Cybersecurity and AI

The advancements in computing power and the evolution of paradigms like distributed computing, big data, and cloud computing have brought about an AI revolution. The number of devices that are being connected to the internet is increasing day by day. This also means that the industry is facing more number of cyber-attacks. At the same time, there’s a massive shortage of skilled cyber workers.

With the advancements in AI, many companies have started to use it as a powerful tool against cyber-attacks and cyber-trespassers. AI allows you to automate the detection of threats and combat without the involvement of humans. This can ease the burden on employees, and potentially help identify threats more efficiently than other software-driven approaches. The most primitive form of cyber-attack is spam. Today, machine learning is successfully being used to tackle spam. Google claims that it has a 99 percent accuracy rate in blocking spam. Automatically detecting, analyzing, and defending against attacks enables data deception technology to detect and trick attackers. AI is a machine language-driven, which provides complete error-free cybersecurity services.

That being said, experts say that it is a bit too early for machine learning to be accurate enough in malware detection and prevention. Journalist Marc Ambasna Jones writes that “newer ideas like behavioral analysis and sandboxing powered by ML should be employed in combination with tried and tested techniques, such as firewalls, intrusion detection and prevention, and web and email gateways.” However, companies have also started to put more resources than ever for boosting AI driven technologies.

AI as part of authentication systems

AI is being used for the detection of physical characteristics like fingerprints, retina scans, etc. Thus, biometric logins are much more secure than the password enabled ones. Password protection and authenticity detection systems are vulnerable to attacks and hacks, making the biometric login the better option.

What makes AI vulnerable?

While AI provides solutions to many problems, it can also open up pathways for attacks, especially when it depends on interfaces within and across organizations that inadvertently create opportunities for access by disreputable agents. Nothing stops cybercriminals from using technologies like Machine learning to automatically tailor phishing messages and manipulating data. Deploying AI by the attackers gives them an edge as well, which would enable them to develop automated hacks that can study and learn about the systems they target and identify vulnerabilities.

While AI empowers cyber-security systems, it can also give power to the wrong people. Many believe that AI can truly be a boon to the industry only if it remains in the hands of the right people. Enterprises are faced with challenges of using AI for their profits while balancing the risk of cyber exposure.

Rise of Conversational AI – Going Beyond Simple Chatbots

Up until two years ago, chatbots were hailed as the next big trend. Thousands of chatbots flocked the market as they are relatively easy to build and could be controlled by a predefined flow. However, this trend began to wane when simple chatbots could not meet complex customer demands. It could only work for completing simpler commands like ordering or booking something. Thus, a need for complex chatbots and conversational AI emerged.

Conversational AI was sought out in order to deal with conversations that require a level of comprehension and cognition that goes far beyond the predefined flow of today’s chatbots. Conversational AI is a form of Artificial Intelligence that allows people to communicate with applications, websites, and devices in every day, humanlike natural language via voice, text, touch, or gesture input. Ram Menon, CEO of Awaamo, writes that “these platforms offer more than a natural language interface (NLI): they demonstrate true advancements in combining a variety of emerging technologies — everything from speech synthesis to natural language understanding (NLU) to cognitive and machine learning technologies — and are capable of replacing humans in a variety of tasks.”

Understanding the customer is the key. Using advanced Conversational AI platforms such as Teneo can result not only in an increase in customer satisfaction but in the actionable data that conversational interfaces generate. Such conversational AI-driven chatbots can understand the context and the sentiment behind the conversation. When conversational AI solutions integrate with back-end data and third-party databases, a deeper personalization can take place. It also needs to be capable of creating a detailed analysis of the chat logs in real-time to feedback into the conversation, improve and maintain the system, and deliver actionable insights to the business.

The benefits of using Intelligent Conversational Interface

Intelligent conversational interfaces are the simplest way for businesses to interact with devices, services, customers, suppliers, and employees everywhere. There are lots of companies that provide AI-driven conversational platforms specifically focused on high impact use cases, including IBM’s Watson and KAI.

Analysts predict the rapid and sustained growth of Virtual Digital Assistants in the coming years. This growth underlines the strongly defined benefits that both consumers and enterprises see in conversational AI. The future of chatbots will be dominated by AI-driven conversational tools.

