In recent years many technologies have emerged; a few stand out as having the potential to “transform” individuals’ businesses, societies, or lifestyles. These technologies are referred to as “Transformative Technologies.” They are creative and can drastically alter an organization’s business models – how it operates, interacts with customers, or meets their needs to make money. These revolutionary technologies can generate new revenue streams or change business processes to save time money or make it easier for customers to use, thereby becoming growth drivers.
However, some of these disruptive technologies may be expensive to begin with, or difficult to deploy or implement, as they may require radical changes in the way we think or work. However, if properly planned and implemented, they can be game-changers for the industry or the concerned company.
The Internet of Things (IoT), in particular, allows everything to be connected via the internet, allowing businesses and managers to control better machines and equipment in factories, offices, and hospitals, automatically or with the help of Artificial Intelligence applications.
We also recognize that Machine learning (ML) or AI products are used in various sectors of the economy, namely industrial or manufacturing activities, and in homes and hospitals where we can use Infrared rays systems.
Artificial Intelligence (AI) and Machine Learning (MI)
Artificial intelligence and machine learning are two intertwined branches of computer science. These are the two most popular technologies for developing intelligent systems.
Artificial Intelligence aims to create computer systems that simulate human intelligence. The artificial intelligence system does not need to be pre-programmed; instead, it uses algorithms to work with its intelligence. For example, it employs machine learning algorithms such as the Reinforcement Learning Algorithm and deep learning neural networks.
AI Is Classified Into Three Types Based On Its Capabilities:
- Weak AI
- General AI
- Strong AI
Machine Learning (ML) is the process that extracts knowledge from data. Machine learning allows a computer system to make predictions or make decisions based on historical data without being explicitly programmed. Instead, machine learning makes extensive use of structured and semi-structured data for a machine learning model to generate accurate results or make predictions based on that data.
Machine Learning (ML) is based on algorithms that learn using historical data. Machine Learning only works for specific domains; for example, if we create a machine learning model to detect pictures of dogs, it will only return results for dog images, but new data, such as a cat image, will become unresponsive. Machine learning is used in various applications, including online recommender systems, Google search algorithms, email spam filters, Facebook Auto friend tagging suggestions, and so on.
It Is Classified Into Three Types:
- Supervised learning
- Reinforcement learning
- Unsupervised learning
What Is A Transformative Technology, And What Does It Imply?
Transformative Technologies “transform” society or individuals’ lifestyles by dramatically changing business models – how a business operates, how to interact with customers or service their needs to make money. These game-changing technologies can usher in Blue Ocean strategies and become growth drivers by generating new revenue streams or altering business processes to save time money or make it more convenient for customers to use.
Artificial Intelligence, Machine Learning, BlockChain, 3D printing, Internet of Things (IoT), Remote Process Automation (RPA), Digital Marketing, and other technologies are expected to enable quantum growth in business and the economy; however, each of these technologies entails different types of risks, including regulatory compliance, cyber security, data privacy, money laundering, reputational risk, public safety, and so on. As a result, every organization must identify potential risks and develop effective risk management and mitigation strategies.
The Risks Of Implementing Transformational Technologies
While transformational technologies will provide enormous opportunities, they will also present significant challenges.
Cybersecurity is a set of processes that protect electronic data, human activity, and systems. Cyberspace has become an essential component of all aspects of modern life. The internet is becoming increasingly important in daily life around the world. The increasing reliance on the internet has also increased the risk of malicious threats. Because of the increasing cyber security threats, cyber security has become a critical component in the cyber world for combating all cyber threats, attacks, and fraud. The expanding cyberspace is highly vulnerable to the possibility of innumerable cyber threats. Cyber security systems have been critical since the dawn of the computer network era. Cyber security is a major area of study because all government-based, military, business, financial, and civil operations collect, process, and store massive amounts of data on computers and other systems.
Moreover, Cybersecurity aims to prevent attacks, damage, and unauthorized access to computers, networks, programmes, or data. Data science is propelling a new scientific paradigm, and machine learning (ML) can significantly alter the cybersecurity landscape.
