Whenever we use our smartphone or any other similar gadget, we do something over data. Data is generated enormously with every passing second, just because the number of people using the internet is increasing, as well as the time they spend over the internet is also increasing exponentially.

The data that is generated is unstructured. It has to be analyzed and organized according to the company’s requirements. The data is generated from various resources and simple BI tools are unable to process that data. Here, Data Science comes into play. The need for more complex and advanced analytics algorithms and tools for processing, analyzing, and making meaningful conclusions and insights out of it, has made Data Science a crucial and inevitable requirement of every business.

The experts predicted that by 2020, there will be 40 zettabytes of data in existence, and this will make career opportunities in Data Science shoot up drastically. And the shortage of skilled professionals makes Data Science a hot career opportunity for the related candidates.

When we are living in the era of Big Data, you would wish to launch a career in the field of Data Science. This article will let you know how promising the future becomes if you take up a Data Science online course and become one of the hottest certification holders.

Data Science as a Career

Data Science abbreviates statistical preparation, programming skills, visualization techniques, and a good deal of business sense which refers to the ability to turn business matters into answers with current or upcoming data.

So, you need to have a blend of skills in mathematics, spotting or finding out trends, and computer engineering. The main role of a data scientist is to decipher large volumes of data and prepare a plan for further analysis to find out trends and other meaningful insights.

Since the field of data science is so vast that there is no perfect definition for it, so is the career in Data Science. You may have many options to choose from as a data scientist. Some roles that come under data science are:

●Data Analyst

●Data Architect

●Statistician

●Data Mining Engineer

●Business Intelligence Analyst

●Data Scientist

●Senior Data Scientist

●Analytics Manager

●Research Analyst

●Data Science Architect

The above list shows you that there are many career options in Data Science and every position requires you to have some specified skill-set. You can choose any of the job roles according to your area of interest and the level of knowledge.

To start a career in Data Science, you must possess hard skills like machine learning, analysis, statistics, Hadoop, etc. apart from these, depending on the job role you may need to excel in persuasive communications, critical thinking, and problem-solving.

In this article, we will discuss the job role of “Data Science Architect”.

Data Science Architect

Data Science Architect is considered as a new role in data that businesses should take into account. A Data Science architect is a job role that is a mix between a data scientist and a data engineer.

We have already read about data scientists. Now, a data engineer is a candidate who is involved in preparing data for operational or analytical uses. The task of data engineer may typically include building data pipelines to bring together information from various sources, integrating, cleansing, and structuring data for usage in individual analytics applications.

The Data Science Architect or DSA comes in between the two. The task of DSA is to deal with the design of data, analysis, and storage processes while taking into consideration cost and time trade-offs and requirements of the business.

A DSA starts with an analysis of a company’s requirements with the end goal of using data to generate values. With the goal in mind, DSA needs to design the architecture and the analytics pipelines along with considering appropriate time frames, and costs.

For the best structuring data strategy and the roles in an organization, Data Science Architect can provide great assistance.

Let us now see how to become a Data Science Architect.

How to Become a Data Science Architect?

The main task of a data architect is to create a blueprint of the data management system. That strong background in computer science. Let us look at the steps required to become one.

1.Pursue a degree in computer science, computer engineering, or related field.

A strong background of computers includes coverage of data management, big data developments, system analysis, and technology architecture.

2.Technical and business skills.

Technical skills required for a data architect include data modeling tools, application server software, Database Management system software, agile methodologies, user interface, and query software, UML, ETL Tools, Hadoop, and NoSQL databases, machine learning, data visualization, development environment software, etc.

Business skills for data architects include analytical problem-solving, expert management, effective written and verbal communication skills, and understanding the way your industry functions.

3.Go for Certification and training.

A certification always keeps you ahead of other similar candidates when it comes to job opportunities. There are many certifications to choose from to become a data architect.

Taking up an online training course for getting certified is the best choice you can go for. This is because becoming a data architect involves studying a lot of material and expertise in many technologies.

The training providers make it easy for you to sort the things and prepare the study material that is specifically designed according to your knowledge level.

Conclusion

Becoming a Data Architect in the world of Big Data can really be rewarding. The average annual salary of a Data Architect is $123,680 according to Glassdoor, and that of senior data architect is $132,312.

With such a high salary and shortage of skilled professionals, this position has become one of the favorite career options among IT professionals.

Taking up an online training course for getting certified has several benefits. It gives you the flexibility of learning at your own pace and learning hours according to your convenience. It gives you options to choose from modes of learning as well. You can go for online training, blended learning, or instructor-led training.

So, go ahead and get your registration done so that you can get certified.

The post How to become a data science architect? appeared first on Artificial Intelligence.

Whenever we use our smartphone or any other similar gadget, we do something over data. Data is generated enormously with every passing second, just because the number of people using the internet is increasing, as well as the time they spend over the internet is also increasing exponentially.

