Machine intelligence is the combination of AI and ML

Serving dishes, controlling traffic, performing surgeries on humans – think of these and the first impression is that you cannot do without humans here. The situation now seems to have undergone a 360 degree transformation. Gone are the days when every task that you can think of needed human intervention. Now, you find machines taking up the role of waiters, traffic controllers, educators and what not. One of the greatest achievement is in the field of healthcare sector. Machines are assisting doctors and surgeons while performing medical procedures. We have reached a stage wherein some not so difficult procedures are done by machines themselves without the involvement of humans.

This machine intelligence has truly transformed the way we look at things. This kind of intelligence has made it easy to address issues and problems in every field like never before. The reason why machines are intelligent to the extent that they hold the potential to perform tasks just like humans is because of Artificial Intelligence. It is only by virtue of Artificial Intelligence that we get to see human-like machines and computers. This area will see a lot more advancements in the near future, without a doubt. With AI, machines are capable of interacting in an intelligent way. Contrary to popular belief, it is not because of the fact that machines are able to perform a couple of tasks like humans that makes them intelligent. The story goes beyond all of this.

An intelligent machine, system, hardware or any computer is not intelligent because it is able to perform human-like tasks. It is solely because such machines stand the potential to complete tasks in an unreliable environment. Unlike what they are being asked to do, machines are intelligent if they can judge what’s going around by being able to monitor the environment and then acting accordingly. Just imagine how a person would react to different situations. Same is the case with machines. If a person is able to make the right use of intelligence, it is then that he / she is said to be intelligent. If the similar criteria is followed in case of machines and they are able to react just like humans by making the best use of their intelligence, then that is what constitutes an intelligent machine.

Probably the best examples of intelligent machines are Alexa and Siri. Not forgetting to mention here, how popular they have become over a period. Also, their demand continues to rise – thanks to AI. It is impossible to imagine machines being intelligent without Artificial Intelligence in place. It is solely because of AI that the machines can come up with improved decisions for the company. They do this by accessing information in the best manner possible.

What constitutes Machine intelligence?

When talking about machine intelligence, there are two concepts that are critical and form the base of the origin – Artificial Intelligence and machine learning. A combination of these two is the reason why machines are proactive. These two allow the machines to not just collect the data but also process it to arrive at conclusions. Basis these conclusions, the organizations make decisions. To make machines work human-like, naturally, some aspects of humans will have to be incorporated. Skills like problem solving, learning ability, prioritization, etc. go in the making of machine intelligence. Needless to say, programming has to be the pre-requisite. Also, machines are designed keeping in mind the concept of “deductive logic”. Using this, they are well aware of when they have made mistakes. Learning from this, the machines ensure that the same mistake isn’t committed againin the future.

Though not many skills go into making machines intelligent, the way they handle the situations and tackle problems does come out to be surprising.

It is because of this that companies are inclined towards machine intelligence. They include a set of automation techniques and develop a model that’d help them achieve their goals. This form of intelligence has eased a lot of issues and hence will continue to rule for the years to come.

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AI has transformed the nature of the eCommerce business in many ways. Artificial Intelligence or machine intelligence replaces human intelligence with machines that possess the same cognitive functions of learning and problem-solving as humans.

The best part about AI is that it can perform cognitive operations with more accuracy and a faster speed than humans. It is the foremost reason for which eCommerce businesses use AI. From improving customer experience to business market research automation, AI brings about a revolution in the eCommerce industry. Consumer insights is another area in which AI is doing wonders. In this article, we will be discussing four ways in which AI is transforming consumer insights.

Why Is Market Research for eCommerce Essential?

Market research has become essential for eCommerce. From ensuring a better consumer experience to gaining quality consumer insights, market research holds great significance in different spheres of eCommerce businesses. It is impossible to gain relevant and genuine consumer insights without proper market research. Moreover, without accurate consumer insights, you cannot improve customer satisfaction as consumer insights play an essential role in understanding your customers.

Market research helps companies gain competitive and actionable consumer insights that can be used for text analytics and sentimental analysis. It is also the best way to know your target audience and the latest trends in the market. Businesses can generate more leads if they know their consumers better. Moreover, you can also know about your competitor’s strategies with market research. It helps you to learn how you can improve your business. Besides, market research also helps in promotion and advertising.

Earlier traditional market research methods such as surveys and polls were the easily accessible options. However, they are time-consuming and expensive. In the age of digital transformation, AI-driven market research has replaced traditional market research to a great extent. It has resulted in tremendous growth of AI text analytics tools and eCommerce marketing platforms.

Below Are Four Ways AI is Transforming Consumer Insights

Market Research: AI-driven market research for eCommerce helps you get actionable and competitive consumer insights faster. There are eCommerce platforms such as Revuze that use AI tools to gain real-time consumer insights that can be used for sentimental analysis. Earlier surveys and polls were the prominent methods of performing market research. However, they are time-consuming and expensive procedures. Moreover, they are not very reliable. However, market research conducted using AI tools is faster and more accurate. AI tools can be used to extract large amounts of relevant data and user-generated content.