Giving the customers the best experience and analyzing the data garnered can increase the profits of the company.  Businesses can also cut down on costs by using AI-driven chatbots for automating many tasks such as customer service. As cost benefits continue to pile up, the trend will accelerate in 2018.

Artificial Intelligence and the Fourth Industrial Revolution

There are many factors that spike up the production costs of a company. In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation especially in this fourth industrial revolution phase. Manufacturing companies are finding it increasingly harder to maintain high levels of quality during the industrial revolution. Bringing out the best product takes time as well as large human resources. But all that is set to change.

Introducing – The Fourth Industrial Revolution

The First Industrial Revolution used water and steam power to mechanize production. The Second Industrial Revolution used electric power to create mass production. The Third Industrial Revolution used electronics and information technology to automate production. Now the Fourth Industrial Revolution is building on the Third and has had a massive impact on the manufacturing sector. The fourth industrial revolution, powered by technology is remolding the industrial sector, helping businesses achieve more profits and more efficiency. The sector is entering its next phase – Industry 4.0 – which is driven by automation, AI, and Internet of things, and cloud computing. The big players are already investing millions in computer intelligence so that they can save time, money, and resources while maximizing their production. The Manufacturer’s Annual Manufacturing Report 2018 found that 92% of senior manufacturing executives believe that ‘Smart Factory’ digital technologies – including Artificial Intelligence – will enable them to increase their productivity levels and empower staff to work smarter.

How is the Manufacturing Sector using Artificial Intelligence?

Through computer vision, machines can be powered to pay attention to the tiniest of details, far beyond a man’s potential. Landing.ai, a startup formed by Silicon Valley veteran Andrew Ng, has developed machine-vision tools to find microscopic defects in products such as circuit boards, using a machine-learning algorithm trained on remarkably small volumes of sample images. If it spots a problem or defect, it sends an immediate alert, an artificial intelligence process known as “automated issue identification.”

Artificial intelligence can also be used to monitor the whole process of manufacturing. Siemens, one of the leading manufacturing companies on the planet, did just that. They embarked on a digitalization strategy of which one of the major goals was Overall Equipment Efficiency. In late 2017, the company announced the latest version of its IoT operating system, MindSphere. Physical machines can be connected to Mindsphere cloud environment, enabling it to build the application that visualizes the various metrics that plant managers need to monitor in 2018. It also gives the resources needed to build an industrial Internet of Things system in a fraction of the time it would take to set up a physical environment.

General Electrics is yet another leader in the manufacturing sector that has adopted the artificial intelligence strategy. In 2015 GE launched its Brilliant Manufacturing Suite for customers, which it had been field testing in its own factories. The system takes a holistic approach of tracking and processing everything in the manufacturing process to find possible issues before they emerge and to detect inefficiencies. GE claims it improved equipment effectiveness at this facility by 18 percent.

It is powered by Predix, their industrial internet of things platform. In the manufacturing space, Predix can use sensors to automatically capture every step of the process and monitor each piece of complex equipment. You can view a short video of how its done here.

Another application of artificial intelligence is the use of generative design. Designers or engineers input design goals into generative design software, along with parameters such as materials, manufacturing methods, and cost constraints. Software explores all the possible permutations of a solution, quickly generating design alternatives. It tests and learns from each iteration what works and what doesn’t. From the many solutions that are put forward, the designers or engineers filter and select the outcomes that best meet their needs. This can lead to major reductions in cost, development time, material consumption, and product weight. Airplane manufacturer Airbus used generative design to reimagine an interior partition for its A320 aircraft and came up with an intricate design that ultimately shaved off 45 percent (30kg) of the weight off the part.

Such applications will affect the future of work. These will bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed. It all comes down to optimal manufacturing performance. If manufacturers do not invest in the long term, ignore the fusion of technologies then their profits would be affected, as prices of products as well as the raw materials would only go up. However, it is not late yet as the field of artificial intelligence is constantly evolving and these applications will be the future of artificial intelligence.

Data Science and how Netflix does it!

