With advanced software, these companies are making cognitive computing accessible.

Business applications of cognitive computing are gaining popularity rapidly. Cognitive computing technology combines machine learning, reasoning, NLP, speech vision, and human-computer interaction in a way that mimics the human brain to improve decision-making. This AI-powered capability has the potential to transform several industries, right from sales forecasting, improving communications, supply chain operations, to better drug discovery, marketing, defense, fraud detection, financial sector, and agriculture.

Tech companies that have released these applications are working on preparing products and services to help clients put data to better work.

• OPTIMIZING THE FINANCING SERVICES INDUSTRY WITH COGNITIVE COMPUTING
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Innovative Cognitive Computing Companies

Aisera

Aesera’s AI Service Management Platform (AISM), helps customers and employees by optimizing processes for better productivity and slashed costs. The platform connects automated service experience with AI-based conversational engagement and workflow automation.

Accenture

Accenture aims to leverage all of its clients and their processes with the company’s unique approach to scaling AI, analytics, data, and automation. With applied intelligence, Accenture’s teams help organizations to invest in the right solutions and services that will suit their business goals.

AWS Machine Learning

AWS’s machine learning services and supporting cloud infrastructure, enable every developer, data scientist, and expert practitioner to use machine learning capabilities. At present, AWS is helping more than a thousand clients accelerate their machine learning capabilities.

Alteryx

Alteryx, provides a platform that facilitates end-to-end analytics process automation. The company recently announced new products that innovatively deal with analytics and data science automation, analytics in the cloud, AI, and machine learning. These new launches focus on delivering a simple user experience with no-code, low-code approaches to leverage business outcomes.

C3 AI

C3 AI, provides enterprise AI software that accelerates digital transformation with fully integrated products like C3 AI Suite (an end-to-end platform for AI applications), C3 AI Applications (a bundle of industry-specific SaaS AI apps), C3 AI CRM (CRM applications for AI and ML), and C3 AI Machina, a no-code AI solution for everyday data science.

SparkCognition

SparkCognition, provides three cognitive computing software for enterprises, SparkPredict, SparkSecure, and MindFabric. SparkPredict uses sophisticated algorithms to large pools of data with intelligence. SparkSecure Cognitive Insights add a cognitive layer to security solutions to improve threat detection, leverage IT abilities, and reduce the probability of false positives. MindFabric platform acts as a workspace for professionals for deep data-led insights.

Microsoft Cognitive Services

Microsoft’s Cognitive Services boosts Microsoft’s machine learning APIs to help developers easily add intelligent features like emotion detection, voice recognition, and language understanding. With just a few lines of code, developers can build apps that can work across devices like iOS, Android, and Windows.

Expert System

Expert System, provides software that is capable of working with language and technology to make sense out of unstructured content. Clients can extract insights and make human-level decisions with strengthened analytics. This software comprehends multiple languages, just like humans.

IBM Watson

IBM Watson performs deep content analysis and uses evidence-based reasoning to leverage and improve decision making, reducing costs for better outcomes. For this, the software uses

A set of transformational technologies that use natural language, hypothesis generation, and evidence-based learning. Experts believe that Watson holds the power to transform the process of business problem solving as the system uses machine learning, statistical analysis, and NLP to find answers amidst the clues. Watson then compares the answers by ranking them based on confidence and accuracy.

Deepmind

Deepmind aims to solve intelligence-based business problems with the research. Deepmind uses real-world applications of AI technology to help industries like healthcare. It enables nurses, doctors, and support staff to quickly analyze test results, forms the right diagnosis and treatment, and escalate the case to a specialist. All these judgments can be made using the advanced technology of accurate analysis.

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India is gradually going forward in establishing artificial intelligence in its various sectors.

The IT sector of India has always been a hub of great minds and it has played a vital role in the economic growth of the country. Around 2018,  the government think-tank, National Institution for Transforming India (NITI) Aayog emphasized the development of AI research in the IT sectors. This development came on the heels of the launch of a Task Force on Artificial Intelligence for India’s economic transformation by the Commerce and Industry Department of the Government of India in 2017. Since then, AI is gradually being adopted in every sector of India. Experts’ reviews show that healthcare and agriculture are so far among the most important sectors of focus to improve living conditions for India’s citizens.