The data that is generated is unstructured. It has to be analyzed and organized according to the company’s requirements. The data is generated from various resources and simple BI tools are unable to process that data. Here, Data Science comes into play. The need for more complex and advanced analytics algorithms and tools for processing, analyzing, and making meaningful conclusions and insights out of it, has made Data Science a crucial and inevitable requirement of every business.

The experts predicted that by 2020, there will be 40 zettabytes of data in existence, and this will make career opportunities in Data Science shoot up drastically. And the shortage of skilled professionals makes Data Science a hot career opportunity for the related candidates.

When we are living in the era of Big Data, you would wish to launch a career in the field of Data Science. This article will let you know how promising the future becomes if you take up a Data Science online course and become one of the hottest certification holders.

Data Science as a Career

Data Science abbreviates statistical preparation, programming skills, visualization techniques, and a good deal of business sense which refers to the ability to turn business matters into answers with current or upcoming data.

So, you need to have a blend of skills in mathematics, spotting or finding out trends, and computer engineering. The main role of a data scientist is to decipher large volumes of data and prepare a plan for further analysis to find out trends and other meaningful insights.

Since the field of data science is so vast that there is no perfect definition for it, so is the career in Data Science. You may have many options to choose from as a data scientist. Some roles that come under data science are:

●Data Analyst
●Data Architect
●Statistician
●Data Mining Engineer
●Business Intelligence Analyst
●Data Scientist
●Senior Data Scientist
●Analytics Manager
●Research Analyst
●Data Science Architect

The above list shows you that there are many career options in Data Science and every position requires you to have some specified skill-set. You can choose any of the job roles according to your area of interest and the level of knowledge.

To start a career in Data Science, you must possess hard skills like machine learning, analysis, statistics, Hadoop, etc. apart from these, depending on the job role you may need to excel in persuasive communications, critical thinking, and problem-solving.

In this article, we will discuss the job role of “Data Science Architect”.

Data Science Architect

Data Science Architect is considered as a new role in data that businesses should take into account. A Data Science architect is a job role that is a mix between a data scientist and a data engineer.

We have already read about data scientists. Now, a data engineer is a candidate who is involved in preparing data for operational or analytical uses. The task of data engineer may typically include building data pipelines to bring together information from various sources, integrating, cleansing, and structuring data for usage in individual analytics applications.

The Data Science Architect or DSA comes in between the two. The task of DSA is to deal with the design of data, analysis, and storage processes while taking into consideration cost and time trade-offs and requirements of the business.

A DSA starts with an analysis of a company’s requirements with the end goal of using data to generate values. With the goal in mind, DSA needs to design the architecture and the analytics pipelines along with considering appropriate time frames, and costs.

For the best structuring data strategy and the roles in an organization, Data Science Architect can provide great assistance.

Let us now see how to become a Data Science Architect.

How to Become a Data Science Architect?

The main task of a data architect is to create a blueprint of the data management system. That strong background in computer science. Let us look at the steps required to become one.

1.Pursue a degree in computer science, computer engineering, or related field.

A strong background of computers includes coverage of data management, big data developments, system analysis, and technology architecture.

2.Technical and business skills.

Technical skills required for a data architect include data modeling tools, application server software, Database Management system software, agile methodologies, user interface, and query software, UML, ETL Tools, Hadoop, and NoSQL databases, machine learning, data visualization, development environment software, etc.

Business skills for data architects include analytical problem-solving, expert management, effective written and verbal communication skills, and understanding the way your industry functions.

3.Go for Certification and training.

A certification always keeps you ahead of other similar candidates when it comes to job opportunities. There are many certifications to choose from to become a data architect.

Taking up an online training course for getting certified is the best choice you can go for. This is because becoming a data architect involves studying a lot of material and expertise in many technologies.

The training providers make it easy for you to sort the things and prepare the study material that is specifically designed according to your knowledge level.

Conclusion

Becoming a Data Architect in the world of Big Data can really be rewarding. The average annual salary of a Data Architect is $123,680 according to Glassdoor, and that of senior data architect is $132,312.

With such a high salary and shortage of skilled professionals, this position has become one of the favorite career options among IT professionals.

Taking up an online training course for getting certified has several benefits. It gives you the flexibility of learning at your own pace and learning hours according to your convenience. It gives you options to choose from modes of learning as well. You can go for online training, blended learning, or instructor-led training.

So, go ahead and get your registration done so that you can get certified.

Disclaimer: This is a company press release. No HT Journalist is involved in creation of this content.

The post How to become a data science architect? appeared first on Artificial Intelligence.

Wikipedia defines artificial intelligence as: In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals.

While AI is usually focused on learning, pattern recognition and problem solving, it can be combined with robotics to perform or assist in the performance of surgical tasks. Da Vinci is a leader in combining AI and robotics for surgery, but there are many other well-funded efforts underway worldwide.

We all know the impact robotics has had in industry, with the manufacture of an automobile being a prime example. Many fewer human hours are required to manufacture an automobile with a fully implemented robotic plant than one relying on human labor only. The robots in an automobile manufacturing plant simply perform repetitive tasks as programmed by their human masters, and they do not use AI to learn how to make a better automobile or improve efficiency themselves. That is left to the human mind.

