IDSC Quiz 6

Central Repository - Data Warehouse

".....large amounts of data stored in repositories." Before the data can be analyzed, the data has to be typically organized into repositories, known as data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. A data warehouse is a collection of data, extracted, structured and designed for querying, decision making, applying Business Intelligence and for data mining. Data warehousing requires: integration of multiple sources of data involving multiple formats multiple database systems databases cleaning the data creating a logical view of the results

For Many Facebook Is The Internet

"A minority of users are seeking to exploit Facebook as a platform to undermine democracy and incite offline violence." •The independent report, commissioned by Facebook, said the platform had created an "enabling environment" for the proliferation of human rights abuse. •It comes after widespread violence against the Rohingya minority which the UN has said may amount to genocide. •Facebook has more than 18 million users in Myanmar. For many, the social media site is their only way of sharing news. •Last year, the Myanmar military launched a violent crackdown in Rakhine state after Rohingya militants carried out deadly attacks on police posts. •Thousands of people died and more than 700,000 Rohingya fled to neighboring Bangladesh. There are also widespread allegations of human rights abuses, including arbitrary killing, rape and burning of land. •The Rohingya are seen as illegal migrants in) Myanmar and have been discriminated against by the government and public for decades. •The 62-page independent report from non-profit organization Business for Social Responsibility (BSR) found that the platform "has become a means for those seeking to spread hate and cause harm" in Myanmar.

Business/(Big)Data Analytics

"The use of math and statistics to drive meaning from data in order to make better business decisions." 3 types: Descriptive ( for example, dashboards) Predictive (use past data to model future outcomes) Prescriptive (optimization--how best to do your job)

Business analytics

is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user.

Changes Of This Magnitude Require Leadership

•"This is something I got wrong," admits Jeff Immelt, the CEO of GE. "I thought it was all about technology. I thought if we hired a couple thousand technology people, if we upgraded our software, things like that, that was it. I was wrong. Product managers have to be different; salespeople have to be different; on-site support has to be different." •To make sense of those new streams of data, the company is building a cloud-based platform which combines its own information flows with customer data and submits them to analytics software that can lower costs and increase uptime through vastly improved predictive maintenance. •Getting this right will require hiring several thousand new software engineers and data scientists, retraining tens of thousands of salespeople and support staff, and fundamentally shifting GE's business model from product sales coupled with service licenses to outcomes-based subscription pricing. • A global airline stitched together data from multiple operational systems to identify more precisely when and why flights were delayed as they pushed back or arrived at a gate. Its advanced prediction algorithms were able to quantify the knock-on impact of events such as mishandled luggage and helped build a system to alert supervisors in real time so that they could react before potential problems developed. Impact: a reduction in delayed arrivals of about 25 percent over the past 12 months. 1. Do we have a value-driven analytics strategy? •Businesses can waste a lot of energy collecting data and mining them for insights if their efforts aren't focused on the areas that matter most for the company's chosen direction. •Successful big data and advanced-analytics transformations begin with assessing your own value drivers and capabilities and developing a picture of the ideal future state, one aligned with the broad business strategy and key use cases. Asking the right questions is the critical first step.

ML Is Driving Changes At Three Levels

1.Tasks and occupations, An example of task-and-occupation redesign is the use of machine vision systems to identify potential cancer cells — freeing up radiologists to focus on truly critical cases, to communicate with patients, and to coordinate with other physicians. 2.business processes, An example of process redesign is the reinvention of the workflow and layout of Amazon fulfillment centers after the introduction of robots and optimization algorithms based on machine learning. 3.Business models - business models need to be rethought to take advantage of ML systems that can intelligently recommend music or movies in a personalized way. Instead of selling songs à la carte on the basis of consumer choices, a better model might offer a subscription to a personalized station that predicted and played music a particular customer would like.

