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Without getting excessively technical, basically, the entire reason of AI is a machine emulating the human brain. The machine can learn and adjust to various situations, and as time passes, the machine gets smarter and responds diversely to accomplish better outcomes. A one of a kind opportunity exists to apply AI to a particular part of the clean energy value chain: materials. Materials fill in as the structure blocks of clean energy, for example, the solar cells that make up the photovoltaic panels found on rooftops. Enhancing the materials used to manufacture parts of clean energy is significant on the grounds that current materials are frequently lethal, non-earth rich, and require carbon-concentrated processing. Utilizing AI along these lines can give producers an edge. Manufacturers will in general put resources into upgrading downstream production capacities, which has prompted a few AI applications in sensor innovations and process optimisation. Utilizing AI for upstream design purposes, nonetheless, is an undiscovered business opportunity that could decrease the time it takes to find new materials, opening up capital for deployment and commercialisation strategies. In July 2023, Curtis Berlinguette, a materials scientist at the University of British Columbia in Vancouver, Canada, acknowledged he was burning through his graduate student’s time and ability. He had asked her to refine a key material in solar cells to boost its electrical conductivity. In any case, the number of potential changes was overpowering, from spiking the formula with hints of metals and different added substances to shifting the heating and drying times. According to Berlinguette, there are such a significant number of things you can go transform, you can rapidly experience 10 million [designs] you can test. So, he and associates re-appropriated the effort to a single-armed robot overseen by an artificial intelligence (AI) algorithm. Named Ada, the robot blended various solutions, cast them in films, performed heat medicines and other processing steps, tried the film’s conductivity, assessed their microstructure, and logged the outcomes. The AI deciphered each examination and figured out what to blend next. At a meeting of the Materials Research Society (MRS) a week ago, Berlinguette revealed that the system immediately homed in on a formula and heating conditions that made defect-free films perfect for solar cells. What used to take them 9 months presently takes 5 days. Ada could change how clean energy is made at a small amount of time and cost. Via autonomously testing materials at high computing forces, Ada plans to make solar panels stronger and to transform carbon dioxide into valuable fuels. Robots have already made a difference. They are currently generally used to blend many somewhat various recipes for a material, store them on single wafers or different platforms, and afterward process and test them all the while. In any case, basically trudging through recipe after the recipe is a moderate course to a breakthrough. High throughput is an approach to do heaps of experiments, however, not a great deal of development. To speed the procedure, numerous teams have included computer modeling to foresee the equation of likely pearls. “We’re seeing a torrential slide of exciting materials originating from the forecast,” says Kristin Persson of Lawrence Berkeley National Laboratory (LBNL) in California, who runs a large-scale prediction enterprise known as the Materials Project. However, those frameworks still commonly depend on graduate students or experienced researchers to assess the consequences of trials and decide how to continue. However, Individuals still need to do things like rest and eat.

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How Ai Robotics Are Transforming The Health Care Industry

Two of the most futuristic technologies that the world is leveraging today are AI and Robotics. Implementing these two technologies can lead to innovations in several industry verticals, including the healthcare industry.

AI Robotics are Transforming the Health Care Industry

AI and Robotics are already working in several healthcare establishments. They’re carrying out tasks such as genetic testing, robotic surgery, cancer research, data collection, and more.

Additionally, in the dermatology sector, AI is detecting skin cancer. The process of detecting skin cancer involves a technology, “MelaFind,” that uses infrared light to evaluate the skin condition. Afterward, with its sophisticated algorithms, AI evaluates the scanned data to determine skin cancer’s seriousness.

When it comes to the healthcare industry, there are several disciplines that AI and Robotics need to cover

AI and Robotics require more unveiling and continued experimentation to become an integral part of the industry and bring innovations through these emerging technologies.

AI and Robotics can fill in the medical and healthcare industry gaps

On the other hand, the most crucial question remains unanswered; “are we prepared to deliver the entire life and death decisions to the machines” ,”can machines indicate if the care has been provided to a patient’s sufficient or not?”

Assessing the questions mentioned previously might not be simple since there are various challenges and problems the program of AI and Robotics can contribute to. But, 1 thing is for certain: AI and Robotics are poised to develop into an essential component of the medical market.

The implementations of AI and Robotics in the healthcare industry.

We are going to be covering many areas of the medical sector where this technology can facilitate the overstraining procedures in health care delivery.

