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The world is up in the clouds (

Spotting Risks

The one place

Data-Intensive Defence Strategy

When using AI technology, the entire setup will involve every email going to an external system (off-site) to be analyzed. This is especially the case with industries that deal with a lot of highly sensitive information and cannot afford to leak that data elsewhere. A machine learning technology would have to keep a part of this sensitive information to learn rules and accurate decisions from it. Given how machine learning works, it goes through a learning phase that can last for months, hence cannot provide instant security controls. For this reason, many companies are not comfortable with their sensitive data being sent elsewhere.

AI’s Role In Cybersecurity

The world is up in the clouds ( cloud computing ) and the fourth industrial revolution is transforming our lives, society, and our work. While it is making things easy and accessible, it comes with its own perils like cyberattacks. The need to make the cybersecurity landscape stronger is more than ever as cybercriminals have become more clever. 2023 gave cyberattackers more opportunities to strike like email phishing scams. In terms of cyberattacks, we’ve reached a new low where phishers are using the COVID-19 vaccine rollout to trick people into paying for fake vaccines. Scientists are working day and night to create innovative artificial intelligence and machine learning tools to eliminate evolving exploits. But the use of AI as a cybersecurity arsenal is being debated by experts. When it comes to determining what type of data is safe to send outside the company, humans do a much better job in making intricate decisions than machines. Relying on AI to make such decisions can lead to leaked data if the AI technology is not mature enough to fully understand the gravity of the situation. So how exactly does artificial intelligence fit into the cybersecurity picture, and where can it present challenges?The one place artificial intelligence feels challenged when it comes to mitigating the risk from accidental insider breaches is spotting similarities between documents and knowing what files are okay to send to a specific person. For example, company invoices have the same template each time they are sent, with minor text differences that machine learning and artificial intelligence fail to distinguish. The technology will categorize all the invoices as the same despite differences in text and numbers, allowing a user to send the attachments, whatsoever. Whereas a human would know which invoice should be sent to a particular customer. In a large organization, this kind of AI technology would only limit a small number of emails from being sent, and when it does find an error, it will intimate the administration and not the person sending the wrong chúng tôi using AI technology, the entire setup will involve every email going to an external system (off-site) to be analyzed. This is especially the case with industries that deal with a lot of highly sensitive information and cannot afford to leak that data elsewhere. A machine learning technology would have to keep a part of this sensitive information to learn rules and accurate decisions from it. Given how machine learning works, it goes through a learning phase that can last for months, hence cannot provide instant security controls. For this reason, many companies are not comfortable with their sensitive data being sent chúng tôi a business’ cybersecurity system, AI has a critical role to play. For instance, antivirus software operates on a ‘yes or no’ policy to determine if the file is malicious or not. AI can quickly find out whether it’s going to crash the system, take down the network, etc. So while AI might not be the best weapon of defence for preventing data leakage through email, it does have an important role to play in select areas like threat analysis and virus detection.

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The Future Of Artificial Intelligence In Manufacturing

Industrial Internet of Things (IIoT) systems and applications are improving at a rapid pace. According to Business Insider Intelligence, the IoT market is expected to grow to over $2.4 trillion annually by 2027, with more than 41 billion IoT devices projected.

Providers are working to meet the growing needs of companies and consumers. New technologies, such as Artificial Intelligence (AI), and machine learning make it possible to realize massive gains in process efficiency.

With the growing use of AI and its integration into IoT solutions, business owners are getting the tools to improve and enhance their manufacturing. The AI systems are being used to:

Detect defects

Predict failures

Optimize processes

Make devices smarter

Using the correct data, companies will become more creative with their solutions. This sets them apart from the competition and improves their work processes.

Detect Defects

AI integration into manufacturing improves the quality of the products, reducing the probability of errors and defects.

Defect detection factors into the improvement of overall product quality. For instance, the BMW group is employing AI to inspect part images in their production lines, which enables them to detect deviations from the standard in real time. This massively improves their production quality.