Data Cleaning: Data analytics cannot be performed on inadequate or irrelevant data. Insufficient data comprises missing, irrelevant, duplicate, and inconsistent or data with errors. Performing data analytics on bad data leads to inaccurate results and consumer insights. Therefore, it is crucial to clean the extracted data before running data analytics. However, this process is too time-consuming when done manually. Moreover, it can further lead to human errors. Nonetheless, AI is changing the way companies perform data cleaning. AI algorithms substitute insufficient data with useful data to make it fit for analysis.

Understanding Open-ended Questions: Analysing open-ended questions is not an easy task. Open-ended questions can generate quality insights. However, the main challenge is to analyze open-ended feedback and questions. With a human-centric approach, there are chances of errors and biased analysis. Moreover, it’s time taking and tedious. However, interpreting open-ended questions with AI algorithms is free from any biases and errors. Also, it is less time consuming and less costly.

Faster Insights: With AI, gaining quick consumer insights has been made possible. Surveys and polls are time taking market research methods to gather consumer insights. With the traditional market research methods, taking appropriate actions on the insights takes a long time, and it is not suitable for your business. Whereas, with AI, you can collect actionable and competitive insights in no time.

Faster insights mean that you can act and respond to your customer’s needs in less time. As a result, more customers will be attracted to buy from you. It will, in turn, result in increased sales and profit.

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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.

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Simply put, Artificial Intelligence (AI) is the level of intelligence exhibited by machines, in comparison to natural intelligence exhibited by human beings and animals. Therefore it is sometimes referred to as Machine Intelligence. When taught, a machine can effectively perceive its environment and take certain actions to better its chances of achieving set goals successfully. How can a machine be taught?

The root of Machine learning involves writing codes or commands using a programming language that the machine understands. These codes help lay out the foundation of the machines’ thinking faculty, such that the machine is programmed to perform certain functions defined in the codes. These machines are also programmed to use their basic codes to generate a continuous sequence of related codes in order to increase their thinking, learning, and problem-solving capabilities when the workload is increased.

Just as cranes are machines designed to lift heavy loads which humans cannot lift, some machines are programmed to think further and solve analytical problems which are cumbersome to the human brain and some software. This machine assistance for thinking and analysis dates way back to the times of the Abacus. Technology has advanced to the point where there is literally no limit to the amount of information/ data that a machine can work with. This brings us to the topic of Big Data.

Big Data, just as the phrase implies, is simply huge or large or broad or complex or a high amount of a specific set of information which can be understood by, and stored in a computer/ machine. Professionally, Big Data is a field that studies various means of extracting, analysing, or dealing with sets of data that are so complex to be handled by traditional data-processing systems. Such an amount of data requires a system designed to stretch its extraction and analysis capability.

The ideal and most effective means of handling Big Data is with AI Our world is already steeped in Big Data. There is a massive amount of data online and offline about any topic you can think of, ranging from people, their routine, their preferences, etc to non-living things, their properties, their uses, etc.

This huge stockpile of data, when properly harnessed, can give valuable insights and business analytics to the sector/ industry where the data set belongs. Thus, artificially intelligent algorithms are written for us to benefit from large and complex data.

How Companies Are Applying Artificial Intelligence and Big Data

We have addressed the meaning of these terminologies, we will dedicate this part of our Artificial Intelligence essay reviewing how applications are benefiting from the synergy between AI algorithm and Big Data analytics, such as:

● Natural language processing, where millions of samples from the human language are recorded and linked to their corresponding computer programming language translations. Thus, computers are programmed and used in helping organizations analyze and process huge amounts of human language data.

● Helping agricultural organisations and corporations broaden their monitoring capability. AI helps farmers to count and monitor their produce through every growth stage till maturity. AI can identify weak points or defects long before they spread to other areas of these huge acres of land. In this case, satellite systems or drones are used by the AI for viewing and extracting the data.

● Banking and securities, for monitoring financial market activities. For instance, the Securities Exchange Commission (SEC) is using network analytics and natural language processing to foil illegal trading activities in financial markets. Trading data analytics are obtained for high-frequency trading, making decision-based trading, risk analysis, and predictive analysis. They are also used for early fraud warning, card fraud detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc.

● Communication, Media and Entertainment. AI capabilities can be used for collecting, analyzing, and utilizing consumer insights. Leveraging mobile and social media content. Understanding patterns of real-time, media content usage. Companies in this industry can simultaneously analyse their customer data along with customer behavioural data to create detailed customer profiles that will be used for creating content for a diverse target audience, recommending content, and measuring content performance.

● Healthcare providers have benefited from the large pool of health data Prescriptions and health analysis have been simplified by AI. Hospitals are using data collected by millions of cell phones and sensors, allowing doctors to use evidence-based medicine. Also, the spread of chronic diseases is identified and tracked faster.

● In the Educational sector, AI syncs with Big Data analytics for various purposes, such as for tracking and analysing when a student logs into the school’s system, the amount of time spent on the different pages of the system, and the overall progress of students over time. It is also useful for measuring the effectiveness of teachers. Thus, teachers’ performance is analysed and measured with respect to the number of students, various courses, student aspirations, student demographics, behavioural patterns, and many other data.