Below are some of the Present examples of execution of AI and Robotics at the healthcare domainname:

Determining the patient’s priority assessments in emergency service.

Automated health tracking of patients.

Quick and continuous supply of medicine and equipment throughout the hospital floors via intelligent robots.

Interacting with patients via vocals or facial recognition.

Programming personalized health programs in robots enable users to leverage them for multifunctional purposes.

Intro to AI & Robotics in Healthcare Sector

AI and Robotics have proved to be widespread in the medical market. The ubiquitous development of both of these technology has the capacity to transform a lot of facets of healthcare.

From providing personal services to patients to expedite the medication manufacturing process, AI and Robotics can make sure a quicker roll-out date and a efficient and precise performance.

In addition, there are numerous major tech companies out there which are capitalizing on AI and Robotics to enhance the medical infrastructure. By way of instance, Google is currently cooperating with the health care delivery system to construct prediction models.

These forecast models by Google derive from large information and machine learning technologies. They could warn the clinicians of high risk states of the individual, like heart failure or sepsis.

Additional Microsoft is focusing on altering the healthcare sector by fostering and building a culture of smart health through AI and Cloud in health care organizations.

1. Supplementary Robots

These bots are usually aimed towards distributing shares all over the hospital or where they are needed. We have discussed these kinds of robots above too nevertheless, their significance and viability have to be clarified also.

In hospitals, there are instances when multiple patients need immediate drugs or help. In instances like this, the team is generally in a rush to aid the individual rather than carrying out other jobs. Therefore, supplementary robots these days are quickly focusing on tasks like restocking, carrying out the garbage, and cleaning while the people are spending additional time together with the individual.

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2. Exceptional Precision

Multiple autonomous systems employed from the planet’s top hospitals now supply immense feasibility in executing more complicated jobs at a quick and precise rate.

Robot’s real concentrate and robots that are attentive further fortify their core performance and permit them to perform jobs with intense precision. These bots are supported by AI which enables them to understand while doing jobs. Because of these characteristics of bots, their significance in a healthcare organization can’t be refused.

It is a simple fact that robots require continual checkup and upkeep to work correctly; therefore, human intervention is essential for the time being.

What’s more, some robots will also be tasked with reallocate supplies during the hospital using especially designed paths and lifts within a hospital.

Another nice example of outstanding precision would function as micro-robots utilized to do micro-surgeries like unclogging blood vessels. Nevertheless, human intervention or oversight may be asked to overlook the whole procedure or always keep the autonomous systems.

3. Remote Treatment

The concept of distant therapy was around for at least a decade today. The technology was originally halted as a result of the inadequate network connectivity in the time of its implementation. However, further improvements and experiments have been conducted following the creation of 4G and 5G networks.

Nowadays, even though human intervention is necessary in the distant therapy industry, machines can execute many complex tasks individually.

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4. Accurate Diagnostics

The accurate and precise diagnostics of individual health requirements is where AI actually shines. The AI finds patterns which are directing the individual towards different health conditions. It decides the patient’s present condition by analyzing and analyzing the health records and information.

Tests so far have concluded that AI is capable of correctly diagnosing disorders in 87 percent of those instances. By comparison, health state detection by people had an 86% accuracy rate.

Additionally, IBM Watson, health care technology, has struck the 99 percent markers in diagnosing cancer. Therefore, considering the proportions mentioned previously, I assume that AI and Robotics could rival even the best physicians of earth when it comes to diagnostics.

5. Performing Daily Tasks

Robots can do daily tasks and execute many functions that were being done by people. By way of instance, a robot may make a sick or older person/patient feel attended all of the time, reducing the need for human existence. These bots are programmed to behave as private aid.

Additionally, robots may also participate patients in discussions, help them take their medication in time by alerting them, and also execute a simple checkup on them to examine their health state from time to time.

6. Assisting Patients & Medical Practitioners

Some particular robots are made to help the health personnel concentrate on other essential facets of the hospital. These robots are assisting patients by helping them in monitoring or walking their health condition.

Prior to the dawn of AI and Robots, tasks like helping a patient walk, helping the patient to get his/her checkup, carrying the background of the patient’s disease were guide and time-consuming. But these jobs are readily automated and quickly completed by machines.

Additionally, today, robots are assisting people with all from minimally invasive procedures to complicated open-heart surgeries.

Another superb illustration of those robots may function as prosthetic legs made by Hugh Herr. The biomechatronic feet enable the consumer to walk separately without human intervention.

