Explainable Artificial Intelligence (XAI) – Challenges & Opportunities

There is no denying the fact that artificial intelligence is the future. From the security forces to the military applications, AI has encompassed our daily lives as well. However, the AIs comes with its own limitations. The machines may be made by humans but the processes which they follow and the speed with which they analyse the huge volume of information is beyond human perception. This is where explainable AI (XAI) comes into the picture. It ensures that the humans can understand the reasoning and the logic behind every decision these machines take and use the knowledge to develop better machines.

How does Explainable Artificial Intelligence work?

Explainable AI (XAI) is artificial intelligence that is programmed to describe its purpose, justification and decision-making process in a way that can be understood by the average person. XAI is often conversed in relation to deep learning and its important role in the FAT ML model (fairness, accountability and transparency in machine learning).

XAI program will incorporate new explanation techniques based on the result produced by the machines to create more explainable models and outputs. Optimization techniques, architectural layers, design data and many other processes are used to experiment and develop interpretable models of the AI machines. Model induction would also take place to treat the machine processes like a black box and experiment with it to develop a better understanding of its processes.

GAVS Technologies collaborates with AI-based companies to create better understanding and learning techniques to provide a greater support for the machine-human relationship. The machine learning and AI capabilities of GAVS’ will support these companies and in their various test and research activities of XAI prototypes to create more explainable models. These prototypes would be available commercially as well create a more robust XAI solutions once they’re approved.

Challenges and opportunities for XAI:

Explainability is a scientifically interesting and socially important topic that is the crux of several areas of active research in machine learning and AI. The challenges that XAI face include:

  • Bias: How can I ensure that my AI system hasn’t learned a biased view of the world (or perhaps an unbiased view of a biased world) based on shortcomings of the training data, model, or objective function? What if its human creators harbor a conscious or unconscious bias?
  • Fairness: If decisions are made based on an AI system, how to verify that they were made fairly? Fairness is contextual and has different perspectives depending on the particular data input given to the machine learning algorithms.
  • Transparency: On what basis can individuals have the right to have the AI decisions explained in layman’s terms. Where and how can they be appealed? XAI tries to answer the transparency issues in intelligent systems.
  • Safety: Can customers gain confidence in the reliability of the AI system without an explanation of how it reaches conclusions? This is closely related to the fundamental problem of generalization in statistical learning theory i.e. how tightly can we bind errors on the unseen data?
  • Causality: Can the learned model provide not only correct inferences but also some explanation for the underlying phenomena? Can users gain a mechanistic understanding of a learned model?
  • Engineering: How to debug incorrect output from a trained model?

All these points present opportunities for businesses to leverage.

  • Collaboration for future innovations

The recently founded Partnership on AI was formed to bring together researchers, developers, and users to ensure that AI technologies work to serve people and society. The partnership’s mission includes addressing challenges and concerns around “the safety and trustworthiness of AI technologies, [and] the fairness and transparency of systems.” It is a platform that unites organization from different levels to collaborate on addressing concerns, rough edges, and rising challenges around AI, as well as to work together to pursue the opportunities and possibilities of the long-term dream of mastering the computational science of intelligence.

  • Issues not specific to deep learning or machines

Deep neural networks have achieved exceptional improvements on a number of challenging tasks, in part due to their enormous expressive power. This power, enabled by large number of free parameters and non-linearities, can make it difficult to interpret the learned values of any given parameter, especially in the deeper layers. XAI present an interesting opportunity to overcome these similar challenges.

  • Learn how to deal with AI systems that outperform humans in specific tasks

AI systems that outperform humans in specific domains already exist and will become more common. One consequence of an AI system’s astounding performance may be that there is no explanation for how it works that is easily acceptable by a human. Studies and research in developing a similar framework for AI systems in critical deployments are necessary to benefit from deploying some tools even before they are completely understood.

  • Make decision-making more systematic and accountable

XAI vastly improves the quality of decision makings and hold the respective stakeholders accountable. For an engineer, this translates to system requirements that can be designed, measured, and continuously tested. They will depend on the domain where they are applied. As we rely more on automated systems for making decisions, it gives us an unprecedented opportunity to be more explicit and systematic about the principles or values that guide on how we decide.

Cost Factor of a Data Breach Including Its Consequences

Every day there is some information on Data breaches occurring around the world. The regularity at which this is happening raises questions on the security and safety measures applied to the digital data throughout the organization.

Determining how costly a data breach can be depends on several factors. Some direct costs include the regulatory fees, technical repair costs, legal costs, the compensatory costs to victims, the costs to pay for determining how the breach occurred, and what can be done to prevent further breaches. Indirect costs include lost profits and damage to the company’s reputation.

Depending on the business type (such as healthcare, financial, etc.) or the country, the cost of the data breach can vary. This amount can be broken down for each business vertical or for the whole organization.

Every year the Ponemon Institute releases their “Cost of a Data Breach Study” where they analyze the lasting cost and impact of information security breaches. Based on the report, the average cost was around $141 per data record in 2017.