● In Manufacturing, inventory management, production management, supply chain analysis and customer satisfaction techniques are made seamless. Thus, the quality of products is improved, energy efficiency is ensured, reliability levels rise, and profit margins increase.

● In the Natural Resources sector, the synergy of AI and Big Data makes predictive modelling possible. Allowing for the quick and easy analysis of large graphical data, geospatial data, temporal data, seismic interpretation and reservoir characterization.

● Governments around the world use AI for various applications such as public facial recognition, vehicle recognition for traffic management, population demographics, financial classifications, energy exploration, environmental conservation, infrastructure management, criminal investigations, and much more.

Other areas where AI is used in Big Data are Insurance, Retail & Wholesale Trade, Transportation, and Energy & Utilities.

Final Thoughts

In conclusion, we have been able to confirm that there are huge investments in the use of AI in Big Data analysis for the benefit of all. Data sets will continue to increase, therefore the level of application and investment will continue to increase over time. Human intervention, as always, will continue to be relevant, although this relevance is projected to continue reducing with time.

One can rightly argue that “Artificial Intelligence is useless without data and data is insurmountable without AI ”. Also, both AI and Big Data are literally impossible without human intervention and interaction.

AI system which enables Machine learning solutions is the future of the development of business technologies and processes. Such enabled Machine Learning applications automate data analysis and find new insights that were previously impossible to imagine by processing data manually or with traditional methods. This possibility of increasing the predictability of certain events allows us to completely redraw our approach to how everything is done.

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When it comes to identifying scents, a “neuromorphic” artificial intelligence beats other AI by more than a nose.

The new AI learns to recognize smells more efficiently and reliably than other algorithms. And unlike other AI, this system can keep learning new aromas without forgetting others, researchers report online March 16 in Nature Machine Intelligence. The key to the program’s success is its neuromorphic structure, which resembles the neural circuitry in mammalian brains more than other AI designs.

This kind of algorithm, which excels at detecting faint signals amidst background noise and continually learning on the job, could someday be used for air quality monitoring, toxic waste detection or medical diagnoses.

The new AI is an artificial neural network, composed of many computing elements that mimic nerve cells to process scent information (SN: 5/2/19). The AI “sniffs” by taking in electrical voltage readouts from chemical sensors in a wind tunnel that were exposed to plumes of different scents, such as methane or ammonia. When the AI whiffs a new smell, that triggers a cascade of electrical activity among its nerve cells, or neurons, which the system remembers and can recognize in the future.

Like the olfactory system in the mammal brain, some of the AI’s neurons are designed to react to chemical sensor inputs by emitting differently timed pulses. Other neurons learn to recognize patterns in those blips that make up the odor’s electrical signature.

This brain-inspired setup primes the neuromorphic AI for learning new smells more than a traditional artificial neural network, which starts as a uniform web of identical, blank slate neurons. If a neuromorphic neural network is like a sports team whose players have assigned positions and know the rules of the game, an ordinary neural network is initially like a bunch of random newbies.

As a result, the neuromorphic system is a quicker, nimbler study. Just as a sports team may need to watch a play only once to understand the strategy and implement it in new situations, the neuromorphic AI can sniff a single sample of a new odor to recognize the scent in the future, even amidst other unknown smells.

In contrast, a bunch of beginners may need to watch a play many times to reenact the choreography — and still struggle to adapt it to future game-play scenarios. Likewise, a standard AI has to study a single scent sample many times, and still might not recognize it when the scent is mixed up with other odors.

Thomas Cleland of Cornell University and Nabil Imam of Intel in San Francisco pitted their neuromorphic AI against a traditional neural network in a smell test of 10 odors. To train, the neuromorphic system sniffed a single sample of each odor. The traditional AI underwent hundreds of training trials to learn each odor. During the test, each AI sniffed samples in which a learned smell was only 20 to 80 percent of the overall scent — mimicking real-world conditions where target smells are often intermingled with other aromas. The neuromorphic AI identified the right smell 92 percent of the time. The standard AI achieved 52 percent accuracy. 

Priyadarshini Panda, a neuromorphic engineer at Yale University, is impressed by the neuromorphic AI’s keen sense of smell in muddled samples. The new AI’s one-and-done learning strategy is also more energy-efficient than traditional AI systems, which “tend to be very power hungry,” she says (SN: 9/26/18).

Another perk of the neuromorphic setup is that the AI can keep learning new smells after its original training if new neurons are added to the network, similar to the way that new cells continually form in the brain.

As new neurons are added to the AI, they can become attuned to new scents without disrupting the other neurons. It’s a different story for traditional AI, where the neural connections involved in recognizing a certain odor, or set of odors, are more broadly distributed across the network. Adding a new smell to the mix is liable to disturb those existing connections, so a typical AI struggles to learn new scents without forgetting others — unless it’s retrained from scratch, using both the original and new scent samples.

To demonstrate this, Cleland and Imam trained their neuromorphic AI and a standard AI to specialize in recognizing toluene, which is used to make paints and fingernail polish. Then, the researchers tried to teach the neural networks to recognize acetone, an ingredient of nail polish remover. The neuromorphic AI simply added acetone to its scent-recognition repertoire, but the standard AI couldn’t learn acetone without forgetting the smell of toluene. These kinds of memory lapses are a major limitation of current AI (SN: 5/14/19).

