Artificial Intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, problem-solving, perception, and language understanding. AI can be categorized into several types based on its capabilities, functions, and application domains.

Types of Artificial Intelligence

1. Narrow AI (Weak AI)

Definition: Narrow AI, also known as Weak AI, refers to artificial intelligence systems that are specialized and focused on performing a specific task or a set of closely related tasks.

Characteristics:

• Task-Specific: Designed to handle specific functions such as image recognition, language translation, or playing a game.
• Limited Scope: Operates within a predefined range and lacks the ability to generalize beyond its programmed tasks.
• No Self-Awareness: Cannot understand or reason outside its specific application.

Examples:

• Voice Assistants: Siri, Alexa, Google Assistant. They can perform tasks like setting reminders or answering questions but cannot engage in conversations outside their designed capabilities.
• Recommendation Systems: Used by platforms like Netflix or Amazon to suggest products or movies based on user preferences and behavior.
• Autonomous Vehicles: Systems like Tesla’s Autopilot use machine learning to navigate roads but are limited to driving tasks and cannot engage in other activities.

2. General AI (Strong AI)

Definition: General AI, or Strong AI, refers to an advanced form of AI that has the capability to understand, learn, and apply intelligence across a wide range of tasks, much like a human being. This is still a theoretical concept and has not yet been realized.

Characteristics:

• Broad Competence: Capable of performing any intellectual task that a human can.
• Contextual Understanding: Can understand and reason about diverse subjects and contexts.
• Adaptability: Can transfer knowledge from one domain to another and learn new tasks with minimal additional input.

Examples: As of now, there are no existing examples of General AI. It remains a subject of research and speculation, with ongoing debates about its potential development and implications.

3. Superintelligent AI

Definition: Superintelligent AI refers to a hypothetical AI that surpasses human intelligence across all fields, including creativity, general wisdom, and problem-solving. This concept is often discussed in the context of long-term future scenarios.

Characteristics:

• Superior Capability: Possesses cognitive abilities that are far beyond the best human minds.
• Potential Risks: Raises concerns about control, ethical implications, and the potential impact on society and humanity.
• Speculative Nature: Discussions around Superintelligent AI are largely theoretical and focus on its potential development and consequences.

Examples: No real-world examples exist. Superintelligent AI is often explored in science fiction and theoretical discussions about the future of AI.

4. Reactive Machines

Definition: Reactive machines are basic AI systems that operate purely on the present input without the ability to form memories or use past experiences.

Characteristics:

• Immediate Response: Reacts to specific inputs with predefined responses.
• No Learning: Does not learn from past interactions or experiences.
• Simple Design: Often simpler in design and implementation compared to more advanced AI systems.

Examples:

• IBM’s Deep Blue: A chess-playing AI that defeated grandmaster Garry Kasparov. It used predefined strategies and calculations without learning from previous games.
• Basic Chatbots: Simple bots that provide scripted responses based on keywords or phrases.

5. Limited Memory AI

Definition: Limited memory AI systems have the ability to use past experiences to improve their performance and make better decisions over time. They can retain and learn from data but only within a specific context.

Characteristics:

• Experience-Based Learning: Uses historical data to inform current decision-making.
• Contextual Memory: Can remember and use past interactions within a specific domain.
• Adaptive: Capable of improving performance as more data becomes available.

Examples:

• Self-Driving Cars: Utilize past driving data to make decisions about navigation and obstacle avoidance.
• Fraud Detection Systems: Learn from historical transaction data to identify patterns indicative of fraudulent behavior.

6. Theory of Mind AI

Definition: Theory of Mind AI aims to develop systems that can understand and simulate human emotions, beliefs, intentions, and mental states. This type of AI is still in the research phase.

Characteristics:

• Emotional Understanding: Able to recognize and respond to human emotions and intentions.
• Advanced Interaction: Facilitates more natural and intuitive interactions between humans and machines.
• Research Focus: Involves ongoing research to achieve a deeper level of human-like understanding.

Examples: No existing examples; the development of Theory of Mind AI is a goal for future AI advancements.

7. Self-Aware AI

Definition: Self-Aware AI refers to AI that has a sense of self and consciousness, including awareness of its own internal states and the ability to reflect on its actions and existence.

