The Effects of Machine Learning

The Effects of Machine Learning

The Effects of Machine Learning

Machine learning, also known as Analytics 3.0, is the latest development in the field of data analytics. Machine learning allows computers  to take in large amounts of data, process it, and teach themselves new skills using that input. It’s a way to achieve artificial intelligence, or AI, using a “learn by doing” process.

Machine learning enables computers to learn and act without being explicitly programmed. It evolves from the study of pattern recognition and the design and analysis of algorithms to enable learning from data and make possible data-driven predictions or decisions. It is so pervasive today that many of us likely use it several times a day without even knowing it.

In earlier stages of analytics development, the companies that most benefited from the new field were the information firms and online companies that saw and seized the opportunities of big data before others. The ability to provide much needed data and information represented  a clear first mover’s advantage for these companies. While the first movers in big data were the big winners, their advantage won’t last much longer as productivity levels out. The evolution to Analytics 3.0 is a game changer because the range of business problems that intelligent automation — a mixture of AI and machine learning — can solve is increasing every day. At this stage, nearly every firm in any industry can profit from intelligent automation. Companies that invest immediately in machine learning have the potential to gain long-term benefits, profiting from the work of analytics pioneers. To gain these benefits, companies must rethink how the analysis of data can create value for them in the context of Analytics 3.0.

Beneficial Applications Versus Risks of Machine

Key Considerations and Implications

  • The moral component — The level of intelligence and “morality” that a machine exerts is a direct result of the data it receives. One consequence is that, based on the data input, machines may train themselves to work against the interest of some humans or be biased. Failure to erase bias from a machine algorithm may produce results that are not in line with the moral standards of society. Yet not all researchers, scientists and experts believe that AI will be hurtful to society. Some believe that AI can be developed to mirror the human brain and obtain human moralistic psychology to enhance society.
  • Accuracy of risk assessments — Risk assessments are used in many areas of society to evaluate and measure the potential risks that may be involved in specific scenarios. The increasing popularity of using AI risk assessments to make important decisions on behalf of people is a direct result of the growing trust between humans and machines. However, there are serious implications to note when using a machine learning system to make risk assessments. A quantitative analyst estimates that some machine learning strategies may fail up to 90 percent when tested in a real-life setting. The reason is that while algorithms used in machine learning are based on an almost infinite amount of items, much of this data is very similar. For these machines, finding a pattern would be easy, but finding  a pattern that will fit every real-life scenario would be difficult.
  • Transparency of algorithms — Supporters of creating transparency in AI advocate for the creation of a shared and regulated database that is not in possession of any one entity that has the power to manipulate the data; however, there are many reasons why corporations are not encouraging this. While transparency may be the solution to creating trust between users and machines, not all users of machine learning see a benefit there.

Next, we highlight some of the ways these implications play out in several industries.

Spotlight: Industry Implications

Today, artificial intelligence makes it possible to predict the likelihood of a heart attack with much better accuracy than before. While manual systems are able to make correct predictions with around 30 percent accuracy, a machine learning algorithm created at Carnegie Mellon University was able to raise the prediction accuracy to 80 percent. In   a hospital, an 80 percent prediction theoretically would give a physician four hours to intervene before the occurrence of the life-threatening event.

However, the accuracy of risk assessments in the medical field may vary depending on the level of bias in the research used to train the machine learning algorithm. For instance, most heart disease research is conducted on men, even though heart attack symptoms between men and women differ in some important ways. If the system is trained to recognize heart attack symptoms found in men, the accuracy of predicting a heart attack in women diminishes and may result in a fatality. For that reason, people who are affected by decisions based on AI risk assessments will want to know how these decisions are systematically made.

Hedge funds, which have always relied heavily on computers to find trends in financial data, are increasingly moving toward machine learning. Their goal is to be able to automatically recognize changes in the market and react quickly in ways quant models cannot. Most of these algorithms are proprietary, for a reason. The risk of having transparency in this case is that as one fund becomes successful using a certain algorithm, others will want to mimic that company’s machine learning method, diminishing everyone’s success and creating an artificial market environment. For this reason, any regulation that attempts to control the transparency of AI must be suitable and appropriate to the various scenarios where AI is used.

