Someday just to stay ahead of the pack you’ll begin shaping your children’s education with Smart Tools, Smart phones, Smart Tablets, Smart Teachers. Will universities be put out of business? Will your children skip public education in favor of Smart Education Technologies, AI’s – tools that follow them 24/7 and provide instantaneous at their fingertip interactive learning – on the go, mobility? The future of education may be interactive Smart Systems that can gauge, select, identify, and create an innovative tailor made curriculum with personal automated e-Teachers – who will not only babysit your child, but keep them learning and progressing according to their natural (notice – IQ level) level of learning capability all through their lives without the aid of educational institutions or human teachers. Home schooling on steroids? Career learning 24/7 non-stop? The age when we compete with machines for jobs? A brave new world is slowly rising around us in the ruins of the human empire. A new empire of machines is drifting into replace us and we seem hell bent on giving them the keys to the mechanosphere.
Will the world even need us in this new brave future of intelligent machines, multi-agent systems, and robotics?
The future of education is one are that intelligent agents are taking over the human equation. As one article tells us the real point here is that custom-tailored lesson plans are well within the capability of smart technologies. The real challenge is for schools to decide to begin using smart technologies in new and innovative ways. The ideal is to combine the insights of each of these learning styles—and others—as each supplies its own part of the complete picture via social networks and team-learning approaches. While some researchers say that classroom productivity has not really risen with increased computer-driven learning, this learning has not been especially innovative to date. Instead, it has been a case of simply putting old ideas into new bottles.
Smart phones and smart tablets are set to invade your child’s learning 24/7 as funtime algorithms follow your child throughout the day. According to Adobe, “Internet users view 70% more pages per visit when browsing on a tablet vs. a smartphone.” Online educators might see a correlation in the popularity of massive open online courses (MOOCs) for the same period. As the general population, especially college-age students, has become more engaged with technology, MOOCs have found a platform for growth. While it is unlikely that the growth of MOOCs has affected mobile-device adoption, it is likely that MOOCs have grown larger and faster because of mobile technology.
What might the data-driven educational models of the future look like and how might they serve us better? Are there looming threats or opportunities for which we can prepare? As Kristin Garn tells us digital data implies complexity and, in “self-organizing” processes (like our own cognitive construction of meaning), this produces emergence. Emergence helps to explain unpredictable and even destructive phenomena like tornadoes and flash floods, as well as wonderfully complex organisms like the human brain, not to mention Twitter and the world wide web. In the very near future, educational data will undoubtedly be combined in the hopes of producing meaningful patterns. The components of this new system will inevitably increase (exponentially) with the amount of available data. This will potentially allow for new and unforeseen types of educational models to emerge.
The technological platforms enabling it
Augmented Reality the wave of future education? Technologies like Aurasma will enable this new mobility of smart platforms to breed across the technosphere. Others like SmartAmp stimulate interactivity and collaboration while promoting creativity and learning. Fifteen new technologies already incorporating mobile – on the go learning. Flashnotes allows students to upload their lecture notes and sell them to other students who need more help or resources. The rating system allows the best note takers to get more business and the general pool of knowledge expands as students continue to share their work with one another. Lore a new startup is using a Facebook type platform- riding the wave of what works- and tailoring it for education. This social network allows professors and students to communicate, follow one another, and discuss class work and lectures. Study Blue has created an app that allows students to organize their coursework, store notes and flashcards, and share their materials with other students. Google’s Chromebook may snatch the competition in the lower grade school classrooms. Read more…
Below a demo of the future of automated Smart Education: an in-depth demonstration of Waggle and how Knewton’s adaptive learning technology within Waggle personalizes pathways for students to meet them where they are and help them engage in productive struggle to build perseverance and accelerate learning. Watch it…
is there a downside to all this?
As Nathan Leigh reports in Disruption of Mental Work computer power is becoming exponentially more powerful and AI algorithms being fed Big Data from the Internet of Things will evolve from a Smart Assistants like Siri, into Smart Workers and Smart Bosses and even to Smart Teachers and Doctors within the next 20 years. In one study he discovered that “Over the next 10 years, the work of 110 million to 140 million knowledge workers around the globe may be handled by cognitive robotic process automation systems. his shift to robotic process automation — which digitizes labor through the use of advanced machine intelligence, engagement, analytics, big data, social media, mobile technologies and cloud computing — will change the knowledge worker labor market as we know it.”