Data, Stages, Change

2. Do we have the right 'domain data' to support our strategy?•In answering such questions, companies typically identify 10 to 20 key use cases in areas such as revenue growth, customer experience, risk management, and operations where advanced analytics could produce clear-cut improvements. •A critical foundational step is to overcome obstacles to using existing data. This work could include cleaning up historical data, integrating data from multiple sources, breaking down silos between business units and functions, setting data-governance standards, and deciding where the most important opportunities may lie to generate new internal data—adding sensors 3. Where are we in our journey? Like any transition, the data-and-analytics journey takes place in stages. It's crucial both to start laying the foundation and to start building analytics capabilities even before the foundation is set. The key is to move quickly from data collection to "doing the math," with an iterative, hypothesis-driven modeling cycle. Rapid successes help break down silos and build enthusiasm and buy-in among often skeptical frontline managers. 4. Are we modeling the change personally?•In a recent survey of more than 500 executives, we turned up a distressing finding: while 38 percent of CEOs self-reported that they were leading their companies' analytics agendas, only 9 percent of the other C-suite executives agreed. They instead identified the chief information officer or some other executive as the true point person. 5. Are we organizing and leading for analytics?•The most important shift, is to put advanced analytics at the center of every core process.

Examples!

A Midwest grocery chain used data mining software to analyze local buying patterns.They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer.Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, they only bought a few items.The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain used this newly discovered information in various ways to increase revenue: -- move the beer display closer to the diaper display! --make sure beer and diapers were sold at full price on Thursdays!

Tech Titans Join Forces to Prevent AI from Behaving Badly

A new organization called the Partnership on Artificial Intelligence to Benefit People and Society will seek to foster public dialogue and create guidelines for developing AI so that systems do not misbehave. That such fierce rivals would come together in this way shows how important the companies feel it is to head off public concern and speculation over the potential impacts of AI. These businesses are all reaping huge rewards from advances in AI, and they do not wish to see their industry subject to strict government regulation. "We all share a duty to take the field forward in a thoughtful and positive and, importantly, ethical way,""The positive impacts of AI will depend not only on the quality of our algorithms, but on the level of public engagement, of transparency, and ethical discussion that takes place around it."

Benefits and Uses of AI

AI can improve efficiency in the back office when applied to routine activities based on well-defined rules, procedures and criteria. Human-assisted AI has also taken big steps towards helping business solve big data problems. Extracting data is not the challenge anymore. Now, the challenge lies in making that data accurate and actionable. Chat Bots! Distribution is key to content marketing success, bots are quickly proving to be an attractive instrument to engage and communicate with a desired audience.A second rapidly growing use case for chatbots is customer service. AI-assisted bots can seamlessly handle and process mounds of frequently asked questions in real time without any human input.

Key Points

AI is a still evolving set of technologies that will significantly change business, culture and the world we live in. There are many benefits and uses for AI in business already in place but even more will come in the future. There are legitimate concerns about the use and expanded power of AI, but it is important not to panic. AI will change the methods and roles for business leadership

Benefits and Uses of AI

AI is being used today in: Video Games Smart Cars Retail Purchase Prediction Security Surveillance Virtual Personal Assistants Online Customer Support Music and Movie RecommendationSmart Home Devices Fraud Detection News Generation

Effective business intelligence needs to meet four major criteria:

Accuracy - This refers to the accuracy of the data inputs as well as outputs. Any system that requires analysis can fall prey to the garbage in, garbage out problem. This is why human discretion is often used to select the data that is relevant. Timeliness - Business intelligence must also be able to deliver those insights at the right time. There are two parts to timeliness - the timeliness of the data going in and the timeliness of the insights coming out. Businesses have different decision time frames depending on what they do. Valuable Insights - Not all insights are valuable. Actionable - The final step is to provide insights that can be acted upon. Business intelligence should provide insight and within a company's unique constraints to deliver actionable ideas designed to improve a business's processes and profitability. BI is only effective if it is trusted and used to guide human decisions.

Facebook - Hate Speech In Myanmar

Although the company is working to stop it, hate speechcontinues to permeate Facebook in Myanmar, which is plagued by ethnic violence.2 •Facebook became the primary means of communication in Myanmar (also called Burma) around 2013, when SIM card prices dropped sharply, and mobile carriers started offering free data for use of the social network. •Soon after, the platform also became the easiest way to spread racist and incendiary messages and encourage violence between Buddhists and Muslims. •According to Reuters, Facebook ignored years of warnings from researchers and activists about the scale of harm the platform was causing in Myanmar. "It couldn't have been presented to them more clearly, and they didn't take the necessary steps." •Despite claiming progress and hiring numerous Burmese-speaking employees, it still struggles with the language and relies heavily on user reports to find and remove damaging content. •Reuters uncovered more than 1,000 pieces of such content attacking Myanmar Muslims were still up on the site — including posts, images, and comments. Some had been posted six years ago. •The report predicts that the Myanmar's 2020 elections will present a substantially increased human rights risk, and Facebook needs to prepare for that eventuality.