Continual learning seems to work well for the neuromorphic system when there are few scents involved, Panda says. “But what if you make it large-scale?” In the future, researchers could test whether this neuromorphic system can learn a much broader array of scents. But “this is a good start,” she says.

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Humans have misconceived robots. They are often observed as employment thieves, undermining labourers with redundancy. Artificial intelligence just aggravates their apparent danger. Machine intelligence can supplement human intelligence. We, humans, are innovative, creative, vital and strategic. Robots are more qualified for tasks that people dislike and find difficult. The assessment of tremendous amounts of information and search for patterns in that information may include interminable repetition. It would debilitate any human brain, but not robots.

However, AI’s progression can’t be achieved in confinement. The growth of intelligent automation (IA) and its continued development should be perceived alongside AI’s advancement to get a clearer perspective of where we remain as far as technological advancement.

With intelligent automation technologies, businesses can change their procedures, accomplishing higher speed and accuracy, yet in addition, automating anticipations and decisions based on structured and unstructured sources of data. The convergence of modernized AI and Robotic Process Automation (RPA), dramatically elevates the business value and competitive edge for organizations, significantly giving rise to intelligent automation processes.

Amplified Value of RPA Along with AI
As it stands now, RPA and AI have individually established themselves as pronounced and extensive technologies revolutionizing various businesses verticals. However, any technology alone has varied limitations and that is why experts suggest to couple diverse technologies for enhanced prospects. “RPA is a great technology; however, the fact is that no tool today can provide transformation across the breadth of processes. Most organizations today are using patches to piece together a broader solution to meet their needs. As a result, the number of organizations that have scaled up automation are low as compared to the total number of early adopters of automation. RPA needs to be coupled with Artificial Intelligence (AI) capabilities like Intelligent Character Recognition (ICR), Natural Language Processing (NLP), Smart Analytics, and Machine learning to provide broader level of automation. In the near future, we will see more integrated tools that can provide real transformation to organizations,” says Siddhartha Singh, CEO, Quale Infotech Pvt. Ltd.

Businesses that eventually adopt intelligent automation technologies will lead the route in their sphere of work. All things considered, it is substantially ahead of direct process automation. IA innovation has the ability to comprehend procedures that are applicable to a business’s usual way of doing things and can execute themselves in accordance. However, to make the most out of IA, it should be in sync with the defined orchestration architecture, where machine-settled decisions are evaluated by humans to yield better results.

Besides, investors, today, clearly know the potential in intelligent automation. Venture capital investment in companies related to artificial intelligence and robotics has developed more than 70% in each of the last two years, surpassing US$600 million since 2011.

With increased investment and business interest, the rapid development of intelligent automation is introducing an altogether new era of innovation and productivity. As its applications set new norms of quality, effectiveness, speed, and functionality, organizations that efficiently deploy IA might outperform contenders that don’t. If exploited earnestly, the overall impact of intelligent automation across a business could match that of the enterprise resource planning wave of the 1990s.

IA Futurism in Digital Era
“Intelligent automation overcomes the breakneck pace of digital change. It has the power to make things simpler. It can help integrate new products, services, tools, business models, alliances, ecosystems and more. Transformational leaders use intelligent automation to create a new digital world where they are masters of competitive advantage,” says Ashish Sukhadeve, CEO, Analytics Insight.

However, barring the innovation and enthusiasm brought in by modern digital technologies, the unpredictability of the present digital landscape makes comprehensive analysis and processing through manual workflows impracticable. Further, the reliability on manual concentrated procedures makes results rather slow, error-prone, despite the inefficiency associated with it. Intelligent automation exceeds these obstructions and empowers a steady, precise analysis of the method of operations, subsequently revoking the present and future challenges of a human operator.

IA futurism is not a vision or far-off tale; it is rather a workplace where the technology impacts the digital space whose potentials are yet to be fully realised by businesses. The prospective dawn of intelligent automation technology is a new breakthrough, outshining other automation technologies, while transforming the organizational operations into future-ready workstation offering demonstrable profits. IA, undeniably, is something to consider, even as an experiment, in an effort to follow the right allocation of future investments with minimal risk.

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Artificial Intelligence (AI), sometimes called machine intelligence, is Computer systems theory and engineering capable of performing tasks that typically require human intelligence, such as visual processing, speech recognition, decision-making, and language translation.

To further expand this concept of AI in the scope of radiology results in “a computer science unit dealing with the processing, reconstruction, analysis and/or analysis of medical images by simulating intelligent human behavior in computers.”

Radiology, also defined as diagnostic imaging, is a series of different tests that take images of different parts of the body. Radiologists perform a wide array of diagnostic tests, including x-rays, ultrasound, densitometry of bone minerals, fluoroscopy, mammography, nuclear medicine, CT, and MRI.

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace.

Radiologists are medical professionals that are incredibly busy. They can’t really make any blunders. They need to interact with a diverse range of prescribing doctors; the list goes on with gastroenterologists, gynecologists, orthopedic practitioners. They must always be sharp. How can AI bring in and enable such stretched radiologists even better at what they’re doing?