Characteristics:

• Self-Recognition: Has an awareness of its own state and existence.
• Reflective: Capable of introspection and understanding its role and impact.
• Ethical and Philosophical Implications: Raises profound questions about the nature of consciousness and the rights of AI.

Examples: No current examples; self-aware AI remains a theoretical concept and is the subject of philosophical and ethical discussions.

Each of these types represents a different level of complexity and capability in AI. The field is rapidly evolving, and future advancements may lead to new forms of AI or refined classifications.

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How to add value to businesses with the help of an AI Factory?

Like a physical factory creates physical products reliably at scale and speed, an artificial intelligence (AI) factory delivers AI solutions for businesses at scale and speed. An AI factory combines data, people, process, product, and platform to move beyond science experiments and deliver AI that drives business value. The AI factory builds on the principles of the AI Ladder, which describes the importance of creating solid information architecture for sustained AI success. It combines DataOps, ModelOps, and MLOps to stimulate AI innovations to market.

How an AI Factory Works

Quality data obtained from internal and external sources train ML algorithms to make predictions on specific tasks. In cases like diagnosis and treatment of diseases, these predictions can help human experts with their decisions. In content recommendation cases, ML algorithms can automate tasks with little or no human intervention.

The algorithm and data-driven model of the AI factory allows companies to test new hypotheses and make a change that improves their system. It could be new features added to an existing product or new products built on top of what the company already owns. In turn, these changes enable the company to obtain new data, improve AI algorithms, and again find new ways to increase performance, create new services and products, grow, and move across markets.

How AI Factories add Value to Businesses

In many ways, building a successful AI company is as much a product management challenge as an engineering one. Many successful companies have figured out building the right culture and processes on long-existing AI technology instead of fitting the latest developments in deep learning into an infrastructure that doesn’t work. Let’s see how an AI factory helps businesses to grow at scale.

The AI Factory begins with Centralised Governance

The idea is to pool and coordinate investment and steering efforts. Only a small number of companies’ highest-value projects will be examined by those sponsors most engaged in their success. The selection of these use cases must be extremely rigorous. No project specifically should see the light if it doesn’t respect the simple law of 10X (offer a 10:1 return on investment). The success and impact of each use case should be measurable as per a simple and understandable KPI. And the systematic improvement of this KPI the most crucial reason for the teams.

Lean AI

Lean AI is a methodology that reduces the uncertainty of efficiency and applicability of AI solutions. Models are never perfect and must be examined in real-world situations. The method contains a continuous improvement loop of short cycles which include the formulation of hypotheses, the identification of pertinent data, the construction and testing of one or more models, followed by deployment on a test perimeter, and collection of user feedback.

The cycle is repeated with the formulation of new hypotheses, new data, etc. This technique enables testing in real situations, then the improvement of cases not explored, until reaching a level of satisfaction considered acceptable by the organization to begin production.

Important Ethical Challenge

The recent example of Alexa and the unpleasant surprise of her listening have been noticed. Regulations will always lag behind technology. It is important that those enterprises that employ AI understand the ethical challenges of these solutions. Seven guiding ethical principles that were published by the Committee of Independent Experts mandated by the European Commission, includes AI at the service of humanity, trustworthiness, which respects private data, transparent, non-discriminatory, dedicated to the improvement of the common good, and finally, with a clearly defined human responsibility.

People lead to the Success of an AI Factory

An AI factory requires a team of people with a variety of skills, roles, and responsibilities to be successful, just like a physical factory. AI development traditionally involves cross-functional or full-stack technical teams. It is essential to consider the AI factory not just as a technical shop but as a market-driven business. In designing an AI factory, all jobs of AI and IT stakeholders, data scientists, data journalists, IT support, business analysts, marketing, and sales need to be done. Assigning people with clear ownership, roles, and responsibilities will add value to a business.

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Artificial intelligence (AI) has gone from being science fiction to an increasingly common part of our lives. TV streaming services use AI and machine learning (ML) to make recommendations of what you might like to watch next, for example, while other AI programmes can carry out rapid trading on the stock market without human intervention.

The information security sector hasn’t been left untouched by this trend, either. Increasingly, an AI element in cyber security technology is seen less as a nice-to-have and more as an essential part of the package.

