty adroit at spotting early signs of disease in medical data. [25] One of the greatest challenges of implementing AI is ensuring that your data is up to date, and actually reflects some underlying process, Rahwan said. [7] AI will be able to speed that process up by processing the applications and flagging specific criteria that you have deemed important, marking people as unhireable if they don’t demonstrate the right experience on their resume, and so on. [8] Healthcare has a number of strong applications for AI and robotics, the panelists agreed. [7] For instance, Autodesk’s Project Dreamcatcher is a computer-aided design (CAD) system that uses AI to generate 3D product designs based on criteria imputted by designers, such as the functional objectives, materials required, manufacturing method, and budget. [5] At the 2018 MIT Sloan CIO Summit, a panel of AI experts discussed how machine learning will impact the workforce. [7] With AI, companies can get the exact same insight but faster and considerably more easily accessible. [8] View the recording to learn how to apply AI Technology to Open-Ended Responses. [14] Right: by determining that your opponent keeps looking at Helena and Seattle, the AI can make better predictions of the routes the opponent might take. [23] Zac Johnson, an Internet Marketer, and Entrepreneur talked about how AI can help customize the user experience. [8] Again, the overarching trend is the evolution away from tools that are merely informational to ones that are transformational, as rightly pointed out by author and AI expert Carlos Perez. [5] Chatbots are a form of AI that people are likely familiar with by now. [8] Some experts believe that AI technologies will eventually become so commonplace as to be ubiquitous, essentially ushering in a new industrial revolution. [5]

Whereas the latter uses historical data to predict the probability of future of events (i.e. the likelihood that it will rain tomorrow), the former assumes an active human agent capable of influencing outcomes. [5] Predictive analytics is the second stage of business analytics in which past data is used with algorithms to predict a future outcome. [5] The new FixStream AIOps platform can quickly detect patterns to predict and prevent future business outages, modernizing IT operations management while increasing revenues, customer satisfaction, and business agility. [26]

That argument points toward a future where data scientists behave something like “machine whisperers,” helping devices steer clear of social faux pas, and generating the missions and projects they engage in. [5] The future will likely involve partnerships between humans and machines (known as collaborative robots, or co-bots) to more efficiently get work done. [7] In preparing for the future of work, CIOs should look to hire workers that are flexible, and open to learning, as automation may change the nature of their job, Reynolds said. [7] Data scientists might have been dubbed the “sexiest job of the 21st century” back in 2012, but today, the future of the role isn?t quite as clear. [5] Given that many tasks may become automated in the near future, it would be wise for the businesses creating those jobs to look for candidates who can eventually take on leadership roles as data science jobs evolve. [5] Tools like these anticipate a future where data science moves from a strictly analytical function (synthesizing big data and offering insights and recommendations) to more product development and R&D applications. [5] The presentation of predictive tools in an easy-to-digest, consumer-facing user interface is exactly what the future has in store for data science. [5]

The potential applications of this kind of proactive data are practical innumerable; it’s especially useful for predicting outcomes in retail, sales, marketing, politics, and charitable donations–essentially anywhere where users hope to inspire populations towards a certain behavior. [5] “FixStream’s multi-layer correlation, visualization and machine learning capabilities across business KPI’s, applications and infrastructure is a gamechanger, reducing the complexity of how enterprises identify issues across their IT infrastructure and saving millions in revenue by predicting outages in the future.” [26] While we are witnessing it achieve remarkable and oftentimes superhuman performance in tasks previously regarded as outside the bounds of computers, AI systems are still narrow in scope and remain, in essence, a prediction technology. [27] The main thesis of Prediction Machines is deceptively simple that AI is best understood as a tool to lower the cost of prediction. [28] In a thoughtful new book, Prediction Machines, Ajay Agrawal and colleagues at the University of Toronto provide a much needed antidote to the overhyped AI discussion that dominates most forums. [28]