Should We Really Be Worried?

As Artificial Intelligence Evolves, So Does Its Criminal Potential Imagine receiving a phone call from your aging mother seeking your help because she has forgotten her banking password. Except it's not your mother. The voice on the other end of the phone call just sounds deceptively like her. It is actually a a tour-de-force of artificial intelligence technology that makes it possible for someone to masquerade via the telephone. Social engineering, which refers to the practice of manipulating people into performing actions or divulging information, is widely seen as the weakest link in the computer security chain. Cybercriminals already exploit the best qualities in humans — trust and willingness to help others — to steal and spy. The ability to create artificial intelligence avatars that can fool people online will only make the problem worse. Some computer security researchers believe that digital criminals have been experimenting with the use of A.I. technologies for more than half a decade.The irony, of course, is that this year the computer security industry, with $75 billion in annual revenue, has started to talk about how machine learning and pattern recognition techniques will improve the woeful state of computer security.

Different methods of Analysis

Clustering: recognizing distinct groupings or sub-categories within the data. Classifying: An example of classifying is to examine a customer as credit worthy or credit unworthy. Estimating and Predicting : Estimating and predicting are two similar activities that normally yield a numerical measure as the result. From the set of existing customers we may estimate the overall indebtedness of the candidate customer. Affinity Grouping: Affinity grouping is a special kind of clustering that identifies events or transactions that occur simultaneously. A well-known example of affinity grouping is market basket analysis.

Data Mining: Two Major Forms

Discovering information from data takes two major forms: description and prediction. Data mining is used to simplify and summarize the data in a manner that we can understand...The patterns detected and structures revealed by the descriptive data mining...If an algorithm can correctly classify a case into known category based on limited data... ...and then allow us to infer things about specific cases based on the patterns we have observed. ...are then often applied to predict other aspects of the data....it is possible to estimate a wide-range of other information about that case based on the properties of all the other cases in that category. Amazon offers a useful example of how descriptive findings are used for prediction. The hypothetical association between purchases, is used, along with many other similar associations, as part of a model predicting the likelihood that a particular customer will make a particular purchase. This model could match all such associations with a user's purchasing history, and predict which products they are most likely to purchase. Amazon can then serve ads based on what that user is most likely to buy.

There are Concerns about AI

Four Primary Concerns about Artificial Intelligence The adverse impact of AI on labor Important decisions delegated to AI systems Lethal autonomous weapon systems "Superintelligence": the risk of humanity losing control of machines There are concerns over the potential for AI and related technologies such as robotics to displace people and increase inequality. There will be ethical conundrums involving AI. For instance, it may prove challenging to devise systems that take differing ethical perspectives into account. AI is advancing at such a breakneck speed that there are concerns about it being deployed in ways that have unintended or unwanted effects. For instance, a machine-learning system designed to identify disease that is fed biased data might discriminate against certain people.

China Has Developed A Virtual Anchor

Hong Kong (CNN Business) News Anchors, beware: the robots are coming for your jobs, too. •China's Xinhua state news agency has debuted a virtual anchor designed to deliver news 24 hours a day. •Developed by Xinhua and Chinese search engine company Sogou, the anchor was designed to simulate human voice, facial expressions and gestures. •The news agency said the simulations can be used on its website and social media platforms and will "reduce news production costs and improve efficiency." •The English-speaking anchor, complete with a suit and tie, is modeled on a real-life Xinhua anchorcalled Zhang Zhao. •"I will work tirelessly to keep you informed as texts will be typed into my system uninterrupted," it said in an introductory video. •A Chinese-language version, which is based on another real-life Xinhua anchor, was also unveiled at the conference.