The detectors in those bionic feet link with the individual’s muscles and also allow more freedom and stability. These detectors perfectly replicate human toes’ functioning and provide the consumer more edge over the first feet.

The Promising Future of AI & Robotics in Healthcare

But, AI or robots won’t be taking complete control within the medical sector anytime soon. Human intervention and oversight would nevertheless be an important element in ensuring 100% accuracy in the general procedure.

Additionally, the patients have been known to create a closer relationship with their physicians, nurses, or other health staff. This particular relationship provides patients a sense of never being alone. This atmosphere can never be replicated by robots or machines. Hence, people will always be there using AI and robotics to treat patients and provide a soothing and calming experience collectively.

In addition, the viability of AI and robotics has been destined to flourish in forthcoming years.

By enhancing the clinical workflow to determining the specific cause of an individual’s specific health condition, AI and robotics benefit the health care domain. Consequently, it’s fairly evident that AI will finally master the health care domain later on.

Which Technologies Are Transforming It?

To investigate the matter, research firm IDC asked 236 line-of-business (LOB) execs and 268 IT professionals (both CIOs and IT managers) to name the following:

Question: The one technology that you believe has the greatest impact on transformation of business process.

The graph below reflects their answers. (Note that the list of responses doesn’t include every possibility. Notably absent, for instance, is outsourcing.)

Clear differences of opinion are seen in the results for virtualization and Web services. The IT pros see virtualization as the leading trend – by a big percentage – while the business execs view Web services as the leading trend.

But this difference isn’t what it seems, says Frank Gens, a senior VP of research at IDC, who explored the results in a blog post.

“It’s like the fable of the six wise men and the elephant,” Gens tells me, “where different people see different parts of the same thing, but they call it different things.”

As he interprets it, virtualization and Web services are parts of the same thing, which is the movement toward a cloud-enabled environment and cloud computing.

Part of the difference in response is that many business execs are not fully knowledgeable about virtualization. The term virtualization “sort of glazes over the eye of the typical business person – they generally don’t know what it means.”

(Indeed, Gens notes that when he writes about virtualization using Microsoft Word, “spellcheck continues to put a squiggly line under the virtualization – you don’t find it in the dictionary.”)

In its most pragmatic use, virtualization is a major cost saver for companies with banks of servers. Instead of having to buy, say, 30 more servers, virtualization software enables companies to squeeze more use out of existing servers. One would think this cost savings would make virtualization near and dear to the hearts of business execs. But again, this is a concept that’s more familiar to IT pros than business execs.

Gens was surprised that Web services was ranked so highly by business execs. Like virtualization, it’s a term that may not be fully understood – or at least have several meanings – outside of geeky datacenter chat sessions. “I was a little surprise that line-of-business people even know what Web services are,” he says.

Yet as he thought about it, he realized that these business execs were viewing Web services in its broadest sense. Not the established technical standards like SOAP or WSDL, but instead Software as a Service and other Web-delivered products. “They’re thinking of chúng tôi or Google Apps – that’s the thing they can see most out of this whole world of virtualized, cloud-enabled next-generation IT environment.”

In contrast, the IT pros think of Web services as the already entrenched technologies of SOAP or WSDL – helpful, but at this point no longer the leading edge of what’s changing IT.

Merging Concepts

If you look past the differences in terminology (and knowledge level) between these two camps, the graph gives an insightful forecast into where IT is going.

But to get this view, you need to combine the attitudes of business execs and IT pros. Virtualization, for instance, is both things: it’s the very pragmatic technology that helps server management (as the IT pros see it), and it’s also a core underpinning of Software as a Service (the trend that business execs see as preeminent).

Or, put another way, “It’s the same thing,” Gens says. “But the IT folks are looking at the underpinning technology – virtualization – and the business people are looking at it from the end deliverable, which is Software as a Service.”

In reality, it’s the merging of several leading technologies will shape the future of enterprise technology.

“I look at the addition of Web services, SOA, and virtualization – those three things together are really all about this next generation of IT,” Gens says. It’s a world in which users will have relatively efficient, low cost access to business and IT resources.

In the next version, expect ‘cloud computing’ to be a leading choice. It will probably be one that both IT pros and business execs can agree on.

James Maguire is the manager editor of Datamation.