Companies of this age invest heavily in data to stay ahead of the competition. Especially the use of data analytics on OTT platform data like those collected from platforms like Amazon’s Prime Video, Hotstar, and Hulu has shown a staggering increase in recent years. This is because of the increasing trend of online streaming entertainment series. Data analysts and data science experts are hired to analyze the vast expanse of data available, an understanding of which will render them capable of making informed and educated choices, optimizing marketing strategies and putting off projects that have a lower rate of success. Once the initial goal of securing customers is achieved, the next step for a successful company is to keep the customers entranced with their work, thereby building the company on their loyalty. For example, recommendation services that are high quality and geared towards recommending new products to customers have the potential to dramatically improve sales per customer and price point per order. Data collected from the customer base can be analyzed to give the customers what they need. Some potential issues can also be solved in the same manner, before they even materialize. Optimizing product locations is another way to provide customer services in a personalized manner.

One of the best examples for analysis of how effective use of big data can result in success is that of Netflix which provides quality content and customer personalization and is presently valued at over 164$ billion, having overtaken Disney’s place as the world’s most valuable media company. Let us dive deep into understanding how Netflix uses data!

Some guidelines to optimize the company’s profits by using data include:

  1. Setting clear goals

Laying out well-defined goals and expectations of the company in order to make the action plan is important as this will give data scientists the opportunity to adopt the right methodology. Netflix is an online streaming channel, with over 130 million users. Their success rates depend on their customer satisfaction, on whether the subscribers like what they are watching. Here Netflix uses predictive analytics to put forth options that the user will find favorable, as it is based on the data collected from the user’s experience. When the channel succeeds to strike a chord with a viewer, then the credibility increases.

  1. Identifying available data

The available data has to be analyzed brought to the right format so that it can be used to bring about the goals of the company. With a large number of subscribers come tremendous amounts of big data on online streaming that Netflix can use. During the onset of the subscription, the customer has to give information about his/her interest in specific genres. They are also asked to rate the movies which they have already seen. Such information is used by Netflix to help them to discover new movies and T.V shows, something that is integral to its success. Data is also collected from events like the customer’s searches, the date the show was watched, the device on which it was watched, when the program was paused, when the program is re-watched, etc. The vast data collected from such events helps Netflix to understand their subscriber’s choices and preferences. This will in turn be used for the user to provide the customer personalization.

3. Adopting the right methodology and being data-driven.

With an in-depth understanding of the data at hand, the right tool has to be chosen so as to use the information most effectively. Netflix uses algorithms for predicting the user’s choice based on his previous ratings. It is said that the recommendation system used by Netflix influences 80% of the content the subscribers watch on Netflix. Recommendation systems are simple algorithms that aim to provide the most relevant and accurate items to the user by filtering useful stuff from a huge pool of information base. Content-based systems recommend items based on a similarity comparison between the content of the items and a user’s profile.

Collaborative Filtering algorithm considers “User Behaviour” for recommending items. Other users’ behavior and preferences over the items are used to recommend items to the new users.

A personalized video ranker orders the entire Netflix collection for each member profile in a personalized way, keeping in mind their interests, habits, and choices. Jenny McCabe, Director of Global Media Relations says “We always use our in-depth knowledge (aka analytics and data) about what our members love to watch to decide what’s available on Netflix….If you keep watching, we’ll keep adding more of what you love.”

An action plan based on the available data would enable the company to make the right decisions. In 2011 Netflix outbid top television channels like HBO and AMC to earn rights for a U.S version of ‘House of Cards.’ At a cost of $4 million to $6 million an episode, this 2-season series cost over $100 million. Such a big decision was made on the data that they already had.

Using methods like clustering analysis, sets with similar attributes are studied. A lot of users watched the David Fincher directed movie The Social Network from beginning to end. The British version of “House of Cards” has also been well watched. Those who watched the British version of “House of Cards” also watched Kevin Spacey films and/or films directed by David Fincher. Such factors gave them the confidence to make the $100 million investment, which turned profitable in the end. Here, using association mining of customer information with similar behavior is targeted to make a decision that satisfies that particular set.

  1. Testing regularly

Data-driven models should be constantly checked as demographics have a way of changing gradually. The algorithms that Netflix uses are constantly revised in order to achieve maximum optimization. Bill Franks, Chief Analytics Officer, International Institute for Analytics says that “ I can say that no changes in Netflix products are not tested and validated and we do not just test to test. If we do not believe it will not improve, it will not be tested. We have 300 major tests of products and dozens of variations within”

Data science practices should be implemented wisely. Netflix has successfully shown us that machine learning can be used to convert the user’s cravings into the company’s business goal. Quantitative data is always a good basis on which better and cost-effective decisions can be taken. Data can predict whether certain innovations or experimental projects can take off.