Paris Call for Trust and Security in Cyberspace

Last week at the UNESCO Internet Governance Forum , the French President launched the Paris Call for Trust and Security in Cyberspace: a call to tackle new threats together, emphasizing the growth in cybercrime and malicious activity can also endanger both our private data and certain critical infrastructures. •The idea is that signatories will adhere to a set of common principles, such as agreeing to: -protect the accessibility and integrity of the Internet; -stop cyberattacks on critical infrastructure like electrical grids and hospitals; -combat intellectual-property theft online; -improve the security of digital products and services and everybody's "cyber hygiene"; -increase prevention against and resilience to malicious online activity; -cooperate in order to prevent interference in electoral processes; -prevent the proliferation of malicious online programs and techniques; -outlaw the use of cyber mercenaries and offensive action by non-state actors to hide the real culprits behind attacks; and -work together to strengthen the relevant international standards.

How Al Will Become Weaponized In Future Cyberattacks

Last week, researchers from Darktrace said that for each sophisticated cyberattack currently in use, there is the potential for future use of Artificial Intelligence. •"We expect AI-driven malware to start mimicking behavior that is usually attributed to human operators by leveraging contextualization. But we also anticipate the opposite; advanced human attacker groups utilizing AI-driven implants to improve their attacks and enable them to scale better." •Trickbot is a financial Trojan which uses a Windows vulnerability EternalBlue to target financial institutions. Darktrace believes, malware bolstered through artificial intelligence will be able to self-propagate and use every vulnerability to compromise a network. •"Imagine a worm-style attack, like WannaCry, which, instead of relying on one form of lateral movement (e.g., the EternalBlue exploit), could understand the target environment and choose lateral movement techniques accordingly," •If chosen vulnerabilities are patched, the malware could then switch to brute-force attacks, keylogging, and other techniques which have proven to be successful in the past in similar target environments. •"Instead of guessing during which times normal business operations are conducted, it will learn it," the report suggests. "Rather than guessing if an environment is using mostly Windows machines or Linux machines, or if Twitter or Instagram would be a better channel [...] it will be able to gain an understanding of what communication is dominant in the target's network and blend in with it."

AI, Machine Learning, Deep Learning

Machine learning is the science of giving computers the ability to learn and find insights without explicitly programming the computers on what to do.

Examples!

Proctor and Gamble:Started the Business Sufficiency program, which gives predictions about P&G market share and other performance six to 12 months into the future.The "what" models focus on data such as shipments, sales, and market share. The "why" models highlight sales data down to the country, territory, product line, and store levels, as well as drivers such as advertising and consumer consumption, factoring in region- and country-specific economic data. "Actions analyses" looked at levers P&G can pull, such as pricing, advertising, and product mix, and provide estimates on what they deliver. Decisions that used to require up to a month of data gathering and research can now be made within a day. Richmond, Va. police department: Police had a mass of data from 911 calls and crime reports; what they didn't have was a way to connect the dots and see a pattern of behavior.Using sophisticated data mining software and hardware they started overlaying crime reports with other data, such as weather, traffic, sports events and paydays for large employers. Something interesting emerged from the data: Robberies spiked on paydays near check cashing storefronts in specific neighborhoods. Other clusters also became apparent, and pretty soon police were deploying resources in advance and predicting where crime was most likely to occur. Major crime rates dropped 21 per cent from 2005 to 2006. In 2007, major crime is down another 19 per cent.

Should we really be worried?

Take fears about AI seriously The transition to machine superintelligence is a very grave matter, and we should take seriously the possibility that things could go radically wrong. This should motivate having some top talent in mathematics and computer science research the problems of AI safety and AI control. —Nick Bostrom, director of the Future of Humanity Institute, Oxford University My current guesses for the most likely failure modes are twofold: The gradual enfeeblement of human society as more knowledge and know-how resides in and is transmitted through machines and fewer humans are motivated to learn the hard stuff in the absence of real need. Secondly, I worry about the loss of control over intelligent malware and/or deliberate misuse of unsafe AI for nefarious ends. —StuartRussell, computer science professor, UC Berkeley But don't freak out I am infinitely excited about artificial intelligence and not worried at all. Not in the slightest. AI will free us humans from highly repetitive mindless repetitive office work, and give us much more time to be truly creative. I can't wait. —SebastiaThrun, computer science professor, Stanford University We should worry a lot about climate change, nuclear weapons, antibiotic-resistant pathogens, and reactionary and neo-fascist political movements. We should worry some about the displacement of workers in an automating economy. We should not worry about artificial intelligence enslaving us. —Steven Pinker, psychology professor, Harvard University

What does Business Intelligence Offer?