Ibm Reshaping Watson For Transforming Its Ai Business

Thomas J. Watson Sr. joins Computing-Tabulating-Recording Company (CTR) in 1914 and over the next two decades transforms it into a growing leader in innovation and technology. He built a worldwide industry; it is called to International Business Machines Corporation (IBM) in 1924. According to Fortune 500, IBM is ranked as one of top 10 firms in 90’s. Let’s take a look at the roadmap of the IBM in the digital transformation, it consists not just software, hardware and services include cognitive solutions and cloud platform. IBM’s Bekas explains that we simply can’t scale enough hardware to solve this. “Ultimately, hardware can’t beat computational complexity. You need to have a combination of algorithmic improvement and hardware development,”

In this article, we will focus on artificial intelligence area and related industry focuses. Artificial intelligence is rapidly coming of age, poised to transform businesses and industries globally. The market for AI is on an exponential growth curve and is expected to reach $16.06 billion by 2023. With over half of all developer teams projected to embed AI services in their apps by 2023, it’s inevitable that consumers will soon be interacting with these new technologies on a regular basis.

IBM’s AI Strategy

Cognitive Solutions

With the highest level of intelligence that exists in technology systems, these solutions tackle challenges ranging from answering client inquiries to helping physicians fight cancer. Watson Health optimizes performance, engage consumers, deliver effective care and manage the health of your population.

What is IBM Watson?

The first cognitive system was Watson, which debuted in a televised Jeopardy! (a quiz competition) challenge where it bested the show’s two greatest champions. Watson answered many questions about synonyms, antonyms or slang and it achieved all of them without the internet connection. To find and understand the clues in the questions use machine learning, statistical analysis, and natural language processing.

New generations of that cognitive systems are trying to use diagnose oncology for healthcare professionals and in the customer services. Watson solutions are being built, used and deployed in more than 45 countries and across 20 different industries. IBM unceasingly pushes the boundaries of Watson increasing its use areas and developing new algorithms.

In earlier 2023 IBM announced the cooperation with Illumina Inc., their new designs’ aim is helping standardize and simplify genomic data interpretation. TruSight Tumor 170 is an assay designed to cover 170 genes associated with common solid tumors by Illumina. In a matter of minutes, Watson for Genomics will read the genetic alteration files produced by TruSight Tumor 170, comb professional guidelines, medical literature, clinical trials compendia, and other sources of knowledge to provide information for each genomic alteration, and produce a report for use by researchers — a process that typically takes scientists more than one week to complete. Watson for Genomics ingests data from approximately 10,000 scientific articles and 100 new clinical trials every month.

IBM’s technology is quite unique thanks to highly adaptable intelligence systems, protect and respect client data, trained in domain depth and transformational services.

What is IBM Watson used for?

IBM’s Watson services based on four main parts as language, speech, vision, and data insights.

In the language part, the conversation is maintained by chatbots that understand natural language and deploy them on messaging platforms and websites, on any device. Document conversation, language translator, tone analyzer, and natural language translator are used and information retrieval is enhanced with machine learning. Also, Natural Language Processing (NLP) has a long and distinguished history at IBM Research and is currently the focus of numerous projects worldwide. IBM interests cover a wide range of topics from Machine Translation, to Information Extraction, to Question Answering. Artificial intelligence tries to understand personality characteristics, needs, and values in written text.

Watson Speech to Text converts audio voice into written text. This system transcribes calls in a contact center to identify what is being discussed, when to escalate calls, and to understand content from multiple speakers. Speech to text creates voice-controlled applications — even customize the model to improve accuracy of the language and content you care about most such as product names, sensitive subjects, or names of individuals. Furthermore, IBM enables computers to speak like humans via converting written text to text into natural sounding audio. The common areas that used are; toys for children, automate call center interactions, and communicate directions hands-free.

Visual Recognition understands the contents of images — visual concepts tag the image, find human faces, approximate age, and gender, and find similar images in a collection. You can also train the service by creating your own custom concepts. It is usually used in the e-commerce sites to detect a dress type. According to February News , a new capability being added to Visual Recognition is color tagging. While Watson has already been able to detect color, it will now return the top colors it sees in each image as response tags, each accompanied by a classification score. The new capability allows users to quickly assess the dominant color schemes within an image and turn these into actionable insights. Not only analyze, fashion designers will predict color trends from ten years of fashion runway images.