The U.S. National Highway Traffic Safety Administration recently released guidelines for autonomous vehicles, requiring auto manufacturers to voluntarily submit their design, development, testing and deployment plans before going to market with their vehicles. Despite these efforts to increase the transparency around “the brains” deployed in autonomous vehicles, car manufacturers, tech companies and auto parts makers are in a tight competition to develop the software behind self-driving cars, and their need to keep development efforts under wraps to gain market advantage may end up hurting the future of autonomy.

In addition, the nature of machine learning itself makes it very difficult to prove that autonomous vehicles will operate safely. Traditional computer coding is written to meet safety requirements and then tested to verify if it was successful; however, machine learning allows a computer to learn and perform at its own pace and level of complexity. The more automakers are willing to be transparent about the data they input into the learning algorithms, the easier it will be for lawmakers and auto safety regulators to create laws that will ensure the safety of consumers.

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Top 10 Technology Trends To Watch

Top 10 Technology Trends To Watch

Top 10 Technology Trends To Watch: Forrester Research

Forrester Research just published The Top 10 Technology Trends To Watch: 2018 To 2020. Ten trends, which Forrester breaks into three phases of dawning, awareness, and acceptance, are setting the pace of technology-driven business change.

In the dawning phase, a few innovators experiment with new technology-enabled business models and exploit emerging technologies. In the awareness phase, change agents leverage the accelerating returns of evolving technology to steal customers, improve the bottom line, and inflict massive impacts on industries. In the acceptance phase, surviving enterprises finally make the tough changes necessary to fight disruptors.

Computing goes to the edge

The internet of things (IoT) shifts computing toward the edge. Firms in the vanguard of this trend will use edge computing to build distributed applications that close the gap between data, insight, and action, and will engage customers more quickly and squeeze new efficiencies out of their processes. Exploiting computing power on the edge will give them an actual edge. It is another phase in the continuous pendulum swing between centralized computing and distributed computing.

By 2020, Forrester predicts, “a few leaders emerge from the pilot phase with integrated solutions that help them intelligently serve customers.”

Blockchain spreads to many markets

In another manifestation of power moving to the periphery, distributed trust systems—systems of methods, technologies, and tools that support a distributed, tamper-evident, and reliable way to ensure transaction integrity, irrefutability, and nonrepudiation—challenge centralized authorities.  Applications of blockchain go beyond cryptocurrencies to attack any market in which assets have to be tracked across owners. Maersk, for example, plans to use blockchain to build a distributed trust system to track shipments for customers and regulators. Forrester doesn’t believe secure blockchain and other distributed trust systems will eliminate the need for brokers and intermediaries. But they will change the function of those coordinating companies away from transaction brokering and toward services that use the technology to solve new transaction problems — such as managing onboarding, identity tracking, compliance reporting, and relationship management.

By 2019, Forrester predicts, “a viable blockchain-based market will be commercialized.”

Cybersecurity at scale

Early successes and increasing vendor capabilities will accelerate the automation of security breach detection and response, leveraging internal and external threat intelligence data. Automated remediation is likely to follow. Security automation and orchestration (SAO) will unshackle the chief information security officer’s team from repetitive, manual tasks, giving analysts more time for higher-value work. It will also help address the dearth of cybersecurity professionals.

 By 2019, Forrester predicts, “established security teams with mature processes begin to tie response automation to improved threat intelligence.”

Employee-centric application development

Successfully engaging customers starts at home. Few firms currently focus on employee experience in their application development, and global employee engagement levels haven’t improved in Gallup’s 17 years of keeping track. Good employee experience, however, serves as a platform for employees to deliver superior customer value through their engagement, service delivery, communication, learning, collaboration, and talent development. Mobile, social collaboration, AI, machine learning, and other technologies will transform the employee experience to consumer-grade standards.

By 2020, Forrester predicts, “firms successful with employee experience systems of engagement will experiment with personalized employee experience.”