In his article the driving force behind Big Data is the new Internet of Things technologies of sensors:
Improvements in cheap sensor technology will make the The Internet of Things(IoT) usage abundant in the near future plus “The Cloud” will become faster, more reliable and accessible from anywhere by any device. 5G is considered key to the IoT and is predicted to arrive in the US in 2020. Download speeds should increase from today’s 4G peak of 150 Mbps to at least 10 Gbps, that’s fast enough to download “Guardians of the Galaxy” in 4 seconds instead of 6 minutes. The response time will also drop from 15 to 25 milliseconds to 1 millisecond with 5G. The Internet will be ubiquitous.
Leigh quotes the author Cory Doctorow: “General-purpose computers have replaced every other device in our world. There are no airplanes, only computers that fly. There are no cars, only computers we sit in. There are no hearing aids, only computers we put in our ears. There are no 3D printers, only computers that drive peripherals. There are no radios, only computers with fast ADCs and DACs and phased-array antennas.”
New speech technologies are taking over every aspect of customer and service industries and support: “Automated intelligent assistants are already hard at work doing customer support, sales, marketing, retail, healthcare, utilities, education, and hospitality, the AIAs are designed to recognize real-world implementations that are the pinnacle of real-time natural language understanding, knowledge management, machine learning, and conversational technologies.”
Even the corporate bureaucrat and middle managers will take a large hit in the near future automated economy of smart technologies: “We should no longer expect traditional job ladders for managers to move up the ranks, or even retaining the notion that middle managers are the glue that connects workers and ensures goal alignment up and down the hierarchy. This is different. Rather than managerial “rules of thumb” to guide such decision-making, real data based on past behaviours has become remarkably effective at predicting what we like to consume. All of these changes — technology, business culture, and demographics — add up to a world where middle managers will be less valued, and less needed.”
Leigh sees a dark side ahead in such technologies as Google DeepMind which recently was able to learn to play Atari games and control it’s “player” to get high scores. This may be really basic and possibly naive of me but imagine in 20 years time, could a business use a similar training model, have profits as a high score, employees as the player, run simulations to learn the best strategy and then use it’s operational visibility from all it’s sensors to control the human “players” to achieve an optimised goal in a real situation?
Read the rest of his article which goes into depth on the disruptive potential of these new technologies.
Why Big Data will actually open up more jobs for humans
The fatal problem in Big Data is simple, there is more data being generated and at an unprecedented rate in the world, faster than they we can process it. Most of this data is unstructured and reside in silos of distributed and disconnected systems. Getting access to this data and using them in advanced analytics is going to be critical in improving care and outcomes in every phase of our civilization. Commercial and Governmental organizations can leverage big data technology to capture and process complete information about a person or discover patterns in historical data to improve our lives or control them. Big data is about population management and machinic engagement/outreach in the process of technological data processing.
As Ioana Cerasella Chis in an article Big Data: A Technology of Anxiety relates the accumulation of information cannot be equated with the growth of knowledge, as the data captured by new technologies is used for private ends: the accumulation of capital and control by a small elite. As such, new data can be gathered about subjectivities, bodies and performance, and used to further pressure individuals to fit into preconceived frameworks and identities as a result of the top down creation of individualized pseudo-problems which can cause anxiety, especially when they are not tackled collectively.