With AI, convert, normalize and enrich your unstructured data. Discover from already exist pre-enriched datasets by using a simplified query language like Discovery News dataset is a public data set that has been enriched with cognitive insights, and is included within the Watson Discovery Service. It is updated continuously, with over 300,000 new articles and blogs added daily, sourced from more than 100,000 sources.

ABB IBM Partnership

If we consider ABB and IBM collaboration form, organizations using the solutions will benefit from ABB’s deep domain knowledge and extensive portfolio of digital solutions combined with IBM’s expertise in artificial intelligence and machine learning as well as different industry verticals. ABB and IBM will leverage Watson’s artificial intelligence to help find defects via real-time production images that are captured through an ABB system and then analyzed using IBM Watson IoT for Manufacturing. Previously these inspections were done manually, which was often a slow and error-prone process. By bringing the power of Watson’s real-time cognitive insights directly to the shop floor in combination with ABB’s industrial automation technology, companies will be better equipped to increase the volume flowing through their production lines while improving accuracy and consistency. As parts flow through the manufacturing process, the solution will alert the manufacturer to critical faults — not visible to the human eye — in the quality of assembly. This enables fast intervention from quality control experts. Easier identification of defects impacts all goods on the production line and helps improve a company’s competitiveness while helping avoid costly recalls and reputational damage. [1]

All these R&D and acquisitions are claimed to cost $16bn during 2023 but Watson would start bringing in money despite all cost. IBM’s chief financial officer Martin Schroeter said revenue would come through Watson serving IBM’s strategic imperatives and cognitive software. Watson is the “silver thread” running through Watson Health and Financial Services, IBM’s IoT and security, he said. “Watson is firmly established as the silver thread that runs through those cognitive solutions and you can see all of that in the solution software performance.”[2]

Strategic imperatives accounted for 40 percent of IBM’s revenue, $32.8bn for 2023, the firm said. Its stated goal is to make $40bn from them by 2023.

Industry Focus: As IBM brings higher levels of value to its clients, as its offerings are being built for the needs of individual industries. Healthcare and Financial Services are two examples of the company’s initial cognitive focus. In the healthcare industry, IBM Watson achieves remarkable outcomes, accelerate discovery, make essential connections and gain confidence on their path to solving the world’s biggest health challenges.

One year ago from today, IBM announced their plan to acquire Truven Health Analytics, a leading provider of cloud-based healthcare data, analytics and insights for $2.6 billion. Other industries are cyber security and financial guidelines IBM Security — which monitors 35 billion security events a day for 12,000 clients spanning 133 countries — launched the world’s first commercial “cyber range,” where clients can simulate and prepare for real-world attacks and draw on the power of Watson to fight cyber crime. The company told The Telegraph that IBM Watson “can help thwart the major hacks that have become a growing concern”, quoting attacks on Yahoo, Lloyds and TalkTalk. Watson’s security machine can additionally save up to 20,000 hours a year chasing false alarms.

Blockchain will enable financial institutions to settle securities in minutes instead of days; manufacturers to reduce product recalls by sharing production logs along their supply chain; and businesses of all types to more closely manage the flow of goods and payments. Blockchain brings together shared ledgers with smart contracts to allow the secure transfer of any asset — whether a physical asset like a shipping container, a financial asset like a bond or a digital asset like music — across any business network. IBM is working with companies ranging from retailers, banks, and shippers to apply this technology to transform their ecosystems through open standards and open platforms.

In April 2023 National University of Singapore (NUS) School of Computing and the IBM Innovation Center for Blockcha (ICB) are collaborating to develop a module on fintech. The aim is to enhance students’ knowledge and skills. Blockchain is a fast growing area across the globe, with banking, healthcare and the government leading the way in terms of adoption.

“Blockchain is one of the most disruptive technologies in computing today, and it is impacting many industries including financial services, trade, healthcare, and supply chain. This collaboration with the National University of Singapore School of Computing will help prepare a future workforce that is born on blockchain, ready to implement, improve and innovate: core skills required for Singapore to achieve its vision as a Smart Financial Centre and Smart Nation,” said Robert Morris, Vice President Global Labs, IBM Research.

IBM’s PowerAI system use combination of deep learning, machine learning, and AI and deploys a fully optimized and supported platform for your business.

What happened to IBM Watson?

At launch, IBM’s Watson was suggested to have boundless applications, from spotting new market opportunities to tackling cancer and climate change, however, these great expectations collapsed under the complications of building real world medical applications. 