“AI [has become] an expected feature within cyber security products and services,” says Jeff Pollard, Vice President & Principal Analyst at Forrester. “It’s now not a distinguishing characteristic or, you know, something that’s outside of the norm, it’s fully expected to be in there.”

It’s not just in defensive software that AI is playing a role, either. Microsoft has developed an AI-powered tool that can help developers spot bugs in their code with a claimed accuracy rate of 99%. In theory, this could eliminate a large portion of the software security flaws a malicious actor could exploit at the point of their creation.

An unsleeping sentinel

For those on the front lines of cyber defenses, AI is fast becoming a game changer.

Craig York, CTO at Milton Keynes University Hospitals NHS Trust, has found that AI is a vital tool in his cybersecurity arsenal. He cites the 2017 WannaCry crisis as a turning point for the IT community when it comes to security.

“WannaCry made security a board-level discussion,” York tells IT Pro.

It was at around this time that he was introduced to Darktrace, a company specialising in AI-enabled security software, by a colleague at West Suffolk NHS Foundation Trust, which was already a customer.

“Humans can only do so much,” explains York. “We have three people in our cyber security team and while they’re very capable and very diligent, they’re human beings; they take breaks, they have a cup of tea. They need lunch, and they go home at the end of the working day.

“Having the latest and greatest patches doesn’t necessarily defend against everything that’s out there at the moment. And, if anything, some of our cybersecurity attacks are coming from other parts of the world that are doing business, effectively, when we aren’t at the hospital.”

He says that it’s in this area of cyber defence that AI comes into its own.

“We need security technologies that are going to provide a safer hospital. 24 hours a day. 365 days a year, at weekends and bank holidays. The AI technology that we use from Darktrace provides some level of that – it never sleeps, so if the AI thinks that something is happening on the network that shouldn’t. It can take action straightaway.”

While AI is starting to inhabit a critical role in cyber security, particularly as IT departments and organisations as a whole adapt to the hyper-accelerated digital transformation brought about by the COVID-19 pandemic, it pays for IT leaders to think carefully about what problems they need to solve, rather than plumbing for anything labelled AI.

“The problem most cyber security vendors have is that it is just that it’s a buzzword, they can’t actually explain what they’re doing with AI – or machine learning for that matter,” cautions Pollard.

He explains that while “there are definitely use cases for AI within cyber security”, it’s not something that can – or should – be applied to everything. 

“The most productive and proven use cases for AI in cyber security are really on the detection side,” he says. “So being able to help identify, you know, malware-associated clustering of activity and behaviour. That’s really an area where it landed and it made a lot of sense.

“What we haven’t seen yet is AI expanded beyond that to more differentiated use cases, or use cases that are not just based on identifying bad things.”

Turning the tables

It’s often argued, especially by those in the tech industry, that technology is neutral. Being passive and unable to act of its own accord, it’s how it is used that is good or bad, rather than the tool itself. In this, AI is no exception.

While it’s become a key component of organisations’ cyber defence strategy, hackers and other malicious actors are also starting to use AI to craft better attacks. One example is pulling together a convincing spear-phishing email, as it’s able to research more thoroughly and more rapidly than humans. Dr Roman Yampolskiy, an associate professor at the University of Louisville, Kentucky, in the department of Computer Engineering and Computer Science and director of the university’s Cybersecurity Laboratory, has claimed the quality of such emails would be so high that “even cybersecurity experts will fall for them”.

“AI is dual use technology used by both attackers and defenders,” he tells IT Pro. “In recent years AI has become capable of finding novel exploits.”

And while others point to streamlining operations in security departments, Yampolskiy sees another long term possibility: “Like in all other fields AI will eventually fully automate all aspects of the job. Given that both attackers and defenders use AI, it will become an arms race between their AIs.”

For now, though, it’s fair to say that while organisations should be realistic in their expectations of what AI can do, incorporating it into your cyber defences is quickly becoming best practice.

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Machine Learning (ML) is reshaping the financial services like never before. It has become more prominent recently due to the availability of a vast range of data and more affordable computing power. It helps financial companies and banks to stand out of the box and achieve desired business growth.