In April 2016, Drs. Tim Dowdell, Joe Barfett, and Errol Colak – all radiologists – created the Machine Intelligence in Medicine Lab (MIMLab) in order to teach computers with artificial intelligence (AI) how to interpret medical images.

“With these AI methods, it’s very unreasonable to think that no one will ever be missing a lung nodule on a chest X-ray in the next five years,” Dr. Barfett says. “AI can span from rare to extremely rare instances like this.”

The three radiologists recruited AI specialist Hojjat Salehinejad, a Ph.D. student at the University of Toronto’s Department of Electrical and Computer Engineering, shortly after founding the MIMLab, who Dr. Barfett said is now the driving force behind their work.

The team found that AI algorithms could not be trained sufficiently to analyze X-rays using hospital databases due to instabilities in the datasets. A new solution was implemented, and the team strengthened its database by programming AI algorithms to create computer-generated chest X-rays rather than relying solely on real medical images. Enough pictures of rare conditions were produced, which, in conjunction with the real ones, gave the team exactly what it needed to teach a computer how to spot conditions on a very wide spectrum – including those rare cases that could mean the difference between a patient’s life and death.

A Stanford research created an algorithm that could detect pneumonia at that particular site in those patients participating with a better average F1 metric (a statistical measure focused on precision and recall) than the radiologists involved in that trial. During its annual meeting, the Radiological Society of North America conference incorporated AI visualization presentations. Many specialists view the advent of AI technology in radiology as a hazard, as the technology in isolated cases, as opposed to specialists, may make improvements in certain statistical metrics.

Benefits

Provide an appropriate treatment. Most AI systems are focused on delivering more info. It can be achieved via the quantifying of information contained in an image, in which it is typically only reported in a qualitative way. Or the system can incorporate universal values, enabling physicians to align patient outcomes with an acceptable boundary-section based on population.

Pick up repetitive routine tasks.AI isn’t good at all. Still not, at least. What are the correct tasks to turn over to AI at such a time? Tasks in which we have access to lots of data that are relatively straightforward and, therefore, do not demand a lot of different inputs to be merged. Therefore, radiologists do a lot of simple routine tasks. It usually includes more repetitive tasks.

Reduce inter- and intra-observer variability. With their diagnosis, even the best trained, most experienced radiologists may sometimes vary. Well rested in the morning, something else that catches your attention than after a long day’s work. In addition, different radiologists in their reports could highlight different aspects. This can be difficult for doctors to respond to, as they need to take these differences into account when synthesizing all the details they have before the final diagnosis is made. AI software can reduce or even remove this heterogeneity between reports of radiologists

AI Realizing the benefits

In the sense of radiology, there are many tasks that AI can do. Many tasks will only require a medical image as input and will depend on pixels (or voxels) to construct the

analysis. This can be done manually, but this is perceived by many radiologists as a tedious job, rendering it an appropriate candidate for some AI assistance. Some will go one move ahead, combining radiological images with other sources of information. The integration of medical images with other knowledge will lead to insights that radiologists do not always consider easy to obtain. Usually, these types of analyzes are regarded as more modern. For example, it is possible to allow an algorithm to extract pathology information from a medical image by linking image data to pathology laboratory results.

Could AI take over the work of radiologists?

The obvious answer, NO, radiologist jobs won’t be taken over. It will certainly take over certain radiologist tasks though By performing automated assessments that are currently very time consuming, it will assist radiologists. It will pick up repetitive tasks that many radiologists encounter as burdensome. However, radiologists have a much more differentiated job than just these kinds of tasks. Radiologist jobs are going to change, but they won’t go away.

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When it comes to keeping up with emerging cybersecurity trends, the process of staying on top of any recent developments can get quite tedious since there’s a lot of news to keep up with. These days, however, the situation has changed dramatically, since the cybersecurity realms seem to be revolving around two words- deep learning.

Although we were initially taken aback by the massive coverage that deep learning was receiving, it quickly became apparent that the buzz generated by deep learning was well-earned. In a fashion similar to the human brain, deep learning enables an AI model to achieve highly accurate results, by performing tasks directly from the text, images, and audio cues.

Up till this point, it was widely believed that deep learning relies on a huge set of data, quite similar to the magnitude of data housed by Silicon Valley giants Google and Facebook to meet the aim of solving the most complicated problems within an organization. Contrary to popular belief, however, enterprises can harness the power of deep learning, even with access to a limited data pool.

In an attempt to aid our readers with the necessary knowledge to equip their organization with deep learning, we’ve compiled an article that dives deep (no pun intended) into some of the ways in which enterprises can utilize the benefits of deep learning in spite of having access to limited, or ‘small’ data.

But before we can get into the meat of the article, we’d like to make a small, but highly essential suggestion- start simple. However, before you start formulating neural networks complex enough to feature in a sci-fi movie, start by experimenting with a few simple and conventional models, (e.g. random forest) to get the hang of the software.

With that out of the way, let’s get straight into some of the ways in which enterprises can amalgamate the deep learning technology while having access to limited data.

#1- Fine-lining the baseline model:

As we’ve already mentioned above, the first step that enterprises need to take after they’ve formulated a simple baseline deep learning model is to fine-tune them for the particular problem at hand.

