How IBM is benefited using Artificial Intelligence?

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    This post is all about," How IBM is using Artificial Intelligence in its core sectors? "

But before going directly to that point I would like to give you a brief knowledge about Artificial Intelligence and its Applications.

What is artificial intelligence?

In computer science, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mind—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems—and combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.

After decades of being relegated to science fiction, today, AI is part of our everyday lives. The surge in AI development is made possible by the sudden availability of large amounts of data and the corresponding development and wide availability of computer systems that can process all that data faster and more accurately than humans can. AI is completing our words as we type them, providing driving directions when we ask, vacuuming our floors, and recommending what we should buy or binge-watch next. And it’s driving applications—such as medical image analysis—that help skilled professionals do important work faster and with greater success.

Artificial intelligence applications

As noted earlier, artificial intelligence is everywhere today, but some of it has been around for longer than you think. Here are just a few of the most common examples:

• Speech recognition: Also called speech to text (STT), speech recognition is AI technology that recognizes spoken words and converts them to digitized text. Speech recognition is the capability that drives computer dictation software, TV voice remotes, voice-enabled text messaging and GPS, and voice-driven phone answering menus.
• Natural language processing (NLP): NLP enables a software application, computer, or machine to understand, interpret, and generate human text. NLP is the AI behind digital assistants (such as the aforementioned Siri and Alexa), chatbots, and other text-based virtual assistance. Some NLP uses sentiment analysis to detect the mood, attitude, or other subjective qualities in language.
• Image recognition (computer vision or machine vision): AI technology that can identify and classify objects, people, writing, and even actions within still or moving images. Typically driven by deep neural networks, image recognition is used for fingerprint ID systems, mobile check deposit apps, video and medical image analysis, self-driving cars, and much more.
• Real-time recommendations: Retail and entertainment web sites use neural networks to recommend additional purchases or media likely to appeal to a customer based on the customer’s past activity, the past activity of other customers, and myriad other factors, including time of day and the weather. Research has found that online recommendations can increase sales anywhere from 5% to 30%.
• Virus and spam prevention: Once driven by rule-based expert systems, today’s virus and spam detection software employs deep neural networks that can learn to detect new types of virus and spam as quickly as cybercriminals can dream them up.
• Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
• Ride-share services: Uber, Lyft, and other ride-share services use artificial intelligence to match up passengers with drivers to minimize wait times and detours, provide reliable ETAs, and even eliminate the need for surge pricing during high-traffic periods.
• Household robots: iRobot’s Roomba vacuum uses artificial intelligence to determine the size of a room, identify and avoid obstacles, and learn the most efficient route for vacuuming a floor. Similar technology drives robotic lawn mowers and pool cleaners.
• Autopilot technology: This has been flying commercial and military aircraft for decades. Today, autopilot uses a combination of sensors, GPS technology, image recognition, collision avoidance technology, robotics, and natural language processing to guide an aircraft safely through the skies and update the human pilots as needed. Depending on who you ask, today’s commercial pilots spend as little as three and a half minutes manually piloting a flight.

Artificial intelligence and IBM Cloud

IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:

• Collect: Simplifying data collection and accessibility.
• Analyze: Building scalable and trustworthy AI-driven systems.
• Infuse: Integrating and optimizing systems across an entire business framework.
• Modernize: Bringing your AI applications and systems to the cloud.

The IBM Cloud AI services start with Watson Studio for building and training AI models, preparing data and performing analysis on the data. This is available in one integrated environment. For existing data, there is Watson Knowledge Catalog to do intelligent data and analytic asset discovery, cataloging and governance, and Watson Discovery to find connections and relations.

IBM has made a point of noting that only 20% of the world’s data is searchable, and there is heavy emphasis in IBM Cloud Watson on data processing and discovery. An example of this is IBM Watson Services for Core ML, which allows enterprises to build AI-powered apps that securely connect to their data and run either on-premises, offline or in cloud. These apps utilize machine learning to adapt and improve through each user interaction.