As more people and devices connect via the IoT (IoT), cyber security becomes the most pressing concern. A hacker, for example, can use IoT to turn off the engine of a running car that is connected to a network. According to IBM’s 2019 Cost of a Data Breach Report, the average cost of a data breach is $3.92 million, affecting approximately 25,000 records. According to the report, the average breach lifespan is 314 days, limiting the breach to 200 days or resulting few in significant savings for organizations.
Data breaches have been observed to occur despite the best controls, but incident response teams and encryption have proven to help contain their costs. In addition, according to regulations, most countries require defaulting organizations to pay penalties for Cyber Security lapses.
Concerns about consumer privacy are growing worldwide, as several high-profile data breaches have compromised their customers’ personal information. Hackers can gain access to customers’ names, phone numbers, emails, passport numbers, travel details, and payment information through Cyber Attacks.
With data breaches on the rise, businesses struggle to collect information without violating their users’ privacy or exposing their personal information to malicious actors.
How Should Risks Be Managed?
A few risk-mitigation and risk-management strategies:-
- Awareness among the corporate leaders on how to manage new technologies strategically.
- The Right use of technical expertise
- Tools for Mitigating the Risks of Disruptive and Transformational Technologies.
- Software Tools to identify Vulnerabilities and Manage Risks.
Artificial Intelligence (AI)/Machine Learning (ML) Weaponized – A New Breed Of Malware:
The idea is to use AI/ML to create software that carries the malware and only deploys and activates the malicious software when a specific condition is met: face and voice recognition, geolocation, or any other information from the connected sensors to the targeted computer system.
The software will function as a “protective shell,” similar to the biological protective shell (wall) of some eucaryotic microorganisms, which shields them from harmful elements in their environment.
- AI/ML can already outperform humans in terms of automation, reducing the likelihood of error and increasing efficiency. As a result, it can be used to create better cyber-attack tools by augmenting existing ones, automating them, and instructing them to analyze all available information about the target and information gathered from previous attacks to maximize chances of success during the attack.
- AI/ML can also be used directly as an attack tool, actively searching for vulnerabilities and attacking the targeted system or network. By scanning the system and locating the protective cyber-defence software.
Artificial intelligence (AI) advancements drew the attention of scientists and practitioners, opening up a wide range of valuable opportunities for AI use in the public sector.
Conclusion
In recent decades, start-ups such as Google, Amazon, Microsoft, Uber, Tesla, and others have sprung up around such technologies and leveraged them for unparalleled growth by employing the Blue Ocean strategy.
Transformational technologies have the potential to transform the business and enable quantum growth in business and profitability, and they have the potential to become growth drivers. In some industries, they may erect a competitive barrier. However, there will be risks involved in the deployment of such technologies. As a result, every organization must identify potential risks and vulnerabilities and develop strategies to manage and mitigate the risks.
Nowadays, advancements in Artificial Intelligence (AI), or more precisely, its subfield, Machine Learning (ML), and its technologies, have pushed innovation and automation to new heights and have been very beneficial. Of course, machine learning is a wonderful tool that has been very beneficial in many areas of the industry, but it, like a coin, has two sides, one good and one bad. However, as ML becomes more popular, this leads to developing sophisticated weaponized AI/ML software. The advances in Artificial Intelligence (AI) and Machine Learning (ML) are assisting disciplines that are dealing with the challenges of learning from rapidly increasing data volume, velocity, and complexity.
AI/ML is used to augment existing cyberattack tools by automating and making them smarter or creating new tools. This new generation of hybrid intelligent tools can evade most, if not all, current cyber defence software and cause harm to the victim’s system or network.
Artificial Intelligence (AI) interest and knowledge are growing so quickly that academia and higher education struggle to keep up with industry demand. Seventy-five per cent of business software is expected to use AI, machine learning, or deep learning technologies by 2021. Still, university programmes are also burdened with students requiring elective training across multiple divisions to receive their data science training. In addition, data science necessitates specific mathematical preparation, particularly for cyber security students whose programmes have minimized the advanced mathematics requirements.