In the modern era, financial institutions are running a race towards digitisation. Staying ahead of technological advancements is a mandatory resort for them. To keep up the pace, disruptive technologies like Artificial Intelligence (AI) and machine learning are improving the way finance sector functions. Leading banks and financial service companies are deploying AI technologies, including machine learning to streamline processes, optimize portfolios, decrease risk and underwrite loans amongst other things.

Machine Learning in Finance

Machine learning in finance is all about digesting large amounts of data and learning from the data to carry out specific tasks like detecting fraudulent documents and predicting investments, and outcomes. Machine learning uses a variety of techniques to handle a large amount of data the system processes. Various financial houses like banks, fintech, regulators and insurance forms are adopting machine learning to better their services.

Machine learning uses statistical models to draw insights and make predictions. Some of the major use cases of machine learning in the financial sector are underwriting processes, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, offering alternative credit reporting methods.

Functions of Machine Learning in Finance

Machine learning is well known for its predictions and delivery of accurate results. The financial sector involves issues of data-rich problems which could be solved by the implementation of machine learning. Similar financial issues in banking and financial series can find a solution using machine learning algorithms.

Machine learning algorithms are designed to learn from data, processes, and techniques to find different insights. Here are some of the reasons why the financial sector should adopt machine learning,

• Improves productivity and user experience

• Enhances revenue

• Low operational cost due to process automation

• Gives security to transactions

Use Cases of Machine Learning in Finance

Enhancing Financial Monitoring

Cyber risks in the financial sector are high. Unlike any other industry, finance involves a lot of money which could drive to a big loss or great fall if mishandled. Thus, financial monitoring is a provided solution for the issue through machine learning. Machine learning algorithms can be used to enhance network security significantly. Data scientists are also working on training systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring.

Making Investment Predictions

Machine learning stands out for its feature to predict the future using the data from the past. The system analyzes a large set of data and comes up with answers to various future related questions. This gives machine learning the ability to have market insights that allows the fund managers to identify specific market changes. Henceforth, divergence in the market can be detected much earlier as compared to the traditional investment models.

Well known financial institutions like JPMorgan, Bank of America and Morgan Stanley are heavily investing in machine learning technologies to develop automated investment advisors.

Maximizing Process Automation

One of the major changes that AI is driving in the financial sector is replacing human labor. Banking sectors are the primary adopters of AI applications like chatbots, virtual assistant and paperwork automation. Financial service companies followed the suit. Machine learning allows finance companies to completely replace manual work by automating repetitive tasks through intelligent process automation. This enables better customer experience and reduces cost.

Furthermore, machine learning accesses data, interprets behaviour, and recognizes patterns which will better the functions of the customer support system. Wells Fargo uses ML-driven chatbots through Facebook Messenger to communicate with the company’s users effectively.

Ensuring Safe Transaction

Machine learning is an expert in flagging transactional frauds. The mechanism analyzes millions of data points that go unnoticed by human vision. Ultimately, machine learning also reduces the number of false rejections and helps improve the precision of real-time approvals. These system models are built using previous client interaction and transaction history.

According to a report, it is predicted that for every US$1 lost to fraud, the recovery costs are US$2.92. Henceforth, detecting suspicious behavior and preventing real-time fraud is a mandatory move for the finance sector. Machine learning powered technologies are equipped to deal with the crisis.

Credit card fraud detection is the highest beneficiary of ML prediction making. The system is trained to monitor historical payments data which alarms bankers if it finds anything fishy.

Managing Risky Situations

The financial sector involves a lot of cash transactions between customers and the institutions. It increases the risk of being mishandled. However, machine learning techniques leverage security to the institutions by analyzing the massive volume of data sources. The system can go through significant volumes of personal information to reduce the risk.

For example, lending loan to an individual or an organization goes through a machine learning process where their previous data are analyzed. This could prevent from lending to fraudulent borrowers.

Enabling Algorithmic Trading (AT)

Algorithmic Trading (AT) has become a dominant force in global financial markets. Machine learning unravels the feature that allows trading companies to make decisions based on close monitoring of funds and news. It detects patterns that can enable stock price to go up or down. Some of the other benefits of Algorithm Trading are,

• Allows trades to be executed at a maximum price

• Human errors are substantially reduced

• Increases accuracy and reduces the chances of mistake

Insisting on Better Decision-making

Decision making by customers on both large and small investments is important for the finance institutions. Henceforth, financial sector organizations are suggesting customers with sources where they can get more revenue. This is possible with machine learning performing analysis on structured and unstructured data.