The rise of technocracy
One article even praises our age of the technocrat. That what we’re seeing from Washington to Frankfurt to Rome, technocrats have stepped in where politicians feared to tread, rescuing economies or at least propping them up. Technocrats are in vogue within the intelligentsia, too. In a new study, “Economics Versus Politics: Pitfalls of Policy Advice,” by Acemoglu and Robinson, authors of Why Nations Fail, will be published later this year in the Journal of Economic Perspectives. What they suggest is that the driving factors behind policy decisions in both the United States and in Russia, the reforms that strengthened powerful vested interests didn’t begin as a cunning plot by a wealthy cabal. Instead, they were endorsed and advocated by today’s high priests, the technocrats and intellectuals, who sincerely believed they were acting in the common interests of society. He reports an interview with a Russian oligarch who once told him he’d been prepared to pay a bribe to influence the privatization process in his favor. But, he delightedly recalled, he soon discovered that all he needed to do was explain that the policy would further the cause of market reforms in Russia. Then, as he put it, “like little darlings,” the technocrats in charge hastened to put it into action.
The age of predictive analytics
One of the dark sides of Big Data is the application of predictive analytics to people’s careers — an emerging field sometimes called “people analytics”—is enormously challenging, not to mention ethically fraught. And it can’t help but feel a little creepy. It requires the creation of a vastly larger box score of human performance than one would ever encounter in the sports pages, or that has ever been dreamed up before. To some degree, the endeavor touches on the deepest of human mysteries: how we grow, whether we flourish, what we become. Eric Siegel reveals the power and peril of predictive analytics. Siegel, a former Columbia University professor, describes in his book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, how government, law enforcement, hospitals, and businesses use technology to track and predict the behavior of everyone, everywhere. Predictive analytics is the science that turns raw data into useful information. As he tells it:
“Data embodies a priceless collection of experience from which to learn. Every medical procedure, credit application, Facebook post, movie recommendations, spammy e-mail, and purchase of any kind — each positive or negative outcome, each successful or failed event or transaction — is encoded as data and warehoused,” Siegel explained. “As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold — data in its raw form is boring crud. The gold is what’s discovered therein. With the new knowledge gained, prediction is possible.”
In an interview with The New American they asked “Often, predictive analytics as described in your very informative book is used by law enforcement to identify potential high-crime areas in order to direct limited resources toward those areas. When that technology is turned to people, however, isn’t there a violation of constitutionally protected privacy?”
Eric Siegel: Yes, likely there is. This use would be an injustice. The ethical issue we deal with in cases like the one you’re describing is that humans are trusting computers as an advisor. Humans must decide how and whether to give credence to the information the computer provides.
In conferences such as Predictive Analytics World for Government’s 5th Annual Conference one can see how these new technologies will be used to enforce and regulate the global human arena in the coming years. It’s all about power and profit. In the workshop description we’re told:
Business metrics do a great job summarizing the past. But if you want to predict how customers will respond in the future, there is one place to turn — predictive analytics. By learning from your abundant historical data, predictive analytics delivers something beyond standard business reports and sales forecasts: actionable predictions for each customer. These predictions encompass all channels, both online and off, foreseeing which customers will buy, click, respond, convert or cancel. If you predict it, you own it. The customer predictions generated by predictive analytics deliver more relevant content to each customer, improving response rates, click rates, buying behavior, retention and overall profit. For online applications such as e-marketing and customer care recommendations, predictive analytics acts in real-time, dynamically selecting the ad, web content or cross-sell product each visitor is most likely to click on or respond to.
Over the past couple of years I’ve seen the overload of data analytics following me around the web, offering me tailored deals, news, consumer goods all ubiquitously from tracking algorithms that seem to embed cookies and even other active networked analytics as I speed around the commercial web. I’ve also seen the over use of java-script and new 1001 pop-up adds, slide-in videos, sidebars, flip-top promotions, zinger intercepts that force you to be attentive to some new product before moving on to the actual commercial data set you’re seeking. We’re living in an overload hell of commercial stupidity that forces one to abandon the commercial news, entertainment, informational sites due to browser crashes under the duress of bad code. The web is a disaster scene, a crash world where ads have taken over every inch of the once lively web, and given us nothing but a sink hole of data analytics of distraction and inattention that wastes one’s time and day. Is this the future of Big Data?
Companies like IBM offer governments predictive analytics to solve emergencies, crime, fraud and other threats to society have never been more numerous or intense. With events like minor transit disruptions to ongoing cybercrime to major natural disasters and terrorist attacks, it is a continual race for communities, corporations and countries to prepare for threats, accelerate responses and prevent future harm. It’s big business and all the top networking tech companies are pushing the policy, the technology, the hype to the nth degree.