The potentially malevolent side of AI combined with robotics was graphically portrayed in the 1968 Arthur C. Clarke and Stanley Kubrick movie: 2001: A Space Odyssey, in which the HAL 9000 computer with AI took over the spaceship Discovery One on the way to Jupiter, killing one of the crew members and threatening the other in order to preserve itself. In the end, the human intelligence and resourcefulness of the remaining crew member outsmarted the HAL 9000, which demonstrated the human quality of fear as it was disconnected.

In medicine, one of the most familiar applications of AI has been the reading of EKGs in which pattern recognition is critical. Still, a human analysis by a cardiologist skilled in the art nearly always follows the AI readout before a final diagnosis is made and treatment of a cardiac arrythmia initiated. In ophthalmology, the FDA-approved IDx-DR system (IDx Technologies), created in collaboration with the IBM Watson AI computer, can accurately grade the level of diabetic retinopathy with 87% sensitivity and 90% specificity. But it is not good at performing a differential diagnosis, and a skilled physician must confirm that the patient has diabetic retinopathy and not another retinal pathology such as a retinal vein occlusion.

AI combined with home diagnostics including preferential hyperacuity perimetry and OCT is being developed by Notal Vision and promises to allow patients to determine if they need an urgent visit to their doctor for an anti-VEGF injection or can delay another month. AI is good at flagging potentially abnormal findings in a diagnostic test, for example, encouraging the ophthalmologist to rule out keratoconus in a topography. It can also provide a differential diagnosis for any cluster of symptoms, signs or test results.

AI is here to stay in the field of medicine and, in my opinion, will be a virtuous partner to the ophthalmologist. Ophthalmology is highly dependent on images and diagnostic tests that will respond well to pattern recognition. Robotics combined with AI guidance systems are also entering our surgical field. I do not see AI or robotics as a threat to we ophthalmologists and expect both to be constructive partners as we strive to care for an increasing number of patients with an at best stagnant number of ophthalmologists.

The post AI can act as partner to ophthalmologists appeared first on Artificial Intelligence.

The Central Board of Secondary Education (CBSE) has integrated a new Artificial Intelligence (AI) course for class XI and XII in the current academic year (2020-2021). Developed in partnership with IBM, this course will be part of CBSE’s Social Empowerment through Work Education and Action (SEWA) program. It is being introduced in approximately 200 schools across 13 states in India including Delhi-NCR, Karnataka, Tamil Nadu, Orissa, Kerala, West Bengal, Andhra Pradesh, Telangana, Maharashtra, Madhya Pradesh, Uttar Pradesh, Rajasthan and Punjab.

Over the last few years, AI has gained prominence and is being used almost everywhere. This IBM AI curriculum is structured around a course framework for students consisting of base strands of knowledge (basics, history, applications), skills (design thinking, computational thinking, data fluency, critical thinking) and values (ethical decision making, bias). The course has been made robust with problem-based learning outcomes and assessment methods for teachers to build foundational skills of AI in students. The idea is to train students with the basics of AI.

“AI will certainly become all-pervasive in our lives in the coming years and it is important to inculcate the necessary skills & knowledge right from high school level. The unique proposition of the IBM AI curriculum is that it allows Grade XI & XII students from all streams, in addition to Computer Science, to build the foundation for themselves to be AI ready,” says Manoj Ahuja, Chairperson, CBSE.

The IBM AI Curriculum was launched in collaboration with CBSE in September 2019 to impart AI skills to 5,000 Grade XI students and 1,000 teachers across India. Following the program launch, a series of Principal Orientation and Teacher Training sessions were conducted between September 2019 and June 2020. As part of the partnership between IBM and CBSE for the AI Curriculum, training (online and classroom) for over 5,000 students was conducted resulting in a cumulative 408 hours of training workshops.

“The question on the minds of educators and economists alike is how will technology impact jobs moving forward and how can we prepare our students to succeed in an increasingly automated, AI-driven world. The objective of our exciting collaboration with CBSE is to help address some of those challenges by designing one of the most accessible and comprehensive gateways for students to begin their AI journey. As they think through designing innovative solutions to address key problems, we also get them deliberate about the ethical implications of the technology,” says Sandip Patel, General Manager, IBM India/South Asia.

This curriculum was co-developed with Australia’s Macquarie University and Indian implementation partners – Learning Links Foundation and 1M1B to meet CBSE’s requirements.

CBSE is India’s national level board of education for public and private schools, controlled and managed by the Government of India.

The post CBSE integrates Artificial Intelligence in high school curriculum, partners with IBM appeared first on Artificial Intelligence.

Can artificial intelligence understand human humor? According to Fei-Fei Li, professor in the Computer Science Department at Stanford University and co-director of Stanford’s Human-Centered AI Institute, the answer is: not yet.“Today’s technology is not there yet,” she said during an online event organized by the Kibbutz Shefayim-based company Zebra Medical Vision on Tuesday. “What is humor? What kind of sentiment does it carry? Humor requires a deep and nuanced reasoning which is not a strength of current AI.”