The government lends a hand

With the government’s inclination towards initialization, more AI initiatives are taking place and a pool of funds are being created for relevant startups. Mayank Kapur, CTO of Indian AI startup Gramener, explained in an interview that the government is still the largest potential customer for data science services in the country. There have been several public sector initiatives encouraging the spread of AI. The government has initiated a proof of concept pilot in 15 districts (counties) in India to use artificial intelligence-based real-time advisory based on satellite imagery, weather data, etc. to increase farm yields where the farm production levels are low. Another long-term project to build a complete natural language processing platform for Indian languages is in operation. This would aid in the development of several applications, like conversational general and career counseling through chatbots and assistants, conversing in 22 Indian languages.

An interest in AI

The level of interest in learning about AI and implementing it in the business is gradually growing in India. Industries have started working to skill their manpower to enable themselves to compete with other global players. Educational institutions have started working on their curricula to include courses on machine learning and other relevant areas. Individuals and professionals have started acquiring these skills and are comfortable investing in upgrading their skills.

“Indian society is not as forgiving to failure in entrepreneurship as the US or Europe”

Dr Nishant Chandra, the Data Science Leader of the Science group at AIG started so in an interview while talking about the high stakes of failure in India. The established startup leaders like Mr Professor Manish Gupta, CEO of VideoKen has described that there is a trend of copying the ideas in the AI market of India, which prevents the Indian potential from flourishing and participating in the global market.

What the future holds

The learning phase of the AI sector in India is evident given the state of AI adoption in the Western markets and it may last longer in India’s relatively underdeveloped economy. Aakrit Vaish, CEO of Haptik, Inc. also seems to suggest that in the next 10 years, the understanding of AI and how it works will potentially be more commonplace among most technical industry executives. He thinks that India may go in the direction that China has gone, become their economies. There are probably going to be pockets, Bangalore might be good at deep tech like robotics or research / Hyderabad being good at data/ AI training, Mumbai being good at BFSI and  Delhi for agriculture and government. Like China, most solutions will probably be applied to the local economy.

Komal Talwar from the Government of India’s AI Task Force has stated “We think AI could have a great impact in the health sector. There is a scarcity of good doctors and nurses, with AI the machine can do the first round of diagnostics. Staff can carry machines with them to help cut down on the physical presence needed for doctors.” According to her, the government is encouraging startups to have AI applications that have a social impact (AI in health, AI in education, etc), where startups compete to solve social problems.

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Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is “ahead” in the AI “race,” often by examining who is deploying or planning to deploy an AI system. But given the many problems AI faces as a technology its deployment is less of a clue about its quality and more of a snapshot of the culture and worldview of the deployer. Instead, measuring the AI race is best done by not looking at AI deployment but by taking a broader view of the underlying scientific capacity to produce it in the future.

AI Basics: The Minds We Create

AI is both a futuristic fantasy as well as an omnipresent aspect of modern life. Artificial intelligence is a wide term that broadly encompasses anything that simulates human intelligence. It ranges from the narrow AI already present in our day-to-day lives that focuses on one specific problem (chess playing programs, email spam filters, and Roombas) to the general artificial intelligence that is the subject of science fiction (Rachel from Blade Runner, R2-D2 in Star Wars, and HAL 9000 in 2001: A Space Odyssey). Even the narrow form that we currently have and continually improve, can have significant consequences for the world by compressing time scales for decisions, automating repetitive menial tasks, sorting through large masses of data, and optimizing human behavior. The dream of general artificial intelligence has been long deferred and is likely to remain elusive if not impossible, and most progress remains with narrow AI. As early as the 1950’s researchers were conceptualizing thinking machines and developed rudimentary versions of them that evolved into “simple” everyday programs, like computer opponents in video games.

Machine learning followed quickly, but underwent a renaissance in the early 21st century when it became the most common method of developing AI programs, to the extent that it has now become nearly synonymous with AI. Machine learning creates algorithms that allow computers to improve by consuming large amounts of data and using past “experience” to guide current and future actions. This can be done through supervised learning, where humans provide correct answers to teach the computer; unsupervised learning, where the machine is given unlabeled data to find its own patterns; and reinforced learning, where the program uses trial and error to solve problems and is rewarded or penalized based on its decision. Machine learning has produced many of the startling advances in AI over the last decade such as drastic improvements to facial recognition and self-driving cars, and has given birth to a method that seeks to use the lessons of biology to create systems that process data similar to brains: deep learning. This is characterized by artificial neural networks where data is broken down to be examined by “neurons” that individually handle a specific question (e.g. whether an object in a picture is red) and describes how confident it is in its assessment, and the network compiles these answers for a final assessment.

But despite the advances that AI has undergone since the machine learning renaissance and its nearly limitless theoretical applications, it remains opaque, fragile, and difficult to develop.