Amplifying Marketing Strategy

Machine learning helps financial institutions analyze the mobile app usage, web activity and responses to previous ad campaigns. This provides an insight into what could be the strategy of marketing. Machine learning and AI acts as a marketing tool under such circumstances.

Aiding Customer Retention Program

Credit card companies use machine learning technology to diagnose high-risk customers. The application includes a predictive, binary classification model to find out the customers at risk. Machine learning predicts user behavior and designs offers based on their demographic data and transaction activity.

Managing Customer Data on a Large-scale

Customer data is an asset that is valued at hundreds of millions of dollars at financial institutions. Data is the most crucial resource which makes efficient data management central to the growth and success of the business. The manual processing of data from mobile communication, social media activity, and market data is near impossible.

But AI and machine learning tools like data analytics, data mining, and NLP helps get valuable insights from data for better business profitability. For example, machine learning algorithms are being used for analyzing the influence of market developments and specific financial trends from the financial data of the customers.

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With big data streaming into organizations worldwide at the rate of 2.5 quintillion bytes of data each day, it’s incumbent on organizations to determine just how much of this big data is vital and needed, and which portions of big data are excess and can be eliminated before the data ever enters corporate systems. If companies fail to do this, bandwidth, storage, and processing capabilities can be overrun–along with budgets.

For every operation and analysis companies perform with big data, the key is to define each business use case upfront and predetermine how much data you’ll really need to address the business case. Inevitably there will be some data that you just don’t need. Paring this data out of your data ingestion process is what I call narrowing the aperture of the lens through which data streams into your data repository.

Here are two divergent examples of data lens adjustment:

IBM RoboRXN and the mechanics of molecular formulation

When IBM designed its RoboRXN project, which takes in enormous quantities of unedited data from the worldwide open source community and others on potential molecular combinations for product formulation, decisions had to be made on how much of this data was relevant to the project they were working on.

The RoboRXN project focused on designing new molecules for pharmaceutical solutions, such as the COVID-19 vaccine. This meant that white papers, statistical research findings, and other sources of research that weren’t directly germane to the molecular formulation project that was being worked were not needed. What IBM decided to do was to implement artificial intelligence (AI) at the front of the data ingestion process while this enormous trove of unedited data was streaming in.

The AI algorithm posed one major question: Did each element of incoming data contain anything relevant to the focus of the project? For research that was not at all related to the project, or that was only distantly and tangentially related, the AI eliminated the data so it was never admitted to the data repository. In other words, the aperture of the data lens to the project’s data repository was tightened, admitting only those elements of data that were relevant to the project. As a result, data storage and processing were reduced, and so was cost.

SETI and the search for extraterrestrial life

Founded in 1984, the mission of the SETI Institute was to seek out extraterrestrial life. This was done by monitoring radio signals and emissions from space to determine if there were any repetitive patterns that could signify a communication from another life form. Scientists and volunteers participated in the SETI initiative, painstakingly examining mountains of unedited radio signals that flowed in ceaselessly.

In this effort, few assumptions could be made upfront about good versus bad data, because no one was entirely sure about what they were looking for. Consequently, there were few ways to “narrow” the aperture on the data lens, which had to be kept wide open. This resulted in high levels of processing, storage, and manual work.

What the Institute was able to do was to narrow down data after it had been searched in total for potential signals that might indicate intelligent life forms. At this point, only the signals with life potential needed to be stored in much smaller databases.

Lessons from SETI and IBM RoboRXN

The examples of IBM RoboRXN and SETI’s search for extraterrestrial life are at opposite ends of the data lens spectrum. In IBM’s case, there was the ability to narrow down the data lens aperture at the front of the process. This was not the case with SETI.

What these use cases tell data scientists and IT is that there is the potential to tamp down big data ingestion at a stage of pre-processing if you have a tight enough use case that does not have the potential of requiring data that initially is regarded as extraneous. In other cases, you have limited ability to tighten up data ingestion.

The goal in every big data project should be to include a task line that addresses how wide you need to set the aperture of the data lens for incoming data. This aperture can be adjusted upward or downward based upon the needs of each use case.