A former Google VP and one of the world’s expert in the field computer vision, in the talk Li highlighted how many Israeli researchers have impacted her over the course of her career.“I was very much looking forward to visiting Israel in person for this event, but the coronavirus has prevented me from doing so. It will need to happen in the future,” she said.In the lecture, the professor focused on different projects to shape the future of artificial intelligence guaranteeing a more ethical approach, a goal that Zebra, a healthcare company proving AI-based medical image diagnosis, also shares.Together with tremendous opportunities, Li acknowledged how the new technologies developed risk to enhance problems such as a wider gap between generations in interacting with machines, but also job displacement, bias and privacy infringements.“For this reason, we believe in a different approach to AI, a human-centered approach,” she pointed out, explaining that the goal is to carry out research with a concern for its human impact, with the idea of augmenting people’s capabilities rather than replacing them, as well as by drawing inspiration from human intelligence.

Among the projects illustrated by the computer scientist was the work to reduce bias in AI facial recognition.  “Today’s state of the art facial recognition algorithm is biased in recognizing people from different races, genders and backgrounds. How do we mitigate it from machine learning bias to machine learning fairness? It turns out that there is a whole slew of solutions, starting from datasets and algorithms,” she explained.Li said that AI can also have a significant impact in healthcare, from improving its delivery to help re-imagine its policies.The future of AI, the scientist argued, is developing the capability of interacting with the surrounding world.“Is today’s deep learning good enough for AI to form an understanding of human behavior and interfacing with humans? The short answer is no,” she said. “Current AI is powerful but is static, driven by simple reward functions, whereas human intelligence is dynamic, multi-sensory, complex, uncertain and interactive. The next wave of AI research is going to focus on this much more active perception and interaction with the real world.”

The post Can artificial intelligence understand human humor? appeared first on Artificial Intelligence.

Advances in the field of robotics which is a mix of computer science, mechanical and electronics engineering, and science have implied that machines or related types of automation currently do the work of humans in a wide range of settings, for example, medicine, where robots perform medical procedures previously done by the surgeon’s hand. Robots have made it simpler and less expensive for employers to complete work. The drawback, in any case, is that some sensibly well-paying jobs that gave white-collar class work to people have become the area of machines.

The present workforce faces various challenges that employees even 10 years back didn’t need to consider, fighting with virtual colleagues (bots, automation, artificial intelligence), the beginning of a near 24-hour work cycle, greater levels of demands (both internal and external) thanks to social media.

Fortunately, there are alternative perspectives on how robotics will affect our businesses. The overwhelming conclusion of industry specialists is that automation doesn’t spell the end of gainful employment for humans, but instead, the improvement of employment opportunities.

There is clear proof that points towards robotic automation much of the time being a supplement for human work, instead of an immediate substitute. As progressively mundane tasks are automated, human effort turns out to be increasingly significant as it is centered around more significant level tasks, creativity, know-how and thinking,” says David Whitaker, Managing Economist at Cebr. “Robots are about efficiency, they don’t do creativity well overall and they don’t do things that include failure, which is at the core of any design process.”

The standard case of a work supplanting technology is robots in car manufacturing plants. Assembly lines, once consisting of a progression of stations where five or six laborers were liable for introducing or connecting a particular vehicle part to a frame before it went to the next station, frequently now include a progression of robotic appendages in place of humans.

Today, this technology has to a great extent replaced direct human effort in building a vehicle. History is packed with instances of these work uprooting technologies. New innovations, for example, the computer this piece was written on, have to a great extent dispensed with a few occupations that were once normal in the twentieth century, for example, the typing pool.

If advances in artificial intelligence (AI) and automation are intensely weighted towards these kinds of technologies, as some anticipate, the potential for generous dislodging of laborers and further disintegration of the labor share is very high. However, this is far from unavoidable. The advancement of technologies that encourage new tasks, for which people are more qualified, might potentially lead to improved future for laborers.

While the widespread introduction of computers into workplaces positively dislodged a large number of secretaries and typists, the new assignments in related enterprises implied new occupations, including computer technicians, software developers, and IT consultants.

Today you have a remarkable opportunity to be increasingly productive, engaged and satisfied than at any time in history. Rather than gazing at your PC screen blankly, you can play a determinedly dynamic role in the business.

In case you’re a company leader, you face a chance to not just train your employees to oversee through the difficulties, yet additionally, inspire them. Rather than generic platitudes and occasional mandated morale boosters, you can be imaginative and free in your thinking. You can offer true leadership by setting a model and easing the anxiety of what the future may hold.

Beyond AI, redesigning and repositioning the workforce implies considering other technologies that will influence your employees. Consider making new working structures and working models that will improve any outcomes you see from automation, for instance. This will require executing new procedures and models through cautious, strategic and thoughtful change planning.