Other data discovery apps include Data Refinery, a self-service data preparation tool for data scientists, engineers and business analysts and Deep Learning, which helps developers design and deploy deep learning models using neural networks, easily scale to hundreds of training runs.

To build AI platforms, IBM has Watson Assistant to build and deploy chat bots and virtual assistants, Watson IoT Platform to provide a cloud-hosted service for device registration, connectivity, control, rapid visualization and data storage.

IBM is also big on language recognition and translation. Watson Speech to Text (STT) converts audio and voice into written text, while Watson Text to Speech (TTS) does the opposite and converts written text into natural-sounding audio in a variety of languages and voices.

How IBM Is Using AI At Scale To Benefit The Media Industry

IBM (NYSE: IBM) recently announced 3 new products to add to its growing suite of artificial intelligence solutions for brands and publishers. The new capabilities are privacy-forward and designed to enable brands to reach consumers while considering user privacy. And IBM intends to work with industry leaders, including Xandr/AT&T, Magnite, Nielsen, MediaMath, LiveRamp, and Beeswax to help scale the use of artificial intelligence across the industry.

The IBM Watson Advertising suite of solutions utilizes artificial intelligence to help clients make informed data-based decisions. And expanding on recent additions to the suite, including Watson Advertising Accelerator, Watson Advertising Social Targeting with Influential and Watson Advertising Weather Targeting among others, the planned AI-enabled capabilities also include:

1.) Extensions for IBM Watson Advertising Accelerator: Enhanced video and OTT capabilities available in the next few months that are expected to leverage Watson Machine Learning to help enable marketers to pivot video advertising creative based on individual user reaction.
2.) IBM Watson Advertising Attribution: The Beta Solution available in the coming months that utilizes Watson Machine Learning — which allows marketers to accurately quantify the efficacy of their advertising spend while understanding intent and performance drivers.
3.) IBM Watson Advertising Predictive Audiences: Solution utilizes Watson Discovery, to help enable marketers to progress beyond ‘look-alike’ to ‘do-alike’ segments in a privacy-forward format to reach consumers that exhibit similar behaviors.

IBM Watson is able to offer increased AI capabilities for businesses across language, automation, and trust. And the planned capabilities from IBM Watson Advertising will be designed to help infuse trust and transparency into the advertising ecosystem. IBM is also negotiating definitive agreements with Xandr/AT&T and Magnite.

IBM sees AI benefits in phase-change memory

In a development that holds promise of more sophisticated programming of mobile devices, drones and robots that rely on artificial intelligence, IBM researchers say they have devised a programming approach that achieves greater accuracy and reduced energy consumption.

AI systems generally employ procedures that divide memory and processing units. This practice means time is consumed transferring data between the two waypoints. The volume of data transfer is massive enough to accrue costly energy tabs.

Nature Communications reported this week that IBM devised an approach that relies on phase-change memory to execute code faster and cheaper. This is a type of random access memory containing elements that can rapidly change between amorphous and crystalline states, offering performance superior to the more commonly used Flash memory modules. It is also known as P-RAM or PCM. Some refer to it as "perfect RAM" because of its extraordinary performance capabilities.

PCM relies on chalcogenide glass, which has a unique capacity to alter its state when a current passes through. A key advantage of phase change technology, first explored by Hewlett Packard, is that the memory state does not require continuous power to remain stable. The addition of data in PCM does not require an erase cycle, typical of other types of memory storage. Also, since code may be executed directly from memory rather than being copied into RAM, PCM operates faster.

IBM recognized that the growing requirements of operations relying on deep neural networks in the fields of image and speech recognition, gaming and robotics demand greater efficiencies.

"As deep learning continues to evolve and demand greater processing power," an IBM team studying solutions posted on a company blog, "companies with large data centers will quickly realize that building more power plants to support an additional one million times the operations needed to run categorizations of a single image, for example, is just not economical, nor sustainable."

"Clearly, we need to take the efficiency route going forward by optimizing microchips and hardware to get such devices running on fewer watts," the report states.