Challenges: The Human Element

The way that AI systems are developed naturally creates doubts about their ability to function in untested environments, namely the requirement of large amounts of data inputs, the necessity that they be nearly perfect, and the effects of the preconceived notions of its creators. First, lack of, or erroneous, data is one of the largest challenges, especially when relying on machine learning techniques. To teach a computer to recognize a bird, it must be fed thousands of pictures to “learn” a bird’s distinguishing features, which naturally limits use in fields with few examples. Additionally, if even a tiny portion of the data is incorrect (as little as 3%), the system may develop incorrect assumptions or suffer drastic decreases in performance. Finally, the system may also recreate assumptions and prejudices—racist, sexist, elitist, or otherwise—from extant data that already contains inherent biases, such as resume archives or police records. These could also be coded in as programmers inadvertently impart their own cognitive biases into the machine learning algorithms they design.

This propensity for deep-seated decision-making problems, which may only become evident well after development, will prove problematic to those that want to rely heavily on AI, especially concerning issues of national security. Because of the inherent danger of ceding critical functions to untested machines, plans to deploy AI programs should not be seen primarily as a reflection of their own quality, but of an organization’s culture, risk tolerance, and goals.

The acceptability of some degree of uncertainty also exacerbates the difficulties in integrating AI with human overseers. One option is a human-in-the-loop system where human overseers are integrated throughout the decision process. Another is human-on-the-loop system where the AI remains nearly autonomous with only minor human oversight. In other words, organizations must decide whether to give humans the ability to override a machine’s possibly better decision that they cannot understand. The alternative is to cede human oversight that may prevent disasters that might be obvious to organic minds. Naturally, the choice will depend on the stakes: militaries may be much more likely to allow a machine to control leave schedules without human guidance rather than anti-missile defenses.

Again, as with doubt about decision integrity, the manner in which an organization integrates AI into the decision-making process can tell us a great deal. Having a human-in-the-loop system signals that an organization would like to improve the efficiency of a system considered mostly acceptable as is. A human-on-the-loop system signals greater risk tolerance, but also betrays a desire to exert more effort to catch up to, or surpass, the state of the art in the field.

The Global AI Race: Measuring the Unmeasurable

Research and development funding is a key component of scientific advances in the modern world, and is often relied on as a metric to chart progress in AI. The connection is often specious, however; the scientific process is often filled with dead ends, ruined hypotheses, and specific research questions with no broader significance. This last point is particularly salient to artificial intelligence because of the tailored nature of specific AI applications, which requires a different design for each problem it tackles. AI that directs traffic, for example, is completely worthless at driving cars. For especially challenging questions (e.g. planning nuclear strategy), development is an open-ended financial commitment with no promise of results.

It becomes difficult, therefore, to accurately assess achievement by simply using the amount spent on a project as a proxy for progress. Perhaps money is being spent on dead ends, an incorrect hypothesis, or even to fool others into thinking that progress is being made. Instead, we should see money as a reflection of what the spender values. Project spending then is not an effective metric of the progress of AI development, but of how important a research question is to the one asking it.

But that importance provides a value for analysis, regardless of its inapplicability to measuring the AI race: the decision-making process can speak volumes about the deployer’s priorities, culture, risk tolerance, and vision. Ironically, the manner in which AI is deployed says far more about the political, economic, and social nature of the group deploying it than it does about technological capability or maturity. In that way, deployment plans offer useful information for others. This is particularly valid in examinations of government plans. Examination of plans have produced insight such as using Chinese AI documents to deduce where they see weakness in their own IT economy, finding that banks overstate the use of chatbots to appear convenient for their customers, or noting that European documents attempt to create a distinctive European approach to the development of AI in both style and substance. It is here that examinations of AI deployment plans offer their real value.

There are instead much better ways to measure progress in AI. While technology rapidly changes, traditional metrics of scientific capacity provide a more nuanced base to measure AI from and are harder to manipulate, which makes them more effective than measuring the outputs of AI projects. The most relevant include: scientists as a proportion of population, papers produced and number of citations, research and development spending generally (as opposed to the focus on specific projects), and number of universities and STEM students. Measuring any scientific process is naturally fraught with peril due to the potential for dead-end research, but taken broadly these metrics give a far better picture of the ability of a state or organization to innovate in AI technology. Multiple metrics should always be used however; any focus on a specific metric (e.g. research spending) will make it just as easy to game the system as relying on AI deployment does. Such a narrow focus also distorts the view of the AI landscape. Consider, for example, the intense insecurity over the position of the United States despite its continuing leadership in terms of talent, number of papers cited, and quality of universities.