Oncologists at University of North Carolina abandoned Watson after using it for a year at the institute on cancer genetic data to spot mutations. The decision to let go of Watson was due to its lack of flexibility in diagnosis and because, as physicians claimed, Watson did not produce better outcomes than traditional diagnosis methods. [4]

MD Anderson Cancer Center terminated the “the Oncology Expert Advisor” project which relied on Watson to analyze patients’ EHRs and suggest treatment recommendations. After adopting Watson, MD Anderson switched to a new EHR system and Watson wasn’t able to decipher unstructured physician’s notes or patients’ historical data, for instance Watson couldn’t reliably distinguish the acronym for Acute Lymphoblastic Leukemia “ALL” from physician’s shorthand for allergy “ALL” introducing very confusing and risky treatments. [5]

Strategic Mergers and Acquisitions

A strategy based on hiring experts of a relative area and gain power from cooperates.

‘Build a network of like-minded people, whether it is a digital community or an in-person one. Establishing your network and growing your connections is vital to becoming a new collar worker.’

– Randy Tolentino, Software Developer, Austin, TX.

April 2023: Early 2023 IBM announced that they will be acquiring myInvenio, an Italian startup that builds and operates process mining and digital twin of an organization software. IBM and myInvenio had worked together since November 2023 to integrate myInvenio technologies on IBM’s Cloud Pak for Business Automation run on OpenShift. The acquisition will provide myInvenio’s capabilities to IBM’s business partners to enable customers to generate data-driven insights about their business processes and optimize their digital transformation roadmap.

June 2023: IBM has acquired Turbonomic, an AI-powered cloud Application Resource Management (ARM) and Network Performance Management (NPM) software provider, to launch Watson AIOps which uses AI to automate IT operations. This acquisition complements IBM’s strategy to become a hybrid cloud and AI company as Turbonomic’s tools rely on AI to automate management, analyze performance, and suggest changes to meet network usage requirements.

July 2023: IBM has acquired Red Hat, a global open source enterprise software provider, for $34B, which is claimed to be IBM’s largest acquisition ever. Red Hat’s open hybrid cloud technologies would enable IBM to progress in the cloud infrastructure market where it used to lag behind tech giants such as Amazon and Microsoft. In August 2023, IBM announced the launch of their software portfolio to Red Hat OpenShift, Red Hat’s Kubernetes-based container platform which runs on Linux and integrated automation solutions such as robotic process automation (RPA), document processing, workflows and decisions. This step allows IBM users to run OpenShift on AWS, Azure, Google Cloud Platform or IBM’s own cloud, among others such as DB2, WebSphere, API Connect, Watson Studio and Cognos Analytics.

April 2023: The combination of digital solutions-artificial intelligence-machine learning. New solutions aim to bring real-time cognitive insights to the industry. AI does not just simply gather data, will help eliminate inefficient processes and redundant tasks to understand the actions. Using data will be more sense and reasoning for the cognitive computing of IBM.

The era of cognitive systems

The sectors have already used or planned on using of cognitive systems are:

Though IBM is one of the major providers of AI solutions to enterprise they are not the only one and they are not active in all areas of AI. You can check out AI applications in marketing, sales, customer service, IT, data or analytics. And If you have a business problem that you want to solve where AI can be helpful:

IBM’s history of AI research

IBM has been a leader in AI research since the field’s early days in the 1950s, when Arthur Samuel developed a checker player that learned from experience. In 1961 he put his program up against the Connecticut state checker champion, the number four ranked player in the nation. His checkers program won. This work was one of the earliest and most influential examples of machine learning. Forty years later, IBM Research’s chess-playing program Deep Blue made history when it beat Gary Kasparov, becoming the first chess-playing program to defeat a reigning world champion. We continue to take on new challenges, including Jeopardy! and Go. Summarily here the list of IBM’s contributions to AI:

Deep Blue — Computer Chess (1997):

IBM chess machine Deep Blue defeated World Chess Champion Garry Kasparov in a six-game match. Thanks to Its successful algorithms, Deep Blue’s victory has a fundamental part of the AI history and development.