The minority report as reality
As Wayne Williams describes it in Minority Report could one day be real, thanks to big data and predictive analytics. He spoke to Murali Nadarajah, Global Head of Big Data Analytics for Xchanging, a publicly listed multi-national business technology and services provider, about how organizations today are using predictive analytics, and how the ability to be predictive has — and will continue — to change the business landscape enabling the development of new approaches and products.
In the interview Nadarajah describes predictive analytics as being like traditional analytics with a built-in lens into the future. The proliferation of things like social media and all-things-mobile have massively influenced the speed at which consumers make buying decisions and interact with brands. These forces have created an incredible amount of data, which best-in-class businesses are not only collecting and reporting on, but are identifying causal relationships between data points and thus, predicting future outcomes. And best yet is that there’s an incredible degree of accuracy in it.
As Williams asks “This all sounds very “Minority Report” — the sci-fi movie based in the year 2054 where a specialized law enforcement team is able to arrest people for crimes they haven’t yet committed. Is that where we’re headed?”
MN: We’re not there quite yet, but I can’t say it would be impossible in the future! That type of knowledge could possibly exist, and it would be provided by big data. Although being arrested for pre-determined crimes is still a ways off, there are a number of practical applications of predictive analytics that are in-play today.
Nadarajah goes on to explain that Tonal analysis is an example he finds particularly interesting: “Historically, call centers have measured customer satisfaction based on metrics like average handling time, call abandon rate and customer surveys. With big data, we can actually listen to, and analyze, the tone of a customer’s voice over the course of a call. Based on this, we can gauge whether the customer called in angry and left the call happy, or if they started the call neutral and left unhappy, and so on. This is Tonal Analysis and, when it’s combined with existing customer care reports can provide a much higher level of preciseness that you wouldn’t have had with traditional reporting:
To take this one step further, Tonal Analysis becomes predictive when it’s used by industries like debt recovery. Debt collectors can call an individual, tell them how much they owe, for example, ask a few questions and then, based on the individual’s tone during the call, can predict the likelihood of recovering that debt. This allows the debt collector to focus their efforts on money they have a greater chance of seeing again.
At the end of interview he quips that “before “Minority Report” becomes reality, we can rest assured that we’re not yet living in a world where our decisions are being made by machines, but businesses have clearly gotten smarter about how they use data to be ahead of the curve, and what’s yet to come will most definitely be worth the wait.”
Machines and Decisions: The next step beyond the human?
Artificial Intelligence techniques are increasingly extending and enriching decision support through such means as coordinating data delivery, analyzing data trends, providing forecasts, developing data consistency, quantifying uncertainty, anticipating the user’s data needs, providing information to the user in the most appropriate forms, and suggesting courses of action. This session of the 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems focuses on the use of Artificial Intelligence to enhance decision making.
Most of these new initiatives are concerned with theory, design development, implementation, testing and evaluation of intelligent decision systems. Including decision making theory, intelligent agents, fuzzy logic, multi-agent systems, Bayesian networks, optimization, artificial neural networks, genetic algorithms, expert systems, decision support systems, geographic information systems, case-based reasoning, time series, knowledge management systems, Kansei communication, rough sets, spatial decision analysis, and multi-criteria decision analysis. These technologies have the potential to revolutionize decision making in many areas of management, healthcare, international business, finance, accounting, marketing, military applications, ecommerce, network management, crisis response, building design, information retrieval, and disaster recovery.
multiagent systems, societies, organizations
In the future one will see artificial multiagent systems, artificial societies, or simulated organizations taking over from human agents. These systems are being touted as technologies in computer science which is going to build more and more complex (i.e., large, distributed, open, heterogeneous, and dynamic) information environments in which computing devices and computational processes act as “individuals” rather than just “parts”. The concepts of multiagent systems, artificial societies, and simulated organizations lie at the bottom of a new, emerging paradigm of computation and intelligence dealing with topics such as cooperation and competition; coordination and collaboration; communication protocols and languages; negotiation, consensus development, and conflict detection and resolution; intelligent cognitive activities jointly realized by multiple agents (e.g., distributed problem-solving, planning, learning, and decision making); intelligent cognitive activities emergent and organizational intelligence; intelligent cognitive activities organizational and social structuring and dynamics; decentralized control and management; security, reliability, and robustness.