While discussing analytics, Netflix co-founder Mitch Lowe says “He [Reed Hastings] taught me how to use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing that he taught me was [that] you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction. ”

Intelligent use of data can reap benefits, but it should be done responsibly. Data should be protected from others and used carefully. Data science should be seen as a solution to solving problems as well as a way to greater rewards; it should be given due importance.

Applications of Artificial Intelligence (AI) in Healthcare

The ever-evolving technology powered by artificial intelligence is transforming many industries. The healthcare industry is no exception. AI in healthcare has become very prominent. Artificial Intelligence (AI) and machine learning technologies are increasingly being depended upon to keep up with the influx of constant new information about health conditions, treatments and medical technology. Today, machine learning algorithms and predictive analytics are being used to reduce drug discovery time, provide virtual assistance to patients and diagnose ailments by processing medical images. Let us dive deep into understanding what AI is in healthcare by learning about its past, present and future.

From the past to the present

The Information age has brought with it an influx of technology that aims to make healthcare cheaper. Opposed to earlier where manual labor and doctors were heavily relied upon, artificial intelligence in the present can help scan thousands of images and identify patterns at a fraction of time and machine learning can help improve sensitivity and accuracy over time. These developments in AI in the healthcare sector are much cheaper than what it has been in the past.

For example, AI can bring down the cost of a cancer screening test, by reducing the time to perform the operation and by bringing down the doctor’s fee, since highly skilled endoscopists may no longer be required to perform screening tests. Medical VR (virtual reality therapy) has been evidenced to stop the brain from processing pain and reduce pain in hospitalized patients. This, in turns, shortens the length of the patient’s stay in the hospital, which also lowers the costs of care.

Key Applications of AI in Healthcare

Virtual Reality

Although VR indeed set sails to enhance the demanding gamer’s experience, it has also made significant improvements to the lives of people with autism, lazy eyes, chronic pain, and other health conditions. Startups like Floreo use virtual reality to help make the delivery of therapy simplified so parents can support their offspring from home. Their product uses mobile VR to instigate social interactions with autistic kids by spurring virtual characters in a scene. It can also be used in a manner that influences the brain to reduce chronic pain. A faster recovery time can be clocked using innovative technologies. Mindmaze is a Swiss app that allows patients to practice how to move their fingers or lift their arms in a fun fashion with the help of VR. Although patients do not carry out the actual movement, their engagement, motivation, and attention is notably improved with audio-visual feedback, which could speed the recovery of traumatized nervous systems.

Computer Vision and Robotics

One of the major applications of AI in healthcare is the usage of computer vision techniques and robotics. Medical imaging is the biggest and most established area of computer vision and is used by computer-aided diagnostics for personalized therapy planning, care assistance, and better decision-making.

Robotic surgery has been making waves in the industry and is being hailed for being ‘minimally intrusive’, thereby allowing the patients to heal faster from smaller incisions. They also analyze data to guide the surgeon. One popular example is the da Vinci Surgical System features a magnified 3D high-definition vision system and tiny wristed instruments that bend and rotate far greater than the human hand. As a result, da Vinci enables the surgeon to operate with enhanced vision, precision, and control.

Among other robots, the HeartLander is a miniature mobile robot that can enter the chest through an incision below the sternum. It reduces the damage required to access the heart and allows the use of a single device for performing stable and localized sensing, mapping, and treatment over the entire surface of the heart.

Virtual Assistants

Virtual nurses are high on demand as they offer many benefits including round the clock availability and quick answers. They offer regular communication between the patients and the care providers. Care Angel’s voice powered virtual nurse assistant provides wellness checks through AI. Another digital nurse is Molly, created by the startup Sensely, which monitors a patient’s condition and follows up with treatments, between doctor visits.