‘In the early 1990’s, IBM Researcher Gerry Tesauro demonstrated that reinforcement learning (RL), hitherto regarded as a mere theoretical curiosity, could achieve spectacular success in complex real-world problems. The ensuing intense interest led to RL becoming one of the most important areas of machine learning research, particularly for tasks requiring automated decision-making. Using “temporal difference” RL combined with a neural network, TD-Gammon played millions of games against itself, in the process developing a level of play on par with world champion human backgammon players. Considering that it started from a completely random initial strategy, used only the raw board state (with no hand-crafted features), and used only the binary win/loss signal at the end of the game to guide its learning, this result shocked the machine learning world.’[3]

RL in real-world domains including elevator control, production scheduling, network routing, financial trading, spoken dialog systems, power plant control, and video game AI.

Infomax Principle for Neural Network Learning

Ralph Linker’s discovery that a standard (Hebbian) learning rule, combined with locally correlated random activity, causes a model visual system network to automatically form “neurons” that respond selectively to light-dark edges having a preferred orientation, and to organize a layer of these neurons

The infomax principle addresses a general feature of biological information processing — the brain’s ability to learn automatically to recognize visual, auditory, and other features present in the environment.


[*] Summarized from the IBM Annual Report 2023

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





How Juno Broke The Distance Record For Solar

NASA’s Juno spacecraft has blown through the ribbon at the end of the robot race, breaking the record for farthest distance traveled using solar power. The spacecraft is on its way to rendezvous with Jupiter on July 4.

Solar-powered spacecraft are very common, but they are never used this far out into space, and for good reason. The amount of sunlight at Jupiter is 25 times lower than what we get here on Earth. This wouldn’t usually be enough energy to power a basketball court-sized spacecraft like Juno, but it is, and that’s due to a few different factors.

A Place In The Sun

Galileo was the first and only other spacecraft to orbit Jupiter. When it launched in 1989, solar panel efficiency was too low to work in deep space–only about 12 percent of the light’s energy got turned into electricity. But when Lockheed Martin designed Juno in 2003, the efficiency had more than doubled to 28 percent, just enough to make the first deep space mission powered only by the sun’s slight touch from 500 million miles away.

Usually robotic crafts like the Curiosity rover, Cassini, New Horizons, and Galileo receive their power from an onboard RTG, or Radioisotope Thermoelectric Generator. These work by using the heat released by a radioactive material–in this case, plutonium–to generate electricity. An RTG is a great way to provide energy and warmth to an expensive spacecraft’s fragile electronics in the cold void of space, but there are a few catches to using an RTG. Radioactive material is incredibly dangerous, increasing the liability of a launch, and very expensive. It’s also difficult to produce in an era of nuclear non-proliferation–which is why NASA only has a few RTG spacecraft batteries left.

To compensate for its extreme distance from the sun (500 million miles), Juno’s solar array is huge–it has three wings of solar cells, each 30 feet long. NASA/Lockheed Martin

When the concept for Juno first started, there was still enough plutonium around to give Juno an RTG power source, but that never factored in.

“We never even considered designing Juno with an RTG,” says Juno engineer Kevin Rudolph from Lockheed Martin. “We knew all along we would do it with solar power.” Because Juno was one of NASA’s lower budget New Frontiers missions, like New Horizons and the asteroid-hunter Osiris-Rex, the Juno team had to keep things simple. “We knew roughly how expensive the Cassini RTGs were, and they were built when there was still a decent amount of plutonium left, “ explains Rudolph. “Even with a good supply of plutonium they were still very expensive so it wasn’t an option for us.”

Built For Efficiency

Juno has three 30-foot solar array wings, outfitted with a total of 18,698 individual solar cells. In total they put out about 500 watts of power, which is more than enough to operate all of Juno’s science instruments and keep the electronics warm.

It’s not just the updated solar efficiency that makes this mission possible, but the inherent design of every single element on the spacecraft. Scientists and engineers built the instruments to be as energy efficient as possible, and even planned the mission trajectory to utilize the solar cells.

The engineers at Lockheed designed Juno so that it would face the sun most of the time, stocking up on solar power. The unique polar orbit keeps Jupiter from eclipsing the spacecraft, providing a constant stream of electricity.

To make sure this system worked before they strapped Juno onto a rocket, the engineers put the solar cells in a test chamber and cooled them to a frosty -290 degrees Fahrenheit, the temperature the spacecraft would live in while at Jupiter. While the panels were frozen, the team shined a very dim light on them and simultaneously blasted them with an electron gun to mimic the harsh radiation environment at Jupiter. “By doing this test we were able to calculate how many solar panels we would need in order to run Juno,” explains Rudolph. “Turns out we needed a 50-square-meter surface, and we divided that into three separate wings.”