When you do this, you have a realistic way of controlling the processing, storage, and funding that will be needed for each project.

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Contribution of AI to a Coronavirus vaccine

Biomedical research of vaccines against COVID-19 was already being tested in humans in March. Three months after the initial outbreak was identified in China, many of those owed their rapid start to the power of Artificial intelligence (AI).

The feat is a promising and remarkable step in more than 200 years of immunization history. The experience may revolutionize the way vaccines are developed, potentially saving countless lives in future epidemics.

According to the World Health Organization (WHO), 34 vaccine candidates were being tested in humans as of early September. Another 145 candidates were picked up to test them in animals or in the lab, says WHO keeping a worldwide running list. Considering no one had heard of the novel coronavirus less than a year ago, these numbers are surprising. Novel coronavirus now recognized as SARS-CoV-2 that causes respiratory disease COVID-19. It typically takes several years or even decades to create a vaccine. The mumps vaccine’s highest speed record went from a collected sample to a marketed product within almost four years.

Research is speeding up with time. Our society and economy likely will not return to normal until a highly effective vaccine has been administered to a substantial amount of the planet’s population. The search for a vaccine has now expanded, collaborating with thousands of researchers at hundreds of laboratories worldwide and spending billions of dollars.

Human lives and the global vaccine market are at stake, risking approximately US $35billion even before COVID-19, and governments, philanthropies, and apothecary companies have been spending accordingly. In July, the U.S. government agreed to pay nearly $2 billion to pharmaceutical giants German’s BioNTech and Pfizer for 100 million doses of a vaccine when and if it comes to the market. Other major vaccine initiatives across the world are also getting funded in the 10 figures.

Machine learning algorithms and computational analyses have played a pivotal role in the vaccine venture. These tools help researchers understand the structure of the virus and speculate which of its components will provoke an immune response, an essential step in designing vaccines. These can also help scientists choose the potential vaccines’ elements, track the virus’s genetic mutations, and make sense of experimental data.

Suchi Saria, a professor at the John Hopkins Whiting School of Engineering and director of the university’s machine learning and health care lab, says, “AI is a powerful catalyst.” She explains, “AI enables scientists “to draw insights by combining data from multiple experimental and real-world sources.” She adds, such datasets are mostly messy and challenging as the researchers historically haven’t attempted this kind of analysis.

AI has contributed to the quest for a COVID vaccine than it has ever before. It is a tiny part of a broader suite of computational tools that are revolutionizing vaccine R&D. A few people have already started thinking about the next pandemic, and on the other hand, scientists have also started to figure out how these tools will do a bit more the next time.

In the early months of the pandemic, Russ Altman and Binbin Chen led a team of computer scientists at the Standford Institute for Human-Centred Artificial Intelligence (HAI) used machine learning to see if vaccine candidates, tested in animals can provoke an excellent immune response. Using the neural-network algorithms NetMHCpan-4.0 and MARIA and DiscoTope, the scientists came up with a list of targets or epitopes on the coronavirus that is expected to provoke an immune response. These epitopes are components of the virus, which B cells and T cells will likely identify.

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Apple might be working on launching its own search engine and according to reports there are several clues online that support the possibility including job announcements for search engineers. Apple is looking to create its Spotlight Search to take on Google Search with iOS 14 beta.

Reports have it that Google pays billions of dollars to Apple to remain their default search engine on iOS, macOS and iPadOS. And this deal has allegedly also come under the scrutiny of UK market regulators. And possibly because of that, Apple might be looking to develop its own search engine.

As per Coywolf, the several indicators of Apple working on its own search engine include these job postings doe search engineers. The listings stress on integrating artificial intelligence (AI), natural language processing (NLP) and machine learning (ML) into other services.

The new post also adds that with iOS 14 beta and iPadOS 14 beta, Apple’s Spotlight Search bypasses Google Search to show search results. Applebot, which is the tech giant’s won Web crawler has been crawling sites regularly as well. The Applebot support page was also recently updated.

Going by the Spotlight Search behaviour the job postings, there are speculations that Apple’s search engine might actually be a personalised data hub. Coywolf suggests that it might be similar to Google Assistant on Android devices but devoid of ads and completely private.