Remember that change isn’t just about technology. It’s about individuals. That is the reason to have leadership and coaching programs completely critical to setting up a workforce of the future. Solid leadership establishes the pace for the future, helps build trust and eventually leads to creating a corps of competent, engaged workers. When you’ve built up solid leadership and coaching programs, you’ll have to build up a clear strategy to support another culture of leadership with continuous engagement and digital campaigns. This is your chance to move from the normal to the extraordinary.

The long-run implication of technological advances for the labor market is, now, obscure. The fate and despair that regularly goes with analysis about the future of work is presumably quite untimely. Technology displaces jobs. However, it additionally makes new jobs and frequently in unpredictable ways. Humans are underrated. The dreaded adverse effect of AI and robots on the working environment is likely enormously exaggerated. Many of the most possibly transformative advances will, in any case, require a human touch.

The post ROBOTS CHANGING THE WAY WE WORK TODAY appeared first on Artificial Intelligence.

An Introduction To Artificial Intelligence:

In the field of computer science, artificial intelligence – sometimes termed as machine intelligence – is “intelligence” portrayed by machines of various kinds. This sort of intelligence is defined to be any device that perceives the environment it is surrounded by, and acts to maximize its chance of achieving specific goals.

Artificial intelligence is also used to describe machines that mimic cognitive functions that are associated with the human mind and its function, such as obtaining knowledge and solving various problems.

A phenomenon called the AI Effect takes place as machines gradually learn to solve increasingly difficult problems, as they do not require intelligent decisions and actions henceforth. Tesler’s Theorem has a very popular quip which is often quoted in various publishing as “AI is whatever has not been done yet”. This progress of intelligent problems becoming machine routine is the AI Effect.

AI systems are well known to demonstrate behaviours that are frequently associated with human intelligence and social intelligence. AI is now a very widespread phenomenon – it is present everywhere, from shopping list recommendations to facial recognition in pictures, from spam detection to fraud detection.

What can AI accomplish?:

There are two differing kinds of AI to understand what works how – Narrow Artificial Intelligence, and Artificial General Intelligence.

Narrow AI has had a vast number of applications emerge in the past few years. Interpretation of video feed from camera drones that carry out visual inspections of various infrastructures (oil fields, terrain analysis, etc.) is a specialized requirement that can be achieved by a specific kind of artificial intelligence.

Other such tasks include organization of all kinds of calendars, responding to customer-service queries utilizing chatbots and keyword recognition, co-ordination with other AI-enabled intelligent systems to carry out various kinds of tasks like hotel bookings at appropriate times and locations, spotting potential tumours and helping radiology as a field of work, flagging content found to be contextually inappropriate on the Internet, gathering all kinds of data from IoT devices and converting them into information and interpreting the next step in the plan, and so on.

Artificial General Intelligence, on the other hand, is a completely different ball game. It is very similar to the type of adaptable intellect found in human beings – a flexible form of intelligence capable of leaning how to carry out a wide range of varying tasks, from anything as simple as haircuts or data entry, to complex and complicated phenomena that can be solved from its previously accumulated experience.

AI can be commonly spotted in science fiction books, games, and movies, such as Skynet from the Terminator franchise, HAL from 2001: A Space Odyssey, and GladOS from the Portal series. Due to the varying behaviours of such machines, heated debates continue to happen over the sustainability of useful artificial intelligence, and the potential for the destruction of the human race should they become self-aware and pseudo-sentient.

Artificial Intelligence in business:

One of the strengths of AI systems is their learning potential from the wide range of scenarios that they can be exposed to. The more they see and experience, the more they learn. And where else would this work effectively but in the world of business, where past mistakes are the key to future breakthroughs?

A survey conducted by Harvard Business Review found that out of the 250 executives that are familiar with their companies’ use of cognitive technology, close to 75% of them believe that the use of AI will radically transform the organization within three years (as of February 2018). Their study conducted across 152 projects concluded that moon-shot projects are less likely to succeed than smaller projects that are cogs to improving their business process rather than transforming the business as a whole.

There are three kinds of business needs that AI can support as of now – automation of processes, data analysis and insights, and customer and employee engagement.

Robotic process automation is a breakthrough in administrative projects – instead of limiting humans to periodic routine tasks that come up again and again, automating such processes saves time, is more efficient and effective, has lesser margins of error, and is cheaper and delimiting. Human beings freed up from administrative work can be employed in sectors where AI cannot effectively work, such as those involving creative processes, ambiguous decision making, and diplomatic engagement.

Jim Walker, project leader for shared services organization in NASA, states, “So far it is not rocket science”.

Cognitive insight provided by machine learning differs from those available from traditional analytics. They are usually much more data-intensive and detailed on their part, and the models are typically trained on some parts of the data sets that are obtained by the systems; such model improvement leads to their ability to utilise new data to predict better and more accurately, and categorizing and organization of things gets better with time.

Various versions of machine learning projects attempt to mimic human brain activity in order to recognize patterns, which can be used to recognize images and speech in turn. This can help intelligent machines make new data available for better analytics. The labour-intensive past of data analytics pays off with the machines making probabilistic matches, where data identified is likely to be associated with the same institute, despite being present in a different format. This has been utilized profitably by various big-shot organizations, most notably General Electric – who integrated supplier data and saved USD 80 million in its first year due to redundancy elimination and improved contract negotiation.