So, assuming the same trend will continue, we can expect the cost of data breaches to be around the same range or increase.

According to the Verizon’s 2018 Data Breach Investigations Report there are over 53,000 incidents and 2,216 confirmed data breaches. The threat actors behind a data breach and their corresponding motives can be represented at a high level as below statistics:
For many businesses it’s more than losing the initial dollars, it’s also about the legal costs, the lowered brand reputation and loss in customer base.

Probable root causes for data breaches

The most likely root causes for a majority of the data breaches is human error with malicious or cyber security attacks and system glitches contributing the rest. These breaches are the result of intentional efforts to capture secure information, whether through hacking, phishing scams, or theft of files. These attackers are looking for holes in the system that they can take advantage of to hack the vulnerable systems.

The overall findings of the Verizon Data Breach report are based on the 4As (Actor, Action, Asset, Attribute)

Actor (External, Internal, Partners or Multiple) and the actor motivation factor (Financial, Espionage, Grudge, Fun and others)

Threat actions (Hacking, Malware, Misuse, Social, Error, Physical, and Environment)

Assets involved in data breaches (Databases, POS terminals, Web servers, Desktops, Documents, Mail servers, Human personnel, Laptops, etc.)

Attributes (Personal, payment, medical, Credentials, Internal, Secrets, Systems, Bank and Classified information)

According to the report Phishing and pretexting represent 98% of social incidents and 93% of breaches. Email continues to be the most common vector (96%). Phishing individuals (Social) and installing keyloggers (Malware) to steal credentials (Hacking) is still a common path for data breaches.

The 9 incident classification patterns include: Crimeware, Cyber-Espionage, Lost/Stolen Assets, Miscellaneous Errors, Payment Card Skimmers, Point of Sale, Privilege Misuse, Web Applications and Everything Else.
It only takes one incident to compromise the systems and result in data breaches.

Ways to reduce the cost of a data breach

How organizations react has a big impact on their future actions with respect to data breaches. Companies should have strategies in place to prevent breaches and to minimize damages when they happen. Some of these methods include:

• Using an incident response team.
• Encrypting data records.
• Regular training of employees on how to prevent breaches, report breaches, and respond to breaches.
• Employing security measures to minimize the likelihood of breaches.
• Having a redundancy plan so that the damaged data can be accurately restored.

Alternatively, engage a security consultant/expert who can perform comprehensive risk assessment using international risk standards and assess the business impact to understand the current security posture of the organization.

Collaborating with GAVS Technologies, companies can leverage its expertise in infrastructure services and digital solutions. Our services include automation led infrastructure services particularly in the security and identity management services which are enabled by smart machines, DevOps & predictive analytics. GAVS’ focus is to strengthen governance & transformation through security, cloud orchestration, Governance, Risk & Compliance (GRC)

Using our analytics platform, companies can invest in behavioural detection in combination with machine learning based on AI, which will help in identifying the malware’s path and take the necessary steps to protect the organisation.

Another big cost-saving factor is reducing the time it takes to discover the breach. Many breaches aren’t discovered until months after the breach occurred.

• Automatically apply patch updates regularly or deploy technology which removes the need for manual updating. This technology works by automatically identifying vulnerable applications and deploying the latest updates as they become available.
• Implement a regular backup process using a simple ‘one touch’ rollback system to allow organisations to automatically roll back and regain access to their data.

Effective from May 2018, the General Data Protection Regulation (GDPR) will make data breach reporting mandatory. Non-compliance with GDPR and its other requirements will result in a regulatory fine.
It might not be possible to completely prevent breaches, but the right preparation can drastically reduce the resulting cost.

Get in touch with GAVS at https://www.gavstech.com/reaching-us/ to understand how to prevent data breaches and its cost associated consequences.

Pivotal Role of AI and Machine Learning in Industry 4.0 and Manufacturing

Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing and cognitive computing. Industry 4.0 is commonly referred to as the fourth industrial revolution.

Industry 4.0 is the paving the path for digitization of the manufacturing sector, where artificial intelligence (AI) and machine-learning based systems are not only changing the ways we interact with information and computers but also revolutionizing it.

Compelling reasons for most companies to shift towards Industry 4.0 and automate manufacturing include

  • Increase productivity
  • Minimize human / manual errors
  • Optimize production costs
  • Focus human efforts on non-repetitive tasks to improve efficiency

Manufacturing is now being driven by effective data management and AI that will decide its future. The more data sets computers are fed, the more they can observe trends, learn and make decisions that benefit the manufacturing organization. This automation will help to predict failures more accurately, predict workloads, detect and anticipate problems to achieve Zero Incidence.

GAVS proprietary AI led predictive analytics solution – GAVel  can successfully integrate AI and machine learning into the workflow allowing manufacturers to build robust technology foundations. This means creating a purpose-built, big data architecture that can aggregate data from disparate systems, such as enterprise resource planning (ERP), manufacturing execution systems and quality management software.