Apple could put ML and AI to optimal use to return search results based in the user’s contacts, events, emails, files, messages, documents, maps, music, news, notes, photos, reminders, movies and TV shows, third-party apps etc. There are parallel speculations that Apple’s new search engine, if rumours are indeed true, is going to challenge Google’s monopoly on search and impact its ad revenue and data mining.

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The pandemic has repeatedly reaffirmed our needs for robots. The time has come for industrial robots to take over factory floors and showcase the suite of benefits they bring to manufacturing.

Robots are generally known to automate repetitive tasks and free up valuable time for their human colleagues to take on more complex and creative tasks; the current social distancing measures have built a stronger case as to why we need robots. 

Industrial robots have a long legacy of assembling everything from heavy automobiles, airplanes, electrical appliances, and are now even bring developed for more domestic tasks such as sorting out your trash.

Globally, robots have demonstrated remarkable versatility and strength in taking over human labor with consistent speed and precision. This highly efficient employee has won over factory owners. The global industrial robot market size is predicted to hit US$66.48 billion by 2027, exhibiting a CAGR of 15.1% during the forecast period, states Fortune Business Insights.

Although there is a phenomenal growth in industrial robots, a new report titled Rogue Automation by Trend Micro Research found that some robots have existing flaws that make them susceptible to cyber-attacks. 

The research paper aims to “reveal previously unknown design flaws that malicious actors could exploit to hide malicious functionalities in industrial robots and other automated, programmable manufacturing machines.”

Since robots are generally connected to networks and programmed via software, they could potentially pose as entry points for bad actors. The report listed several real-life examples of flaws found in the software produced and distributed by Swiss-Swedish multinational corporation ABB, one of the world’s largest industrial robot producers. Researchers also spotted vulnerabilities in the popular open-source software named “Robot Operating System Industrial” or ROS-I.  

Researchers discovered vulnerabilities in an app written in ABB’s proprietary programming language and used to automate industrial machines. The discovered flaw is the very tool that hackers can leverage on and gain access to networks, exfiltrating valuable files, and sensitive data.

“Industrial secrets are traded for very high prices in underground marketplaces and have become one of the main targets of cyberwarfare operations,” the study noted. 

The research also found a vulnerability that attackers can exploit to interfere with a robot’s movements via a network. By spoofing (an unknown source disguising as a known, trusted source to communicate) network packets, attackers can cause unintended movements or interrupt existing flows of set procedure, but adequately configured safety systems could make it challenging for hackers to succeed. This vulnerability found in a ROS-I’s software component was written for Kuka and ABB robots. 

The report clarified that appropriate measures were taken to deal with the discovered vulnerability. “One was removed by the vendor (ABB) upon our responsible disclosure. The other vulnerabilities fostered a fruitful conversation with ROS-Industrial, which led to the development of some of the mitigation recommendations described,” as written in the report.

Robotics are continuing to show their worth on the factory floors, and while they’ve been a fixture in many industries such as car manufacturing for decades, they are becoming increasingly advanced and versatile. Artificial intelligence (AI), machine learning (ML), cloud, and 5G are fueling the evolution of highly automated and increasingly intelligent industrial robots. 

The International Federation of Robotics estimates that by 2022, we will see close to 4 million industrial robots in factories worldwide. At the same time, the intricately connected networks between machines and systems are susceptible to the growing scale and robustness of cyberattacks.

Dr. Nicholas Patterson, a cybersecurity lecturer at Deakin University, commented that the security risks are not limited to industrial robots but also home-based robots such as robotic vacuum cleaners and drones.

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Nokia is setting up a robotics lab at the Indian Institute of Science for Research. Dubbed the ‘Nokia Centre of Excellence for Networked Robotics’, the center will serve as a hub for research into robotics, advanced communication technologies and artificial intelligence (AI).

A particular focus will be the development of “socially relevant” use cases across areas like emergency management, agriculture and industrial automation. The aim is to promote engagement between academia, startups and industry stakeholders. The initiative also aligns with Start-up India, an initiative started by the Indian government in 2015.

On announcing the launch, Nokia said emerging technologies such as 5G have potential to enable an “entirely new array of use cases with a profound societal impact.”