In conclusion:

Artificial intelligence is not a thing of science fictions and dystopian novels as they become more commonplace and impact our various lives in a meaningful way (looking at you, Alexa). While AI is a new phenomenon to be accepted in mainstream society, it has been decades of work to significantly progress toward developing artificially intelligent systems, making them a technological reality.

Despite various warnings that AI will take over humanity one day, be it in the industrial sector or a species as a whole, we will never know until we take a decision – which we have to, in good time. Indecisiveness puts a pause on the progress of our society as a whole, but does nothing to stop the progress of time towards an inevitable extinction.

AI should rather be seen as a supporting tool. While it faces problems completing commonplace tasks in the real world, it is adept at processing and analysing mounds and piles of data much quicker and more accurate than a human being. Software enabled with AI return with synthesized courses of action and present them to the human user, who then has to take a step and decide for the system, providing a learning experience to them as to what makes a human think how, leading to a decision.

Such traits make AI highly valuable in the business sector, apart from various other industries, whether it comes to helping people around various tasks, or monitoring various physical aspects of a system and giving it meaningful insight to reduce the margin of error for human interpretation.

AI is also changing the way customer relationship management works in various fields of work. Application of artificial intelligence to software that required heavy human intervention turns a regular customer relationship management algorithm into a self-updating, auto-corrective system that monitors all pieces of all relationships effectively, always staying on top as a relationship manager for all employees and employers.

The post Powering Businesses Using Artificial Intelligence appeared first on Artificial Intelligence.

There is clearly a mixture of excitement about a future driven by digital and AI and a desire to better understand what it means and how to prepare for it. Everybody is discerning and working on AI and their digital future. Many executives have come to terms with the idea that disruption is a fact of life and that their companies need to transform.

But what exactly is AI and how can it shape the future of GRC?

Artificial intelligence (AI) deals with building smart machines capable of performing tasks that typically require human intelligence. Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”

According to Wikipedia, AI, sometimes called machine intelligence, is intelligence validated by machines, in contrast to natural intelligence displayed by humans and animals. It is the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

The term is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving”

Although artificial intelligence educes thoughts of science narrative, studies have shown that it already has many usages today:

• Spam filters on email;

• Personalization: Online services use artificial intelligence to personalize experience. Services, like Amazon or Netflix, “learn” from an individual’s previous purchases and the purchases of other users in order to recommend relevant content;

• Fraud detection: Banks, for instance, use artificial intelligence to determine if there is strange activity on an account. Unexpected activity, such as foreign transactions, could be flagged by the algorithm;

• Smart assistants (like Siri and Alexa);

• Disease mapping and prediction tools;

• Manufacturing and drone robots;

• Optimized, personalized healthcare treatment recommendations;

• Conversational bots for marketing and customer service;

• Robo-advisors for stock trading;

• Social media monitoring tools for dangerous content or false news; and

• Song or TV show recommendations from Spotify and Netflix.

Hardly a day passes without a news story about a high-profile data breach or a cyber-attack costing millions and millions of dollars in damages. Cyber losses are difficult to approximate, but the International Monetary Fund [IMF] places them in the range of US$100–$250 billion annually for the global financial services industry.

Furthermore, with the ever-growing pervasiveness of computers, mobile devices, servers and smart devices, the cumulative threat exposure grows each day.

While the business and policy groups are still beleaguered to shawl their heads around the cyber realm’s brand-new importance, the use of AI to cyber security is foreshowing even greater changes.

One of the fundamental purposes of AI is to automate tasks that heretofore would have required human intelligence. Cutting down on the labor resources an organization must employ to complete a project, or the time an individual must devote to routine tasks, enables terrific gains in efficiency.

The nature of risks is unremittingly fluctuating and evolving at unprecedented levels and hence implementing a successful risk management program is the call for organizations looking to safeguard their hard-earned reputation. Failure to do so could be injurious, as many organizations in the past have realized the hard way.

The standard organizational framework used to manage risk and compliance are the three [3] lines of defense:

• The first line of defence (functions that own and manage risks);

• The second line of defence (functions that oversee or who specialise in compliance or the management of risk); and

• The third line of defence (functions that provide independent assurance).

A key requirement of the lines of defense is the assistance provided to various levels of management. While first and second lines of defense are archetypally organized to support levels of management, the 3rd line of defense classically works with management and the board to surface risks and compliance issues and works to address slits and deficiencies.

In order to provide proper assistance for these levels of management, the lines of defense need to provide insights that enable:

• Enriched execution on a daily basis of the performance of risk and control activities;

• Finer and tenacious control and management of the activities, and

• Forward and outward looking comprehensions for strategic risk management.

Integrated GRC platform is the only solution to help businesses manage risks across the organization while driving overall enterprise performance and being flexible enough to keep pace with a rapidly-changing environment.