To maximize the many opportunities presented by Industry 4.0, manufacturers need to build a system with the entire production process in mind as it requires collaboration across the entire supply chain cycle.

Top ways in which GAVS expertise in AI and ML are revolutionizing manufacturing sector:

  • Asset management, supply chain management and inventory management are the dominant areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. Combining these emerging technologies, they can improve asset tracking accuracy, supply chain visibility, and inventory optimization.
  • Improve predictive maintenance through better adoption of ML techniques like analytics, Machine Intelligence driven processes and quality optimization.
  • Reduce supply chain forecasting errors and reduce lost sales to increase better product availability.
  • Real time monitoring of the operational loads on the production floor helps in providing insights into the production schedule performances.
  • Achieve significant reduction in test and calibration time via accurate prediction of calibration and test results using machine learning.
  • Combining ML and Overall Equipment Effectiveness (OEE), manufacturers can improve yield rates, preventative maintenance accuracy and workloads by the assets. OEE is a universally used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness.
  • Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios that reduces costs by 50% or more.

Direct benefits of Machine Learning and AI for Manufacturing

The introduction of AI and Machine Learning to industry 4.0 represents a big change for manufacturing companies that can open new business opportunities and result in advantages like efficiency improvements among others.

  • Cost reduction through Predictive Maintenance that leads to less maintenance activity, which means lower labor costs, reduced inventory and materials wastage.
  • Predicting Remaining Useful Life (RUL). Keeping tabs on the behavior of machines and equipment leads to creating conditions that improve performance while maintaining machine health. By predicting RUL, it reduces the scenarios which causes unplanned downtime.
  • Improved supply chain management through efficient inventory management and a well monitored and synchronized production flow.
  • Autonomous equipment and vehicles: Use of autonomous cranes and trucks to streamline operations as they accept containers from transport vehicles, ships, trucks etc.
  • Better Quality Control with actionable insights to constantly raise product quality.
  • Improved human-machine collaboration while improving employee safety conditions and boosting overall efficiency.
  • Consumer-focused manufacturing – being able to respond quickly to changes in the market demand.

Touch base with GAVS AI experts here: https://www.gavstech.com/reaching-us/ and see how we can help you drive your manufacturing operation towards Industry 4.0.

Artificial Intelligence (AI) or Intelligence Augmentation (IA) – What’s the difference?

When Artificial Intelligence (AI) technology was first introduced, it was hailed as the next big advancement moving us closer to achieving human superiority. Just as we became comfortable with this idea a new one cropped up about AI replacing humans leading to irrational fears.

At this juncture, AI proponents put forth the concept of Intelligence Augmentation (IA) that kept the humans in the loop while still maintaining control over the machines. Interchange the letters of AI and IA offers a completely different perspective on AI. Intelligence Augmentation (IA) – another name for cognitive intelligence – is gaining fast precedence among business leaders.

What’s the difference?

AI is built to replace human intervention and interactions involving repetitive tasks, thereby reducing human errors and operating costs, and improve efficiency and productivity. Intelligence augmentation on the other hand is built to assist humans in their cognitive tasks and complement in the decision-making process.

IA or cognitive computing is a more comprehensive branch as it can “reason” over all structured and unstructured data and deal with “grey areas” to help make judgements and decisions.

Consider the example of a target-driven salesperson. She must close deals based on various information inputs like emails, CRM, quotas, etc. AI can help her to assimilate all these disparate information sources and organize it for easy analysis. But what happens next? How will she gain insights for closing the deal?

That’s where AI work ends, and IA comes into the picture. IA offers her potential actions that can be taken to convert a lead like offering discounts, providing additional incentives, customer support etc. It assists her in completing the task.

This is one example where both AI and IA complement each other and ultimately help humans in the long run.

Businesses are recognising this important fact and reaching out to expert service providers in this field like GAVS Technologies to integrate it in their business processes.

GAVS for bridging the AI gap between Humans and Machines

GAVS AI led analytics solution, GAVel is part of the current AI and IA landscape that tries to bridge the gap between man and machine to provide adequate insights from the information overload that businesses face.

GAVel  is an aggregator and correlates ITIL data across multi-cloud and on-premise infrastructures to provide real time insights leading to faster incident remediation.

GAVS’ approach also involves cognitive computing that includes a comprehensive set of capabilities based on emerging technologies like language, speech and vision technologies; ML, reasoning and decision technologies; Brain-Computer Interface (BCI), which connects the brain with an external computing device to augment or repair human cognition; distributed and high-performance computing; and new computing architectures and devices.

Integrating these varied technologies requires technical expertise, resources and adequate planning that GAVS Technologies provide through their comprehensive AI led digital solutions. They provide solutions for a wide range of practical problems, boost productivity and foster new discoveries across many industries.

Reach out to GAVS Artificial intelligence and Intelligence Augmentation experts at https://www.gavstech.com/reaching-us/