However, fine-tuning a baseline model sounds much difficult on paper, then it actually is. The fundamental idea behind fine-tuning a large data set to cater to the specific needs of an enterprise is simple- you take a large data, that bears some resemblance to the domain you function in, and then fine-tune the details of the original data set, with your limited data.

As far as obtaining the large data set is concerned, enterprise owners can rely on ImageNet, which subsequently also provides an easy to fix to any problems of image classification as well. The dataset hosted by ImageNet allows organizations access to millions of images, which are divided across multiple classes of images, which can be useful to enterprises hailing from a wide variety of domains, including, but certainly not limited to images of animals, etc.

If the process of fine-tuning a pre-trained model to suit the specific needs of your organization still seems like too much work for you, we’d recommend getting help from the internet, since a simple Google search will provide you with hundreds of tutorials on how to fine-tune a dataset.

#2- Collect more data:

Although the second point on our list might seem redundant to some of our more cynical readers, the fact of the matter remains- when it comes to deep learning, the larger your data set is, the more likely you are to achieve more accurate results.

Although the very essence of this article lies in providing enterprises with a limited data set, we’ve often had the displeasure of encountering too many “higher-ups,” who treat investing in the collection of data equivalent to committing a cardinal sin.

It is all too often that businesses tend to overlook the benefits offered by deep learning, simply because they are reluctant to invest time and effort in the gathering of data. If your enterprise is unsure about the amount of data that needs to be collected, we’d suggest to plot learning curves, as the additional data is integrated into the model, and observe the change in model performance.

Contrary to the popular belief held by most CSO’s and CISO’s, sometimes the best way to solve problems is through the collection of more relevant, data. The role of CSO and CISO is extremely important in this case because there is always a threat of cyber-attacks. It is found that in 2019, the total global spending on cybersecurity takes up to $103.1 billion, and the number continues to rise. To put this into perspective, let’s consider a simple example- imagine that you were trying to classify rare diamonds, but have access to a very limited data set. As the most obvious solution to the problem dictates, instead of having a field day with the baseline model, just collect more data!

#3- Data Augmentation:

Although the first two points we’ve discussed above are both highly efficient in providing an easy solution to most problems surrounding the implementation of deep learning into enterprises with a small data set, they rely heavily on a certain level of luck to get the job done.

If you’re unable to have any success with fine-tuning a pre-existing data set either, we’d recommend trying data augmentation. The way that data augmentation is simple. Through the process of data augmentation, the input data set is altered, or augmented, in such a way that it gives a new output, without actually changing the label value.

To put the idea of data augmentation into perspective for our readers, let’s consider a picture of a dog. When rotated, the viewer of the image will still be able to tell that it’s an image of a dog. This is exactly what good data augmentation hopes to achieve, as compared to a rotated image of a road, which changes the angle of elevation and leaves plenty of space for the deep learning algorithm to come to an incorrect conclusion, and defeats the purpose of implementing deep-learning in the first place.

When it comes to solving problems related to image classification, data augmentation serves as a key player in the field and hosts a variety of data augmentation techniques that help the deep learning model to gain an in-depth understanding of the different classifications of images.

Moreover, when it comes to augmenting data- the possibilities are virtually endless. Enterprises can implement data augmentation in a variety of ways, which include NLP, and experimentation of GANs, which enables the algorithm to generate new data.

#4- Implementing an ensemble effect:

The technology behind deep learning dictates that the network is built upon multiple layers. However, contrary to popular belief maintained by many, rather than viewing each layer as an “ever-increasing” hierarchy of features, the final layer serves the purpose of offering an ensemble mechanism.

The belief that enterprises with access to a limited, or smaller data set should opt to build their networks deep was also shared in a NIPs paper, which mirrored the belief we’ve expressed above. Enterprises with small data can easily manipulate the ensemble effect to their advantage, simply by building their deep learning networks deep, through fine-tuning or some other alternative.

#5- Incorporating autoencoders:

Although the fifth point we’ve taken into consideration for has received only a relative level of success- we’re still on board with the use of autoencoders in order to pre-train a network and initialize the network properly.

One of the biggest reasons apart from cyber-attacks as to why enterprises fail to get over the initial hurdles of integrating deep learning is because of bad initialization, and it’s many pitfalls. Unsupervised pre-training often leads to poor, or incorrect execution of the deep learning technology, which is where autoencoders can shine.

The fundamental notion behind a neural network dictates the creation of a neural network that predicts the nature of the dataset being input. If you are unsure of how to use an autoencoder, there are several tutorials online that give clear cut instructions.

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Like that emotional kid David, played by Haley Joel Osment, in the movie A.I. Or WALL•E, who obviously had feelings for EVE-uh. Robby the Robot sounded pretty emotional whenever warning Will Robinson of danger. Not to mention all those emotional train-wreck, wackadoodle robots on Westworld.

But in real life robots have no more feelings than a rock submerged in novocaine.

There might be a way, though, to give robots feelings, say neuroscientists Kingson Man and Antonio Damasio. Simply build the robot with the ability to sense peril to its own existence. It would then have to develop feelings to guide the behaviors needed to ensure its own survival.