As these platforms allow companies to meet their GRC targets by automating the workflow, many organizations are espousing GRC platforms to augment their operational activities.

In this day and age of disruption, technology is a sturdy enabler of business. And arguably, few developments in technology have generated as much interest as AI. From digital assistants to streaming services, AI is ubiquitous, with seemingly endless possibilities. But beyond all the flimflam, what are the practical applications of AI in GRC?

Artificial Intelligence (AI) in GRC is the need of the hour. As companies expand their digital footprints, cyber security vulnerabilities increase due to huge amount of data being produced. Surely, the demand for intelligent use of accumulated risk data will only increase.

GRC solutions that incorporate AI and its application Machine Learning (ML), will play a key role. The key players in GRC industry are working hard to offer AI-as-a-Service (AlaaS), particularly to industries where data is too valuable.

A recent report  found that the use of artificial intelligence will bring about massive changes to GRC. By automating payments, calculating risk, and maintaining records, the study broke down how the technology will influence each role within GRC:

• Risk manager –With the rise of AI, risk managers’ tasks will fundamentally shift to data-based identification and interpretation of changes in risk exposures. This includes the ability to assess trends by exploring existing facts and applying cognitive skills to understand the analyses of large volumes of data;

• Compliance manager –With automated reports, the future responsibility of compliance managers goes one step further: identifying internal or external dangers as well as the management of cybercrime. This will require the ability to work adroitly and to solve problems independently;

• Fraud examiner – The role of the fraud examiner will shift intensely as artificial intelligence becomes more ubiquitous. The main tasks will move from reviewing reports to performing fraud assessments and developing KRIs for avoiding future cases of fraud;

• Auditor – The role of the auditor may not change markedly; and

• Treasury manager –With AI, the treasury manager must build up new expertise to be able to utilize technology to monitor liquidity and risk management, to monitor and optimize cash-flow streams, and to give recommendations to the executive board with regard to strategy development.

While many fear that the widespread use of automation will displace white-collar jobs, AI is far more likely to be used as an augmentation tool.

Overall, productivity will improve and fast-track implementation of elementary financial tasks. It will also impact almost every role within finance and GRC, and rather than hiding behind fear, should motivate everyone to further develop their methodological skills to keep pace with transformation.

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Industries across the globe are using applications of data mining to gain insights from a huge volume of data and improve the efficiency and accuracy of their businesses.

Data mining is an interdisciplinary field of computer science and statistics that can find patterns in large data sets. Its overall goal is to extract information from data and transform it into an understandable insight for further use, such as for predictive analysis, segmentation, and anomaly detection. And it is a part of almost all predictive analytics techniques. Data mining helps to find patterns from data sets that are used to create predictive models. Predictive algorithms are then applied to these predictive models for making accurate predictions. The ability of data mining to detect patterns and relationships from data can help businesses to make better decisions. These insights can help businesses to increase customer loyalty, unlock hidden profitability, and reduce client churn. These potential benefits have paved the way for several applications of data mining that are adopted by businesses across the globe.

Five Applications of Data Mining Across Industries

Businesses primarily use data mining for providing strong consumer-focused services as they can collect consumer data and gain insights to get a competitive advantage.

Retail

The retail industry is one of the most customer-centric industries as they require their customers to visit them more frequently. And therefore, retailers can benefit the most from data mining. One of the biggest trends in the retail industry is to provide recommendations to customers. But if the recommended products are not related to customers frequently, then it might frustrate customers. And this may end up the relationships between customers and retailers. And that’s where data mining comes into the picture.

Data mining can help retailers to provide the most accurate and appropriate personalized product recommendations to customers. It can classify customers based on their demographics, likes, dislikes, and historical purchases to provide accurate and most relevant personalized product recommendations to customers. And this can also help retailers to promote their most profitable products and earn extra profit.

Data mining can also help to detect changes in customers’ behavior and help to retain them. For instance, based on the frequency of purchases, data mining can determine any changes in the pattern of consumer purchase data. And, if the purchase frequency sees a steady decline, then retailers can put in extra effort and provide promotional offers to retain customers.

Healthcare and Health Insurance

The healthcare industry collects a dazzling amount of data via wearables or health files in the form of electronic Health Records (EHRs). And data mining techniques can help to gain insights from this data to provide optimal treatment and enhanced service to patients. Data mining can compare the treatment results provided by various medicines for a specific disease for different age groups. Thus, data mining can help determine the best standard drug for a disease.

Data mining can also help health insurance agencies in several ways, such as detecting fraud, identifying potential customers, and determining risky customer behavior. It enables health insurance agencies to detect fraud by first establishing normal patterns from previous claims and then comparing them with new claims to find any unusual pattern that can lead to fraud. It can also help them to predict which customers are likely to buy new policies based on their medical data. For instance, if a patient is suffering from a disease, but that disease is not covered in his or her current insurance, then insurance agencies can sell that person a new policy that covers the disease he or she is suffering.