Machines are first introduced to assist humans by augmenting their skills, which, in the context of AI and machine intelligence, is called intelligence augmentation (IA for short). [27] At each stage of this inexorable automation march, tasks that were once believed to lie beyond the scope of computers and were reserved exclusively for humans and their “unique” abilities are nonchalantly redefined as not “real intelligence,” and thus the goalpost for “real AI” is moved a bit further. [27] Recent cases show that people don?t like relying on AI and prefer to trust human experts, even if these experts are wrong. [29] Combining the human element and machine learning to enhance the customer journey is what Blended AI does. [30] The trucking industry is catching up to its obsolescence and is embracing growing demands from customers through IoT enabled technologies, coupled with AI, machine learning & predictive analytics to solve these issues. [31] Powered by Q4 Desktop’s AI-engine, iris, AI Targeting leverages deep capital markets intelligence and machine learning capabilities to bring institutional investors and corporate issuers together. [32] Article by Vyacheslav Polonski: “U nless you live under a rock, you probably have been inundated with recent news on machine learning and artificial intelligence (AI). [29] Meet Kate, customer experience artificial intelligence (AI) from Genesys. [30] The Amazon Go convenience store uses AI to track customer purchases, while a growing number of companies are testing self-driving vehicles on public roads. [9] The authors use classic principles from economics to demystify why AI is on the rise, what it means for leaders, businesses, and industries, and how organizations can derive more value from it. [28] Use our AI score to understand precisely when investment in your company becomes an attractive proposition for the highest quality prospective investors. [32] Experience: One solution may be to provide more hands-on experiences with automation apps and other AI applications in everyday situations (like this robot that can get you a beer from the fridge). [29] It found that 65 percent of American adults think that in 50 years, robots and computers “will do much of the work currently done by humans.” 1 A 2018 Brookings survey found that 49 percent on adult online users worry AI will reduce personal privacy and 38 percent fear it will cut the number of jobs. [9] To summarize, there were a number of major spikes in public interest in AI, robots, and automation over the past year. [9] June 13, 2018 – For the 2018 World Cup in Russia, Fuji TV is planning to conduct match predictions and PR activity using Japan’s first AI MC robot to energize the live soccer matches broadcast by Fuji TV. The robot is modeled after Mr. Jon Kabira, a famous Japanese. [33] “AI is best understood as a tool to lower the cost of prediction. [28]

Control: Lastly, creating more of a collaborative decision-making process will help build trust and allow the AI to learn from human experience. [29] Of course, many decisions in our lives require a good forecast, and AI agents are almost always better at forecasting than their human counterparts. [29] Kate brings together AI, adaptive learning, bots, cognitive computing, and other Genesys automation technologies. [30] If we want AI to really benefit people, we need to find a way to get people to trust it. [29] To do that, we need to understand why people are so reluctant to trust AI in the first place. [29] A similar practice for AI systems could help people have a better understanding of how algorithmic decisions are made. [29] In our work at Avantgarde Analytics, we have also found that involving people more in the AI decision-making process could improve trust and transparency. [29] Many people are also simply not familiar with many instances of AI actually working, because it often happens in the background. [29] Thanks to a number of advances, both technological and algorithmic, a new wave of AI applications is forming, turning what once belonged exclusively to futuristic sci-fi or dystopian stories into commoditized, ubiquitous technology. [27] Simply having previous experience with AI can significantly improve people’s attitudes towards the technology, as we found in our experimental study. [29] We predict that mission-driven organizations will initially adopt AI to build. [33] No AI will pick up the phone and have the ability to talk to a potential customer and close a deal. [27] From optimizing channel mix to helping with experimentation, AI allows marketers to spend more time thinking about their customers and less time tinkering with campaign optimization. [27] AI Targeting takes into account both the buying/selling probability of investors, as well as their overall quality, to uncover the most ideal investors, at exactly the right time. [32] The real-time nature of AI Targeting enables you to foster relationships with the highest quality investors at precisely the right time. [32] These guidelines (experience, insight and control) could help making AI systems more transparent and comprehensible to the individuals affected by their decisions.( More )”. [29] Sketch to product: Going from idea to prototype with the help of AI almost immediately empowers designers and products managers to test their hypothesis faster, leading to a highly efficient product-development cycle. [27] That is not to say that AI won’t transform our economy and job market. [27] AI is still unable to handle complex support cases involving multiple pieces of information. [27]

How can a company claim that they have a good model of leadership potential if they fail to predict how leaders will perform in the future? How can organizations be satisfied with their learning, training, and development programs if they cannot predict the ROI of such interventions? For the most part such predictions are rarely or never tested unlike science, HR tends to subject its claims to rare falsification. [28] You will need to own this loop, and know its value not just as a tool to improve your organization, but also as an asset to enhance future predictions. [28] Workforce planning is the prediction of future talent needs in your business or industry, or a bet on how the job market will change. [28] ABOUT: Tenth episode of Innovate UKs Prediction YouTube series on the future of Robots and Artificial Intelligence. [34]