Not Quite Everywhere Under The Sun

Because NASA only has a few plutonium reserves left, it ultimately limits the number of missions that can venture out to places like Saturn, Neptune, Uranus and the Kuiper Belt. Luckily, though, just last year the Department of Energy teamed up with NASA to begin producing a new supply of Plutonium-238, eventually restocking NASA’s shelf of RTG’s.

Artist illustration of Juno spacecraft orbiting Jupiter. NASA

Juno arrives at Jupiter on July 4 after a 1.7 billion mile journey. When it arrives will break a few more records, too; not only will it be the fastest spacecraft ever, moving away from Earth at a whopping 165,000 miles per hour, but Juno will orbit closer to Jupiter than any spacecraft in history, providing the highest resolution images ever taken of the planet. Not bad for a smaller budget, solar-powered science robot!

Juno’s mission will be to figure out if Jupiter has a solid core or not, and it will peek through the atmosphere to better understand how gas giants like Jupiter form. Because Jupiter is the largest planet in our solar system, it holds many answers to how our solar system formed, and might even give us a better idea of how Earth formed as well.

Popular Science will be at NASA’s Jet Propulsion Lab for Juno’s flyby! Follow our coverage here.

Correction 7/1/2023 at 14:33PM: A previous version of this story misstated Juno’s distance from the sun. We regret the error.

Why Are Iot And Ai Perfect Partners To Boost Business Productivity?

Most businesses these days majorly rely on the Internet of Things (IoT) and Artificial Intelligence (AI) to drive business growth and envisage the next big trends. Today, it is easier for companies to collect more and more data thanks to IoT. When coalescing IoT with

Role of AI and IoT in Improving Enterprise Management

The rise of IoT devices creates a massive amount of data in a short period of time. This could be in any form like text, audio, video, image, or even in an unstructured form. It would be difficult for humans to process the data and thereafter provide its analysis. As data accrues continuously in the IoT systems, leveraging AI is one of the effective ways to use it for optimization. The integration of AI and IoT is progressively influencing how businesses are operating and sing their profits. They allow enterprises to move towards the cloud as an organization requires a large amount of computing power while running with AI. Both technologies are able to assist companies to better engage and satisfy their customers. In fact, it is said that over 85 percent of consumer relationships with a brand will be driven by AI in the next coming years. In this context, the world is already seeing the use of chatbots that are perfectly delivering personalized customer experience by responding to customers’ queries with reliable answers. Many industry experts consider that AI and IoT are no longer in use separately. AI closes the loop in an IoT environment where IoT devices accumulate or create data, while Artificial Intelligence assists in automating essential choices and actions based on that data. Currently, most organizations using IoT are only at the first visibility phase where they can perceive ongoing events through IoT assets. The technologies can help organizations to make accurate predictions, which can assist them to run more efficiently. Also, AI and IoT will provide them useful insights into those time-consuming and redundant tasks that can be automated or fine-tuned to become more effective. Since the majority of the population nowadays is shifting to urban areas, it has become more significant to strike a perfect balance between supply and demand. Fortunately, by leveraging AI and IoT, business leaders can better manage their inventory and ease the pressure on their stock by knowing when to refill items. This helps relieve marketers from having to purchase too many products and then finding that they cannot sell all of them. So, leveraging AI and IoT will be more beneficial to them compared to the current manual methods they are using.  

Leveraging the Combined Strategy of AI and IoT

As companies are racing to wear the tech leadership cap, they are increasingly drafting strategies for the internet of things, reviewing additional jobs on this tech and garnering more values in their current IoT installation using artificial intelligence. With the basic command of flow diagrams together with trends and forecasts, the use of AI and IoT combined helps get a very clear idea for further research. By integrating IoT with AI, the startup capital for an IoT project can grow rapidly as they are positioned on the top. A large number of companies across diverse industries are starting or already proceeded to learn more about the amalgamation of AI and IoT in order to provide new services as well as operate more effectively the service suppliers for IoT. IoT applications and implementations are essentially influenced by Artificial Intelligence. Whether it is a small investment or a startup, companies that have begun to unite IoT with AI accomplished a larger profit over the past decades. Now, major IoT providers are started to offer integrated AI applications based on machine learning analytics. Moreover, IoT applications provide machine learning-driven analytics which is part of AI that identifies the patterns and detects irregularities in the information generated by the smart sensors and devices.  

Wrapping Up

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