Banking and Finance

The banking and finance sector produces a huge volume of transactional data every day. And based on this data, data mining can determine unusual patterns to detect and predict fraud. Data mining can help detect current trends in the banking and finance sector to provide an opportunity for more business. For instance, it can detect current and predict future demands for certain services and help businesses to prepare in advance for it. Based on the frequency and patterns of transactions, data mining tools can also help to determine the loyalty of customers. And, then businesses can provide custom offers to loyal customers and ensure that they retain them.

One of the most popular data mining use cases in the banking and finance sector is fraud detection. Banks and finance agencies provide loans and credit cards to their customers. But, some customers cheat them and do not return the money. Data mining can detect transactional and behavioral fraud patterns and help them to minimize fraudulent instances. For example, based on historical data, data mining tools can detect transaction patterns of a fraud case. And, they can flag accounts of customers with similar transaction patterns. The banks and finance agencies can then decline all the transactions of those customers until further communications.

Telecommunication

Telecommunication companies are operating in a highly competitive environment. And they can use data generated by their operational systems like ERP and CRM with data mining techniques to get a competitive advantage. Data mining can help telecommunication companies to categorize customers based on their tastes, gender, demographics, and income. And based on this customer segmentation, businesses can offer personalized services to customers from different groups. Businesses can also create different offers for different groups based on their usage and lifestyle.

Based on the call-detail records, data mining can detect unusual behavior in calling patterns to flag fraud on customers’ accounts. Data mining can also help to determine potential customers who can be easily converted to leads. Thus, the sales team can use such insight gained from data mining to increase their sales.

Bioinformatics

Bioinformatics is an interdisciplinary field of developing methods and tools for storing, analyzing, and studying biological information. The bioinformatics includes structural, expressional, cellular, and genomic data. Data mining can find patterns in this biological data to help doctors and researchers study the analysis of biological functioning. And, this can help enhance the speed of research and development in the healthcare industry. Further, data mining can find patterns in protein bonding with a specific drug. For instance, it can determine which drug bonds best with which type of protein molecules. Thus, doctors can provide personalized optimal treatment to patients with the help of data mining.

Organizations are leveraging several applications of data mining. But, data mining faces few challenges like unreliable sources of data, the proliferation of security and privacy concerns, and mining methodology challenges. Businesses need to minimize the limitations of data mining caused due to such challenges to be able to gain better insights and improved accuracy of outputs from data sets.

In today’s world, data is readily available from various sources, but not all of them are reliable. Organizations should gather data from reliable sources so that they can get the desired results. Also, there are several mining methodologies that function differently. Every methodology has its way of controlling and handling the noise in data. And organizations should wisely choose the appropriate methodology based on the diversity of data available with them to benefit in the best possible way. And to overcome such challenges, organizations need a data science team. If organizations can overcome these data mining challenges, then the output accuracy will improve, thereby enhancing the performance of predictive technologies.

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Data Science has quickly become a buzzword and popular career course in the last few years but the ideas that make up this field have been around for almost 3 centuries. According to John Foreman, VP of Product Management at MailChimp “Data Scientists are kind of like the new Renaissance folks, because Data Science is inherently multidisciplinary.” 

The roots of this field can be dated back to Bayes’ Theorem from the 1740s that created the basis for probability calculations which is the very thing that powers AI today. Fast forward to 1954 and the foundations of Statistics explained the scientific objectivity of Data Analysis.

Then in 1977, the International Association for Statistical Computer stated that their mission was to untie Statistics with Computer Technology and Subject Matter Expertise in order to build knowledge. It is no surprise then that when BusinessWeek ran a cover story of “Database Marketing – A Potent New Tool for Selling” in 1994 the beginning of the era of Big Data was brought to fruition. Two years later in 1996 the term “Data Science” had its first appearance when it was used at the International Federation fo Classification Societies in Japan. Since then “Data Science” has become a commonly used term and a growing field.

Today the field of Data Science has grown so large that job listings for “Data Scientist” increased by 15,000% in just one year. This growth is not expected to slow down either, in fact by the end of 2020 there will be over 2.7 million Data Scientist job openings. The roles needed for this job are for Data Engineers, Software Engineers, and AI hardware specialists.

The nature of Data Science allows for just about anyone to become a Data Scientist. Those with skills in Mathematics, Computer Science and who tend to be creative and problem-solving minded will go far. 

Data Scientists use their skills to solve complex problems as they analyze data. Data Engineers specifically create and maintain methods that bring in data. Software Engineers, on the other hand, analyze business data and design software to fit its needs. Lastly, AI hardware specialist create and program AI to perform specific tasks. Data Scientists also take full advantage of tech to help them break down the data. There are plenty of advanced tools that help to further understand and analyze all the data that is being created each day. Some of the common tech tools include Python, PyTorch, Hadoop, and Apache Spark. 

Data Scientists use Math, Data, AI and the scientific method to collect, clean and munge data. Without their skills AI software couldn’t be built, new tech wouldn’t be developed and the world wouldn’t see changes such as energy optimization and crime stopping-AI. Data Science is essential to our world, find out more below. 

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