Over two days of congressional hearings, Facebook CEO Mark Zuckerberg answered questions about how his company handles user data and what changes will prevent unauthorized sharing of data in the future. [9] The ability to draw the right conclusions from the insights provided, and suggest the right interventions, will be highly contextual to each organization, and a key driver of your future business value. [28] Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future. [31] Prior to Incandescent, Darko was a Fellow of the World Economic Forum, where he worked with public and private sector leaders to understand our changing world, including deeper examinations of the impact of digital technologies on other industries, the future of health, the dynamics of resource scarcity, and the political realities of the Arab world. [28]

Recruiting is about predicting candidate’s performance in a certain job (not in absolute terms, but rather, compared to to other existing candidates). [28]

RANKED SELECTED SOURCES(34 source documents arranged by frequency of occurrence in the above report)

1. (23) About predicting the future – Elements of AI

2. (16) The Future of Data Science & Predictive Modeling in Business

3. (15) AI: Overhyped and Misunderstood: 10 Marketing Predictions on The Future of AI | GreenBook

4. (15) AI software for marketing & sales | Conversica | Convert more leads into opportunities

5. (12) AI trust and AI fears: A media debate that could divide society – The Governance Lab @ NYU

6. (12) Ten Ways AI Drives the Future of Big Business

7. (11) Valuing the Artificial Intelligence Market, Graphs and Predictions

8. (10) Can HR Become a Prediction Machine? Rethinking AI and Talent | HR Examiner

9. (10) The Future of Artificial Intelligence: Is Your Job Under Threat? – DZone AI

10. (9) Assessing the role AI will play in your companys future | VentureBeat

11. (8) How AI Is Making Prediction Cheaper

12. (7) The Future of IT: IoT, AI, and Machine Learning | KORE1

13. (7) Why human-AI collaboration will dominate the future of work – TechRepublic

14. (6) Views of AI, robots, and automation based on internet search data

15. (5) Predicting The Future: Can Machine Learning Help? Yes.

16. (4) 5 Predictions for Artificial Intelligence in 2016 |

17. (4) Q4 Inc. – Products – Intelligence – AI Targeting

18. (3) Looks aren’t so deceiving: AI could predict your next move from watching your eye gaze

19. (3) Customer Experience AI for Service, Sales & Marketing | Meet Kate

20. (2) Artificial Intelligence is Set to Explode. Is Your Data up to the Task?

21. (2) [Webinar Recording] How Social Listening Data Can Predict the Future. Findings from New Ground-Breaking US Consumer Packaged Goods Study | Webinar |

22. (2) From Sourcing to Hire: Artificial Intelligence (AI) in Recruiting « Zoho Blog

23. (2) AI & Robots | Future Timeline | Blogs | Technology | Singularity | 2020 | 2050 | 2100 | 2150 | 2200 | 21st century | 22nd century | 23rd century | Humanity | Predictions | Events

24. (2) Predicting future IT outages using AI | App Developer Magazine

25. (2) FixStream Launches Industry’s First Visual Artificial Intelligence Platform To Predict Business

26. (2) AI, Autonomous Vehicles Predictive Analytics: How Weather Telematics? Technology Can Save Lives | Internet of Things Inc.

27. (2) A pilot study indicates that artificial intelligence may be useful in predicting which students are at higher risk of perpetrating school violence – Innovation Toronto

28. (1) As five VCs predict the future, robotics and AI rise to the surface – PE Hub

29. (1) Definition of Artificial Intelligence (AI) – Deeplearning4j: Open-source, Distributed Deep Learning for the JVM

30. (1) Artificial intelligence can predict the future – and it looks grim |

31. (1) How Social Listening Data Can Predict the Future. Findings from New Ground-Breaking US Consumer Packaged Goods Study — I-COM

32. (1) Experts’ Predictions about the Future of AI [x-post from /r/aivideos] : artificial

33. (1) Using AI To Predict Heart Attacks 18 Months Out – DZone AI

34. (1) Innovate UK Predictions – Robotics & AI on Vimeo

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