“Today’s robots lack feelings,” Man and Damasio write in a new paper (subscription required) in Nature Machine Intelligence. “They are not designed to represent the internal state of their operations in a way that would permit them to experience that state in a mental space.”

So Man and Damasio propose a strategy for imbuing machines (such as robots or humanlike androids) with the “artificial equivalent of feeling.” At its core, this proposal calls for machines designed to observe the biological principle of homeostasis. That’s the idea that life must regulate itself to remain within a narrow range of suitable conditions — like keeping temperature and chemical balances within the limits of viability. An intelligent machine’s awareness of analogous features of its internal state would amount to the robotic version of feelings.

Such feelings would not only motivate self-preserving behavior, Man and Damasio believe, but also inspire artificial intelligence to more closely emulate the real thing.

Typical “intelligent” machines are designed to perform a specific task, like diagnosing diseases, driving a car, playing Go or winning at Jeopardy! But intelligence in one arena isn’t the same as the more general humanlike intelligence that can be deployed to cope with all sorts of situations, even those never before encountered. Researchers have long sought the secret recipe for making robots smart in a more general way.

In Man and Damasio’s view, feelings are the missing ingredient.

Feelings arise from the need to survive. When humans maintain a robot in a viable state (wires all connected, right amount of electric current, comfy temperature), the robot has no need to worry about its own self-preservation. So it has no need for feelings — signals that something is in need of repair.

Feelings motivate living things to seek optimum states for survival, helping to ensure that behaviors maintain the necessary homeostatic balance. An intelligent machine with a sense of its own vulnerability should similarly act in a way that would minimize threats to its existence.

To perceive such threats, though, a robot must be designed to understand its own internal state.

Man and Damasio, of the University of Southern California, say the prospects for building machines with feelings have been enhanced by recent developments in two key research fields: soft robotics and deep learning. Progress in soft robotics could provide the raw materials for machines with feelings. Deep learning methods could enable the sophisticated computation needed to translate those feelings into existence-sustaining behaviors.

Deep learning is a modern descendant of the old idea of artificial neural networks — sets of connected computing elements that mimic the nerve cells at work in a living brain. Inputs into the neural network modify the strengths of the links between the artificial neurons, enabling the network to detect patterns in the inputs.

Deep learning requires multiple neural network layers. Patterns in one layer exposed to external input are passed on to the next layer and then on to the next, enabling the machine to discern patterns in the patterns. Deep learning can classify those patterns into categories, identifying objects (like cats) or determining whether a CT scan reveals signs of cancer or some other malady.

An intelligent robot, of course, would need to identify lots of features in its environment, while also keeping track of its own internal condition. By representing environmental states computationally, a deep learning machine could merge different inputs into a coherent assessment of its situation. Such a smart machine, Man and Damasio note, could “bridge across sensory modalities” — learning, for instance, how lip movements (visual modality) correspond to vocal sounds (auditory modality).

Similarly, that robot could relate external situations to its internal conditions — its feelings, if it had any. Linking external and internal conditions “provides a crucial piece of the puzzle of how to intertwine a system’s internal homeostatic states with its external perceptions and behavior,” Man and Damasio note.

Ability to sense internal states wouldn’t matter much, though, unless the viability of those states is vulnerable to assaults from the environment. Robots made of metal do not worry about mosquito bites, paper cuts or indigestion. But if made from proper soft materials embedded with electronic sensors, a robot could detect such dangers — say, a cut through its “skin” threatening its innards — and engage a program to repair the injury.

A robot capable of perceiving existential risks might learn to devise novel methods for its protection, instead of relying on preprogrammed solutions.

“Rather than having to hard-code a robot for every eventuality or equip it with a limited set of behavioral policies, a robot concerned with its own survival might creatively solve the challenges that it encounters,” Man and Damasio suspect. “Basic goals and values would be organically discovered, rather than being extrinsically designed.”

Devising novel self-protection capabilities might also lead to enhanced thinking skills. Man and Damasio believe advanced human thought may have developed in that way: Maintaining viable internal states (homeostasis) required the evolution of better brain power. “We regard high-level cognition as an outgrowth of resources that originated to solve the ancient biological problem of homeostasis,” Man and Damasio write.

Protecting its own existence might therefore be just the motivation a robot needs to eventually emulate human general intelligence. That motivation is reminiscent of Isaac Asimov’s famous laws of robotics: Robots must protect humans, robots must obey humans, robots must protect themselves. In Asimov’s fiction, self-protection was subordinate to the first two laws. In real-life future robots, then, some precautions might be needed to protect people from self-protecting robots.

“Stories about robots often end poorly for their human creators,” Man and Damasio acknowledge. But would a supersmart robot (with feelings) really pose Terminator-type dangers? “We suggest not,” they say, “provided, for example, that in addition to having access to its own feelings, it would be able to know about the feelings of others — that is, if it would be endowed with empathy.”

And so Man and Damasio suggest their own rules for robots: 1. Feel good. 2. Feel empathy.

“Assuming a robot already capable of genuine feeling, an obligatory link between its feelings and those of others would result in its ethical and sociable behavior,” the neuroscientists contend.