The goal of BI is to help managers make more informed and better decisions to guide the business. BI software refers to a variety of vendor applications used to analyze data from a variety of sources. (BIG DATA) With today's BI software, functional staff can jump in and start analyzing data themselves, rather than wait for IT to run complex reports. This democratization of information access helps users make business decisions with data that would otherwise be based only on gut feelings and anecdotes.What does Business Intelligence Offer? Companies are able to determine relationships among "internal" factors such as price, product positioning, and "external" factors such as economic indicators, competition, and customer demographics. Using BI, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchases history. It's not enough that BI report sales were X yesterday and Y a year ago that same day. They need to explain what factors influence the business caused sales to be X one day and Y on the same date the previous year. St. PatricksDayat McGoverns Irish Pub?

Key Points

The goal of BI is to help managers make more informed and better decisions to guide the business. Organizations that can harness Big Data Analytics effectively will be able to create significant value and differentiate themselves Data Mining is the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data Data Warehouses are necessary to manage the large amounts of data that can be used for Data Mining, Analytics, and generating Business Intelligence.

Can Software Substitute for the Responsibilities of Senior Leaders in Their Roles at the Top of Today's Biggest Corporations?

The job is going to be to figure out, "Where do I actually add value and where should I get out of the way and go where the data take me?" That's going to mean a very deep rethinking of the idea of the managerial "gut," or intuition. Right now, there are a lot of leaders of organizations who say, "Of course I'm data driven. I take the data and I use that as an input to my final decision-making process."But there's a lot of research showing that, in general, this leads to a worse outcome than if you rely purely on the data. In some activities, particularly when it comes to finding answers to problems, software already surpasses even the best managers. Knowing whether to assert your own expertise or to step out of the way is fast becoming a critical executive skill. Yet senior managers are far from obsolete. Top executives will be called on to create the innovative new organizational forms needed to crowdsource the far-flung human talent that's coming online around the globe. Those executives will have to emphasize their creative abilities, their leadership skills, and their strategic thinking.

The Real Challenge of Business Intelligence

The most important step in successful Business Intelligence isn't about technology, it's about understanding the business. This business phase is focused on gathering requirements and identifying and prioritizing a list of opportunities that can have a significant business impact. Identifying opportunities must be connected to the realities of the data world. By the same token, the data itself may suggest business opportunities.

Data Mining:

The process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques.

Data Mining

The process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques. Data mining allows users to analyze data from many different dimensions, categorize it, and summarize the relationships identified into useful information that typically is not obvious to the user. While descriptive data is important, the most influential variables in a data mining model are typically behavior-based. Companies can be overwhelmed by the amount and access of data being collected, stored and available from multiple sources: Internet, e-commerce, mobile, financial transactions, partners, suppliers, government. Much of the existing data is never analyzed. There are significant opportunities "hidden" in the data. Human analysts may take weeks or months to discover useful information.

Business/(Big)Data Analytics

These technologies could generate productivity gains and an improved quality of life, but they carry the risk of causing job losses and dislocations. Research found that 40-70 percent of work activities could be automated using current technologies.Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage. Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models,and they are actively looking for ways to enter other industries. Where digital natives were built for analytics, legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition. The urgency for incumbents is growing, since leaders are staking out large advantages, and hesitating increases the risk of being disrupted. Disruption is already happening, and it takes multiple forms.

Room for Improvement

Users of China's micro-blogging site Weibo were not completely convinced by the virtual presenter. •"(His) voice is too stiff, and there are problems with the pauses," said one user. •China operates one of the most aggressive media censorship regimes in the world and has tightened restrictions on domestic and foreign news outlets under President Xi Jinping. But that hasn't stopped its newsrooms from innovating. •In 2015, China's Dragon TV used Microsoft's XiaoIce chatbot to deliver a weather report on its live breakfast show. The AI computer program delivered the forecast in a "cute" female voice, according to Xinhua. •Automated reporting has proliferated in recent years. The AP wire service uses sophisticated computer algorithms to write thousands of automated stories a year. •Advanced software programs scrape sources like corporate earnings reports and baseball box scores and then transform the data into sentences that humans can understand.