Software learns to learn

Models will monitor and retrain themselves in the public cloud, allowing software systems to be trained instead of programmed. With improvements in AI, software systems that we used to have to program with rules are learning how to learn on their own. But, Forrester cautions, don’t expect miracles by 2020 as talent and technology will remain big constraints. Your machine learning and AI aspirations depend on data, but if you don’t know where it is, what it is, and how to get at it, you are in for trouble.

By 2019, Forrester predicts, “machine learning services and deep learning platforms mature to accelerate self-training models.”

Digital employees enter the white-collar workforce

Nearly half of all jobs face losses to automation from software robots that complete increasingly complex tasks. Humans will work alongside software robots, and leaders will manage workforces that are part robot and part human. Many enterprises, however, lack an integrated approach to mining the value of white-collar automation. Repeatable tasks that search, collate, update, access multiple systems, and make simple decisions provide today’s best targets for automation. But…it’s dangerous to let the software make decisions entirely unsupervised. Not only must people be trained on the human-machine interface, but experts must continue to monitor critical tasks.

 By 2020, Forrester predicts, “AI and Robotics Process Automation will work together to accelerate complexity and automation tasks.”

Insights-driven firms outpace competitors

Insights-driven businesses, designed for continuous measurement and optimization, will outpace the competition. The quest to use big data as a competitive asset has sparked a $27 billion industry; Hadoop and Spark initiated this, but it’s rapidly expanding into services and the cloud. Amid the data gold rush, a new kind of firm — the insights-driven business — is slowly emerging that approaches data analytics differently. Instead of focusing on data, these firms emphasize implementing insights in software and continuously learning.

By 2019, Forrester predicts, “many data lakes crumble, and data strategy work stalls. More firms learn insights-driven lessons.”

Customer experience becomes immersive

Top firms will further fuse digital and physical experiences, employing new business models to do so. The boundaries between the human, digital, physical, and virtual realms are blurring as customer experience becomes more immersive. Customer-obsessed firms are integrating systems of insight and systems of engagement that interconnect people, places, and objects with data to improve customer experience and forge two-way, value-driven relationships. Leaders will focus on the human experience and invest significantly to integrate technology into the physical experiences of both customers and employees. Forrester expects significant growth in the use of technologies that fuse digital and physical experiences via mobile devices, such as image recognition, gestures, voice, and augmented reality.

By 2019, Forrester predicts, “firms will augment physical experiences with emerging digital technologies.”

Contextual privacy boosts brand value

Consumers are concerned about data security and privacy, forcing transparency in firms’ data collection and use practices. Top firms will look beyond risk reduction and compliance, employing contextual privacy to drive retention and loyalty. Contextual privacy is a business practice in which the collection and use of personal data is consensual, within a mutually agreed-upon context, for a mutually agreed-upon purpose. It requires business collaboration and support for processes like classifying data as well as understanding the value of the data to the business, the data’s useful life cycle, and the company’s obligations for data protection and handling.

By 2020, Forrester predicts, “customer-obsessed firms will recognize contextual privacy as a competitive differentiator for long-term loyalty.”

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Microsoft shows off Windows PCs that run on Qualcomm chips

Microsoft shows off Windows PCs that run on Qualcomm chips

Intel’s stumbles in the mobile arena grew to become Qualcomm’s gains with its Snapdragon platform, but now it appears that Qualcomm intends to upend their dominance in entry-level PCs as well. Today, Microsoft and Qualcomm announced that Windows would begin running on systems powered by Qualcomm’s Snapdragon mobile chipsets.

This isn’t entirely earth-shattering given that these are mobile chipsets running on what are still just laptop convertible 2-in-1s or tablets, but the fact that Windows 10 — albeit a more lightweight “Windows 10 S” — can run on Qualcomm 835 chips should still worry Intel quite a bit.

These devices have the brains of smartphones with the battery capacities of laptops. Qualcomm obviously won’t be competing with Intel on the performance of its higher-end chips, but one thing the company highlighted today was just how long its power-saving technologies will extend the battery life of your device.

They referred to this tech as “beyond all-day battery life,” which, as the name implies, suggests that the power efficiency of these PCs will push battery life into a second day of full usage, something that isn’t too likely to be had on most laptops that are powering a full work day. Furthermore, these PCs are optimized to be always on and always LTE-connected.

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