That might just seem a bit optimistic. But if it’s possible, maybe there’s hope for a better future. If scientists do succeed in instilling empathy in robots, maybe that would suggest a way for doing it in humans, too.

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In the hands of enthusiastic marketing departments, the terms “artificial intelligence,” “machine learning,” and “deep learning” have become fuzzy in definition, sacrificing clarity to the need for increasing sales. It’s entirely possible that you’ll run into a product or service that carries one (or several) of these labels while carrying few of its attributes. 

Talk of machine intelligence can often lead to its own special rabbit-hole of jargon and specialized concepts. Which of these will form an important part of your future security infrastructure — and does the difference really matter?

Three Branches
Broadly speaking, machine “intelligence” is a system that takes in data, produces results, and gets better — faster, more accurate, or both — as more data is encountered. Within the broad category are three labels frequently applied to systems: machine learning, deep learning, and artificial intelligence. Each has its own way of dealing with data and providing results to humans and systems.

The differences between how the three function make them appropriate for different tasks. And the sharpest difference divides AI from the other two. Put simply, AI can surprise you with its conclusions, while the other two can “only” surprise you with their speed and accuracy.

Machine Learning
Machine learning uses statistical models (often marketed as “heuristics”) rather than rigid algorithmic programming to reach results. Looked at from a slightly different perspective, machine learning can use an expanding universe of inputs to achieve a specific set of results.

There are many techniques that fit within the category of machine learning. There are supervised and unsupervised learning, anomaly detection, and association rules. In each of these, the machine can learn from each new input to make the model on which it bases its actions richer, more comprehensive, and more accurate.

With all of these, the key is “a specific set of results.” For example, if you wanted a machine learning system to differentiate between cats and dogs, you could teach it all kinds of parameters that go into defining cats and dogs. The system would get better at its job given more data to build its models, and ultimately could predict — based on an ear or a tail — whether something was a dog or cat. But if you showed it a goose, it would tell you it was a cat or dog because those are the only options for results.

When the goal is sorting diverse input into specific categories, or directing specific actions to be taken as part of an automation process, machine learning is the most appropriate technology.

Deep Learning
Deep learning stays within the realm of machine learning, but in a very specific way. “Deep learning” implies that neural networks are the family of techniques being used for processing. While neural networks have been around for quite a while, developments in the last decade have made the technique more accessible to application developers.

In general, neural networks today used a layered technique to pass input through multiple layers of processing. This is one of the ways in which the neural network is designed to mimic animal intelligence. And that mimicry makes neep learning applicable to a wide range of applications.

Deep learning is frequently the technology behind speech recognition and image recognition applications outside of security. Within security, deep learning is often seen in malware and threat detection systems. The number of connections between nodes in the neural network (which can range up into the hundreds of millions) make deep learning a technique often used in applications where most of the learning and processing happens in a central, cloud-based system, with the application of that learning performed at the network’s edge.

To use our earlier examples, deep learning would also be able to learn how to tell cats from dogs, and could be trained to tell breeds of dogs apart, as well as breeds of cats. It could even get to the point of being shown mutts (or “All-American Dogs” as the American Kennel Club dubs them) and assigning them a likely breed based on physical characteristics. But it would still be separating cats and dogs — the poor goose would still be left out in the cold.

Artificial Intelligence
Both machine learning and deep learning involve systems that take an expanding set of data and return results within a specific set of parameters. This makes them technologies that can readily be incorporated into automation systems. Artificial intelligence, on the other hand, is capable of reaching conclusions that are outside defined parameters. It can surprise you with the results it finds.

If you ask many academic AI researchers, they will say that there is no “real” AI on the market today. By this, they mean there’s no general AI — nothing that remotely resembles HAL from “2001” or the Majel Barrett-voiced computer in Star Trek.

There are, however, AI systems that apply advanced intelligence to specific problems. IBM’s Watson is the most widely known, but there are many application-specific AI engines in use by various vendors. Much of the concern about “deep fake” audio and video is fed by AI capabilities used in different applications and services. Robotics, including autonomous vehicles, are another.

To complete our example, an AI system would be able to take all the model information built in deep learning and extend it. Given a bit more information, it might be able to tell that a new image showed a mammal or some other type of animal — and if presented the photo of a fire-hydrant could tell the human operator that this was a novel “animal” never seen before and deserving of more study. AI can go beyond narrow categories of results.

Within cybersecurity, AI is being used to help analysts sort through and classify the vast array of input data coming into the security operations center (SOC) every day. The important note is that, today, the possibility for an unexpected result means that AI is used to assist or augment human analysts rather than merely drive security automation.

Not Quite Skynet
With each of these types of machine intelligence, operators have to be aware of the possibility of two huge issues, one driven by internal forces and the other driven from external agents. The internal issue is called “model bias” — the possibility that the data used for learning in the system’s model of its world will push it in a particular direction for analysis, rather than allowing the system to simply reach the mathematically correct answers.

The external problem comes through “model poisoning,” in which an external agent makes sure the model will deliver inaccurate results. The poisoning can provide results that are embarrassing — or devastating, depending on the application, and the IT or security staff has to be aware of the possibility.

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