when the machine decides for us
Sean OHeigeartaigh in a recent article Would you hand over a moral decision to a machine? Why not? Moral outsourcing and Artificial Intelligence tells us one area in which this is causing growing concern is military robotics. The degree of autonomy with which uninhabited aerial vehicles and ground robots are capable of functioning is steadily increasing. There is extensive debate over the circumstances in which robotic systems should be able to operate with a human “in the loop” or “on the loop” – and the circumstances in which a robotic system should be able to operate independently. A coalition of international NGOs recently launched a campaign to “stop killer robots”.
He goes on to report on IBM’s Jeopardy-winning “Watson” for use in medicine. As evidenced by IBM’s technical release this week, progress in developing these systems continues apace (shameless plug: Selmer Bringsjord, the AI researcher “putting Watson through college” will speak in Oxford about “Watson 2.0″ next month as part of the Philosophy and Theory of AI conference).
But what happens when things go wrong? As he describes it Human decision-making is riddled with biases and inconsistencies, and can be impacted heavily by as little as fatigue, or when we last ate. For all that, our inconsistencies are relatively predictable, and have bounds. Every bias we know about can be taken into account, and corrected for to some extent. And there are limits to how insane an intelligent, balanced person’s “wrong” decision will be – even if my moral “outsourcees” are “less right” than me 1 time out of 10, there’s a limit to how bad their wrong decision will be. Yet, this is not necessarily the case with machines:
When a machine is “wrong”, it can be wrong in a far more dramatic way, with more unpredictable outcomes, than a human could. Simple algorithms should be extremely predictable, but can make bizarre decisions in “unusual” circumstances. Consider the two simple pricing algorithms that got in a pricing war, pushing the price of a book about flies to $23 million. Or the 2010 stock market flash crash. It gets even more difficult to keep track of when evolutionary algorithms and other “learning” methods are used. Using self-modifying heuristics Douglas Lenat’s Eurisko won the US Championship of the Traveller TCS game using unorthodox, non-intuitive fleet designs. This fun youtube video shows a Super Mario-playing greedy algorithm figuring out how to make use of several hitherto-unknown game glitches to win (see 10:47).
One can imagine as the years go on that development of such systems will be as they are now full of buggy code and algorithms, that have been pushed out too early with little or not complete testing. Looking at the typical testing cycle I remember my 40 years in software engineering field and all the short cuts, skipped testing procedures, etc. that were skated by due to the pressure of deadlines by management. Over and over bad code is released that will eventually have to be fixed, updated, improved, etc. due to economic and in-house politics. The other side of the coin is the pure control systems like Lockheed Martin, or other large Military-Industrial systems that enforce such top-down control of the software due to the potential risks that other deadly factors are introduced. Because of such tight and enforced rules the technologies used are usually very conservative and years behind current development transformations so that what is innovated and creative is secondary to productive risks.
As Christopher Bishop, a scientist with Microsoft Research Cambridge tells us “we’re a long way from general intelligence,” yet with advances in machine learning, including deep neural networks and probabilistic models, computers can now instantly translate spoken and written conversation, recognize and accurately caption photos, identify faces and be your personal assistant. With “integrative AI,” in which competencies including vision, speech, natural language, machine learning and planning are brought together to create more capable systems, such as one that can see, understand and converse with people.
The political, social, religious, and ethical aspects are barely registering on the human radar, but as AI gets incorporated into high-stakes areas like cars, medicine and defense, industry, education, etc. we’ll be faced with a new brave world that we may realize doesn’t need us anymore. A world where the motto is:
“Let the machine do the thinking for you. Sit back, enjoy the ride!”
Maybe, maybe, we should begin to think about what the future might look like without us in it?