Accenture defines artificial intelligence (AI)

as a collection of multiple technologies that together enable machines to sense, comprehend, act and learn, either on their own or to augment human activities.

Data Analytics

has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. Whatever the use cases, "analytics" has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data.

Business intelligence (BI)

is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

AI vs Humans

•AI - tasks that are hard for humans are easy for computers, and vice versa. •The simplest computer can run rings around the brightest person when it comes to wading through complicated mathematical equations. •At the same time, the most powerful computers have, in the past, struggled with things that people find trivial, such as recognizing faces, decoding speech and identifying objects in images. •One way of understanding this is that for humans to do things they find difficult, such as solving differential equations, they have to write a set of formal rules. Turning those rules into a computer program is pretty simple. For stuff human beings find easy, though, there is no similar need for explicit rules—and trying to create them can be hard. •To take one famous example, adults can distinguish pornography from non-pornography. But describing how they do so is almost impossible, as an American Supreme Court judge, discovered in 1964. Frustrated by the difficulty of coming up with a legally definition, he threw up his hands and wrote that, although he could not define porn in the abstract, "I know it when I see it." •Machine learning is a way of getting computers to know things when they see them by producing for themselves the rules their programmers cannot specify. The machines do this with heavy-duty statistical analysis of lots and lots of data.

What Does It Mean To Become An AI-enabled Company?

•AI will bring about a transformation of a lot of companies and even the rise of new types of companies. Today, we have things called Internet companies. The fundamental thing that defines an Internet company is not whether you operate a website. It's whether you have architected your whole company to leverage the new capabilities the Internet gives you. •With the rise of AI, we're still figuring out how to architect our companies to leverage AI capabilities. Just as building a website doesn't make you an Internet company, sprinkling machine learning doesn't make an AI company. •AI is affecting how companies organize themselves. Old job descriptions—engineer, product manager, designer—are breaking down. For example, if you look at any mobile app, there was probably a product manager that drew a simplified diagram called a wireframe to design that app. But if you look at the self-driving car, you don't need a wireframe for a self-driving car. That just doesn't make as much sense. So we're inventing brand-new processes and workflows for the AI era as well. •Today, AI talent is scarce, and even more scarce is the skill to take the AI technology and figure out how to take it to market. •What can be very effective is to have executives send a very clear message that employee AI development is valued. •Thanks to the rise of online education, AI talent is disseminating very rapidly. There are tons of resources on the Internet, but to use resources like that to level up your whole employee base, that would make your whole organization more effective at maybe working with your centralized AI organization.

AI's Future Role in Cyberattacks

•Darktrace uncovered malware from a medical technology company. What made the findings special was that data was being stolen at such a slow pace and in tiny packages that it avoided triggering data volume thresholds in security tools. •By fading into the background of daily network activity, the attackers were able to steal patient names, addresses, and medical histories. •AI could not only provide a conduit for incredibly fast attacks but also "low and slow" assault, and be used to learn what data transfer rates would flag security solutions. •Instead of relying on a hard-coded threshold, for example, AI-driven malware would be able to dynamically adapt data theft rates and times to exfiltrate information without detection. •"Defensive cyber AI is the only chance to prepare for the next paradigm shift in the threat landscape when AI-driven malware becomes a reality," the company added. "Once the genie is out of the bottle, it cannot be put back in again."

Countries Conspicuously Absent

•In order to respect people's rights and protect them online as they do in the physical world, States must work together, but also collaborate with private-sector partners, the world of research, and civil society. •This high-level declaration has already received the backing of more than 50 States, including all European Union members. •It's also been endorsed by civil society organizations and private companies, including tech giants like Microsoft and Facebook. •The US, Russia, and China, which all have huge cyber offensive capabilities, haven't signed up — presumably because they don't want to have their hands tied.

Neural Networks Immensely Complex

•Neural networks were invented in the 1950s by researchers who had the idea that, though they did not know what intelligence was, they did know that brains had it. •Brains do their information processing not with transistors, but with neurons. If you could simulate those neurons then some sort of intelligent behavior might emerge. •In the past few years, the remarkable number-crunching power of chips developed for the demanding job of drawing video-game graphics has revived interest. •Early neural networks were limited to dozens or hundreds of neurons, usually organized as a single layer. The latest, used by the likes of Google, can simulate billions. •Big internet companies like Baidu, Google and Facebook sit on huge quantities of information generated by their users. •The people who run those firms know that these data sets contain useful patterns, but the sheer quantity of information is daunting. It is not daunting for machines, though. •The problem of information overload turns out to contain its own solution, especially since much of the data come helpfully pre-labelled by the people who created them. •Fortified with the right algorithms, computers can use such annotated data to teach themselves to spot useful patterns, rules and categories within.