Administrative Automation

AI can help in compiling, managing and analyzing medical records and other data. Automated Administrative tasks can thus save money and time. Robots collect, store, re-format, and trace data to provide faster, more consistent access. Mundane tasks analyzing tests, X-Rays, CT scans can me made faster and more accurate. The data collected and stored can accessed consistently. Technology such as voice-to-text transcriptions could help order tests, prescribe medications and write chart notes. IBM’s cloud based intelligence Watson, mines big data and helps physicians provide a personalized and more efficient treatment experience. It is also among the pioneers of the field.

Doubts still remain

Even though there have been breakthroughs in the applications of artificial intelligence in healthcare, people still harbor fears of mismanaged care due to a mechanical error. Numerous problems exist with the use of AI in healthcare. The lack of human insight and data privacy issues are other concerns that the industry has to deal with. While technology can support highly trained medical professionals, the chances of it taking over the industry completely remain very low.

The future

In a few years, the market for AI-powered healthcare technologies will exceed 6 billion dollars. Demand for electronic, data-driven, and virtual-based care is the driving force, especially because they offer more convenient, accessible, and affordable care. Patients look forward to gaining greater insight into their own health and finding a more appropriate level of care for their needs.

Artificial Intelligence & Autonomous Vehicles – The future of transport

Almost everybody has experienced artificial intelligence of one level or the other by using everyday things around them. The next big thing everybody is looking forward to is revolution in the automated mobility industry. In 2016, Apple Chief Executive Tim Cook described the challenge of building autonomous vehicles as “the mother of all” AI projects.

While big players like Google, Uber, and Tesla are competing with other each and other prominent companies, investing billions to come up with a commercially successful fleet of driverless cars, AI experts believe that it may take many a year before self-driven vehicles can successfully conquer the unpredictability of traffic.

AI plays the main role, as always

An autonomous car can be defined as a vehicle capable of navigating itself without human help, using various sensors to perceive the surrounding environment accurately.  They can make use of a variety of techniques including radar, laser light, GPS, odometry, and computer vision.

Complex algorithms, cameras and LIDAR sensors are made use of to create a digital world that orients the self-driven car on the road and helps identify fellow cyclists, vehicles and pedestrians. It is extremely difficult to design and produce such systems (Find out how Xaltius’s Computer Vision Team is building new innovative solutions). They must be programmed to cope with an almost limitless number of variables found on roads. The autonomous vehicle industry therefore looks to machine-learning as the basis for autonomous systems. That is because huge amounts of computing power are required to interpret all of the data harvested from a range of sensors and then enact the correct procedures for constantly changing road conditions and traffic situations.

Deep learning and computer vision systems can be ‘trained’ to drive and develop decision-making processes like a human. Humans naturally learn by example and this is exactly what computers are taught to do as well, ‘think like humans’.

What is deep learning? – Deep learning is a method that uses layered machine-learning algorithms to extract structured information from massive data sets (Read our blog on AI vs ML vs DL). It is a key technology behind driver-less cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. Each self-driven car is programmed to capture data for map generation, deep learning and driving tasks while they move along the traffic.

Autonomous vehicle industry developments

Google launched its self-driving car project in 2009, being one of the first to invest in this stream. Sensing that autonomous vehicle technology can open up a huge market and disrupting the current one, other tech-giants like Intel, IBM and Apple as well as cab hailing companies- Uber and Lyft and car makers have joined the race.

Alphabet’s Waymo, the self-driving technology development company was launched in December 2016. Waymo has been testing its vehicles Arizona for a little more than a year now. Places like California, Michigan, Paris, London, Singapore, Beijing among others regularly witness test-drives by self-driven cars.

The ground reality

While test-drives have become common in these places, the people have not yet adjusted to it. Research conducted by British luxury car maker Land Rover shows that 63% of people mistrust the concept of driverless cars. They are programmed to drive conservatively. While under the right conditions, it can eliminate aspects of human error and unpredictability like speeding, texting, drunken driving, when they move along with human drivers, the same unpredictability can confuse the autonomous cars. This could lead to accidents as well as a general mistrust over the technology. In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman named Elaine Herzberg in Tempe, Arizona. It is clear from regular reporting of accidents that happen during test-drives that autonomous car technology has a long way to go. Even after succeeding to avoid accidents, self-driven cars will have to face more than a decade long transition period, where humans have to accept this technology as well as give up driving.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

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