IBM compared PCM to the human brain, noting that it "has no separate compartments to store and compute data, and therefore consumes significantly less energy."

One drawback with PCMs is the introduction of computational inaccuracies due to read and write conductance noise. IBM addressed that problem by introducing such noise during AI training sessions.

"Our assumption was that injecting noise comparable to the device noise during the training of DNNs would improve the robustness of the models," the IBM report states.

Their assumption was correct. Their model achieved an accuracy of 93.7 percent, which IBM researchers say is the highest accuracy rating achieved by comparable memory hardware.

IBM says more work needs to be done to obtain even higher degrees of accuracy. They are pursuing studies using small-scale convolutional neural networks and generative adversarial networks, and recently reported on their progress in Frontiers in Neuroscience.

"In an era transitioning more and more towards AI-based technologies, including internet-of-things battery-powered devices and autonomous vehicles, such technologies would highly benefit from fast, low-powered, and reliably accurate DNN inference engines," the IBM report.

Why IBM is using A.I. to find jobs for people who don’t have a college degree

With the unemployment rate at a low 3.7% and the skills shortage severe, corporations need to get creative about finding talented job candidates. IBM is among the technology giants testing new methods involving artificial intelligence to overcome the labor market challenges.

AI has been applied to the job application process directly as a method to prevent human bias in hiring decisions. Now more companies are using AI assessment tools to reverse-engineer job roles and find candidates often overlooked by recruiters.

IBM introduced its SkillsBuild platform in France in May 2019 with the goal of identifying job skills and employment opportunities for members of disadvantaged communities. It will be rolled out in Germany in the coming months, followed by India, and then IBM plans to bring the platform to the U.S. in 2020, by which time it is likely that the program will have thousands of users, the company says.

The IBM initiative provides jobseekers, including those with long-term unemployment, refugees, asylum seekers and veterans, with career fit assessments, training, personalized coaching and learning needed to reenter the workforce. SkillsBuild has partnered with several NGO partners and nonprofits to form, in IBM’s words, “a new, sustainable hiring mindset,” but it is not currently used as part of the application process for IBM jobs, specifically.    

Hiring below the college degree

Within the IBM Skillsbuild platform are AI tools like MyInnerGenius, created by San Diego area-based GreatBizTools, which designs AI products to find talent in nontraditional ways. 

MyInnerGenius is helping fill entry-level and mid-level IT roles for IBM’s New Collar program in the U.S. and SkillsBuild program in Europe. Many of these roles are being filled by applicants with no prior experience in technology, many of whom don’t have college degrees.

“There are currently more than 500,000 IT job openings, and colleges are only producing around 50,000 people with degrees in IT per year,” said Denise Leaser, president of GreatBizTools. The skills shortage has made it more necessary than ever for companies like IBM to look outside of their usual applicant pool. “IBM wants to open up IT to more people, especially people who may have never thought of or considered an IT role before.”

IBM using AI to predict employee performance

IBM may have the most forward-thinking employee performance review system around. Rather than simply judge employees on what they’ve already done, the company uses its Watson AI to predict what they’re going to do in the future.

How it works: Predicting the future is right inside of Watson’s wheelhouse. In this case it isn’t determining whether you’re going to win the lottery and quit, it’s using company data to make logical projections about individual performance.

Why it’s cool: IBM has 380,000 employees worldwide. That’s a lot of performance reviews for human managers to handle, and a massive time investment if they’re going to give them the scrutiny they deserve.

Watson could, theoretically, give each individual a comprehensive evaluation based on every scrap of information available, in a fraction of the time it would take humans. And the data generated is what it uses to fuel its predictions for future performance.

According to a report from Bloomberg, IBM claims Watson predicts future employee performance with 96 percent accuracy.

What’s next: IBM wants Watson everywhere. It’s been to the Grammys and outer space, and the next stop could be your company. But at least an AI probably won’t overlook your recent training, forget about your strong sales in the first quarter, or hold the fact that you’re a Yankees fan against you.

Hope you guys enjoyed reading and gained quiet good amount of knowledge about IBM and its benefits of using AI.

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