AI is Truly Global

•Singapore - was one of the first countries to announce a national strategy, called A.I. Singapore, in May 2017. The initiative brings the government, research institutions and companies together to collaborate on research and speed up local adoption of A.I. Singapore has a head start in autonomous vehicles: It had the first self-driving taxis for use by the public and built a mini-town for further testing. •Israel- is becoming a world leader in medical A.I. with dozens of new health care start-ups. The government announced a five-year program with a budget of $280 million to digitize patient data and use A.I. to gather important insights, with hopes of turning the homegrown expertise into consumer products that could make Israel an industry leader. •India - released its A.I. strategy only this summer, but it contains a big idea that could catch them up: become the "garage" that develops A.I. that creates economic growth and social development for themselves and the rest of the developing world. The plan, will focus on projects around health care, agriculture, education, smart cities and infrastructure, and smart mobility and transportation. •France - the French government released a 150-page document earlier this year that spells out its A.I. efforts around the health, environment, transportation and security sectors, and is putting $2 billion into funding projects around those areas. •Canada - two of the four "godfathers" of the current A.I. boom, live, work, and teach in Canada. Their efforts have helped spur major research and an A.I. industry there, including offices for Uber, Facebook and Google. The current immigration restrictions in the United States have also sent talented international researchers to Canada instead of Silicon Valley.

Digitization, Data Acquisition & Organization

•The first wave is the digitization wave, where we take things that were analog, or just not in the computer, and digitize it. The digitization revolution in a lot of industries first comes and creates digital data. •After that comes some data science, where you start to get more insights, and then also AI, because it's only after you have the digital data that AI is very efficient in coming in to eat that data to create value. •True AI organizations are much more sophisticated, much more strategic in data acquisition. •If you can just have enough data to launch a product that's good enough, that allows you to enter a positive feedback loop in which your users help you generate more data. More data makes the product even better, so you have more users. And that positive feedback loop allows you to accumulate data, so that maybe after a few years you could have a pretty defensible business. •AI companies tend to organize the data better. So putting data in a centralized data warehouse makes it more efficient for engineers or software to exploit that data. Instead of federated or distributed data sets, we like to bring it together because it's like gunpowder. You put a lot into it to make a big bang.

Putting Machine Learning to Work

•There are three pieces of good news for organizations looking to put ML to use today. •First, AI skills are spreading quickly. The world still has not nearly enough data scientists and machine learning experts, but the demand for them is being met by online educational resources as well as by universities. The best of these, can actually get smart, motivated students to the point of being able to create industrial-grade ML deployments. •Second development is that the necessary algorithms and hardware for modern AI can be bought or rented as needed. Google, Amazon, Microsoft, Salesforce, and other companies are making powerful ML infrastructure available via the cloud. The cutthroat competition among these rivals means that companies that want to experiment with or deploy ML will see more and more capabilities available at ever-lower prices over time. •The final piece of good news is that you may not need all that much data to start making productive use of ML. The performance of most machine learning systems improves as they're given more data to work with, so it seems logical to conclude that the company with the most data will win. That might be the case if "win" means "dominate the global market for a single application such as ad targeting or speech recognition." But if success is defined instead as significantly improving performance, then sufficient data is often surprisingly easy to obtain.

Autonomous Weapons

•Weapons using artificial intelligence (AI) to identify and kill humans should be banned under international law, United Nations general secretary António Guterres has warned. He sees the weaponization of AI as "a serious danger". •"Machines that have the capacity to select and destroy targetswill create enormous difficulties to avoid escalation in conflict and to guarantee that humanitarian law and human rights law are respected in the battlefield." •"Machines that have the power and the discretion to take human lives are politically unacceptable, are morally repugnant and should be banned by international law," he said. •Improvements in AI are making autonomous weapons that could recognize and attack human targets a plausible prospect in the near future.