“With Nokia’s rich innovation heritage, we aim to engage with the bright and young minds at the Institute to nurture and advance the latest technologies that can benefit communities.

“We are confident that it will lead to the development of ground-breaking use cases.”

The Centre of Excellence will comprise a state-of-the-art network robotics lab, which will be available to the Institute’s community and partners for advanced research projects involving and designing next-gen networks and applications of AI for solving pertinent social problems.

The collaboration will see Nokia leverage its expertise in robot orchestration, robot network controller and human-robot interaction. The Institute, meanwhile, will engage its cross-disciplinary faculty and researchers, and provide its in-house expertise in algorithms, drones and robotic systems.

Some of the use cases which the tie-up plans to explore include using drones for remote management of agricultural orchards to promote water conservation and avoid human contact with pesticide, as well as the use of drones employing 5G-enabled wide-area network to gather situational information to help first responders, and drones that can help anticipate crop fires.

Drones in India

As noted by Computer Weekly, drone laws for non-defence applications have recently eased up somewhat in India. The country’s Ministry of Civil Aviation has given exemptions to government agencies for drone operations in the fight against COVID-19. Now, programs in development by airline SpiceJet, Google-backed Dunzo, among a slew of others, have the greenlight to operate on an experimental basis until September 30 this year.

This will allow those operators to pilot drones beyond the visual line of sight for transportation of goods, with the limited experiments serving as a basis for further laws which could allow long-range drone operations, such as for deliveries. Currently, laws limit drone operations to line of sight operations, limiting applications to surveillance.

“The ultimate relevance of technology is to find solutions to improve the quality of our lives,” said Professor G Rangarajan, Director at Indian Institute of Science, commenting on the partnership.

“Collaboration with a global technology leader, Nokia, will go a long way in helping our students to gain knowledge and insights and make significant contributions to the development of innovative and societally relevant 5G use cases.

“This is a critical initiative and it will help us move closer to finding technology-powered solutions to enrich our lives.”

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Artificial intelligence (AI) and machine learning (ML) are two technologies that have witnessed a massive growth trend over the years. In a quest to look for a quick, cost-efficient and innovative way to gain advantages from data science, enterprises are relying more on the use of rapidly growing big data available at their disposal. Data and analytics combined with artificial intelligence (AI) technologies will be paramount to predict, prepare and respond in a proactive and accelerated manner to ensure business continuity during this global crisis and after-forward.

Deep Learning v/s Competitive Advantage

In 10 Enterprise Analytics Trends to Watch in 2020, Frank Bernhard, author of SHAPE – Digital Strategy by Data & Analytics, notes that in 2020, deep learning should no longer be considered a buzzword, but a “tempest disruptor in how companies will perform with intelligence against their competitors.” To enable largely unsupervised learning against unstructured data in a bid to return hidden signals, deep learning will free up the time of in-demand data scientists to connect insights to action.

Responsible AI

Expect AI to be more responsible. As forecast, by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.

Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions for the spread of the deadly coronavirus and the effectiveness and countermeasure impact.

Data will Deliver Extended Business Value

With IDC predicting the global data to balloon to 175 zettabytes by 2025, deriving business value from data is becoming increasingly difficult given the complexity of the data landscape, the need for data governance and the resulting higher costs of analysis.

Significant investments made in new chip architectures such as neuromorphic hardware that can be deployed on edge devices are accelerating AI and ML computations and workloads and reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have higher business impact.

Massive Adoption of IoT

According to a report by IDC, investments in IoT technology are expected to reach $1 trillion by the end of this year. A clear indication of the anticipated growth in smart and connected devices. Many people are already using apps and devices to control their home appliances like furnaces, refrigerators, air conditioners and TVs. These are all examples of mainstream IoT technology. Expect more smart devices such as Google Assistant, Amazon Alexa and Microsoft Cortana will allow us to easily automate everyday tasks in our homes, in 2020 and beyond.

The Rise of Data Pipelines

As more data is generated, expect this data to be filtered and ready for analytics purposes. The data pipelines will ensure the citizen data scientists can do more with ML models and imbibe key strategies of action formulation.

AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trial data during the coronavirus pandemic, to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. Data Pipelines combined with AI and other techniques such as graph analytics will play a key role in identifying, predicting and planning for natural disasters and other crises in the future.

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