Recharging the Scientific Base

The U.S. National Security Commission on AI draft report notes, “The nation with the most resilient and productive economic base will be best positioned to seize the mantle of world leadership.” This statement encapsulates the nature of the AI race, and naturally, measuring it. If a government or a company wishes to take a leadership position in the race, the goal should be to stimulate the base that will produce it, not actively promote a specific project, division, or objective. This involves tried and true (but oft neglected) policies like promoting STEM education, training new researchers internally, attracting foreign talent with incentives, providing funding for research and development (especially if it forms a baseline for future work such as computer security or resilience), and ensuring that researchers have access to the IT hardware that they need through adequate manufacturing and procurement processes.

These suggestions are often neglected in the United States in particular because of intense politicization of domestic priorities such as education policy (affecting universities), immigration policy (affecting the attraction of foreign talent), and economic policy (affecting manufacturing and procurement). At the same time, it is not only about providing more funding but streamlining processes that enable scientific capacity. For example, the system for receiving scientific research grants is byzantine, time-consuming, and stifling with different government agencies having overlapping funding responsibilities. Efforts should be made to ensure that applying for grants is not only easier, but that it promotes broader scientific inquiries. By solving problems like these, leaders invest in the components that will create the winning position in the AI race, and observers can determine who is making the strides to lead now, as well as in the future.

In the information age, the deployment of new technologies and their level of advancement have become key metrics in measuring power and effectiveness, but these are often flawed. Particularly for AI projects, research budgets, task assignments, and roles relative to humans demonstrate little about the state of the technology itself. Given the many fundamental problems with deploying AI, risk tolerance and strategic culture play much more of a role in determining how it is carried out: the more risk tolerant an organization is and the more it feels challenged by competitors, the more likely it will adopt AI for critical functions. Rather than examining AI deployment plans to see which country or organization is “ahead,” we should use them to study their worldview and strategic outlook. Instead, we should rely on overall scientific capacity to determine pole positions in the AI race.

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For years, the sheer messiness of data slowed efforts to launch artificial intelligence (A.I.) and machine learning projects. Companies weren’t willing to wait a year or two while data analysts cleaned up a massive dataset, and executives sometimes had a hard time trusting the outputs of a platform or tool built on messy data.

Data pre-processing is a well-established art, and there are many tech pros out there who specialize in tweaking datasets for maximum validity, accuracy, and completeness. It’s a tough job, and someone has to do it (usually with the assistance of tools, as well as specialized libraries such as Pandas). But now IBM is trying to apply A.I. to this issue, via new data prep tools within AutoAI, itself a tool within the cloud-based Watson Studio.

“We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources,” Rob Thomas, General Manager of IBM Data and AI, wrote in a statement.“The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building ML models and experiments faster.”

In addition to data cleanup, AutoAI includes a number of other tools for building A.I. and ML algorithms, including ones that set optimal hyperparameters (which are the parameters with values set before the machine’s learning begins). There’s also IBM Neural Networks Synthesis, or NeuNetS, which creates customized neural networks (users are asked to optimize for either speed or accuracy).

IBM is competing fiercely with Google (which is plunging into the ML-automation game with AutoML Video and AutoML Tables, with other tools surely on the way) and Microsoft (which has automation and recommendation tools built into its Azure Machine Learningplatform) to claim the attention of companies interested in the A.I./ML market. If that wasn’t enough of a crowded landscape, Amazon is plunging heavily into the enterprise-automation game with Amazon Personalize, which streamlines everything from mobile-app development to email marketing.

Of course, the rise of A.I./ML automation could lead to a new host of problems. Sure, having tech professionals build bespoke algorithms and tools in-house is a painstaking process with a fair amount of risk (if you fail, you’ve burned tons of time and resources), but there’s the reasonable expectation that you’ll have something tailored to your needs, based on reliable data and math. When you begin to automate these processes, you risk obfuscating at least a portion of the data and logic behind dashboards—which might lead some to question the output of the work.

Then again, many firms can’t afford to even begin an internal, customized A.I./ML program; in that context, these automated solutions are the best (and perhaps only) bet if they want to get into this particular game.

For tech professionals, these new tools are yet another sign that the A.I./ML market is maturing. Those professionals who understand how these tools work—as well as the underlying logic and theories—will have their pick of positions, as companies desperate for A.I./ML talent are willing to pay enormous salaries and benefits. Although these technologies might seem daunting, there are a number of resources designed to give you a solid education; check them out.

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