Nokia started using an AI-driven video application to inform the operator at the assembly plant about inconsistencies in the production process. This means issues can be corrected in real time.

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Predict Failures

Predicting when a production line will need maintenance is also simple with machine learning. This is useful in the sense that, instead of fixing failures when they happen, you get to predict them before they occur.

Using time-series data, machine learning models enhance the maintenance prediction system to analyze patterns likely to cause failure. Predictive maintenance is accurate using regression, classification, and anomaly detection models. It optimizes performance before failure can happen in manufacturing systems.

General Motors uses AI predictive maintenance systems across its production sites globally. Analyzing images from cameras mounted on assembly robots, these systems are identifying the problems before they can result in unplanned outages.

High speed rail lines by Thales are being maintained by machine learning that predicts when the rail system needs maintenance checks.

Optimize Processes

The growth of IIoT allows for automation of most production processes by optimizing energy consumption and predictions for the production line. The supply chain is also improving with deep learning models, ensuring that companies can deal with greater volumes of data. It makes the supply chain management system cognitive, and helps in defining optimal solutions.

Make Devices Smarter

By employing machine learning algorithms to process the data generated by hardware devices at the local level, there is no longer a need to connect to the internet to process data or make real-time decisions. Edge AI does away with the limitation of networks.

The information doesn’t have to be uploaded to the cloud for the machine learning models to work on it. Instead, the data is processed locally and used within the system. It also works for the improvement of the algorithms and systems used to process information.

Also read: The 15 Best E-Commerce Marketing Tools

What’s Next?

The manufacturing market is seeing a huge boost thanks to the IIoT and AI progress. Machine learning models are being used to optimize work processes.

The quality of products is getting improved by reducing the number of defects that are likely to occur. This is expected to improve over time, and it also will heavily improve the production process to reduce errors and defects in products.

There is still a huge potential of AI that has yet to be utilized. Generative Adversarial Networks (GAN) can be used for product design, choosing the best combination of parameters for a future product and putting it into production.

The workflow becomes cheaper and more manageable. Companies realize this benefit in the form of a faster time to market. New product cycles also ensure that the company stays relevant in terms of production.

Networks are set to upgrade to 5G, which will witness greater capacities and provide an avenue for artificial intelligence to utilize this resource better. It will also be a connection for the industrial internet of things and see a boost in production processes. Connected self-aware systems will also be useful for the manufacturing systems of the future.

What Is Prediction, Detection, And Forecasting In Artificial Intelligence?

Understanding the Difference among Detection and Forecasting Models, and Predictive Analytics and Leveraging Them in Business.

We do not need a soothsayer to realize how

Understanding the differences

While detection and

How can they help Business?

Detection Vs. Prediction A paper published by MIT states how detection can help businesses via a Forecasting vs. Prediction Coming to forecasting, Business leveraging Artificial Intelligence-based forecasting models, can figure out trends that shall dominate the market in the coming days. Forecasting relies on the input of base data to arrive at an outcome. The quality of this data affects the results, unlike prediction or predictive models that have no separate input or output variable. Typically, forecasting is all about the numbers and using level and trend and seasonality observations to predict outcomes; predictive analytics is more about understanding consumer behavior. Even though forecasting is considered as projective of predictive models, the former is based on temporal information. It is scientific and free from intuition and personal bias, whereas prediction is subjective, arbitrary, and fatalistic by nature. This is why we have weather forecast instead of weather prediction. We need to strike a balance when employing these algorithms in Business. For, e.g., forecasting can help in

Outlook:

We do not need a soothsayer to realize how Artificial Intelligence (AI) has transformed our lives. From using machine learning for drug discovery to facial unlock ID using facial recognition, its’ application is everywhere. While AI may not say what the next reading on a dice (or magic 8) ball can be, it surely can predict the probability of getting 6 in the next roll of dice. The predictive aspect of AI has become more refined and accurate with time, thanks to deep learning and data analytics . However, the question is, can Artificial Intelligence do more than just prediction like forecasting or detection of a trend?While detection and forecasting may sound similar to predictive analytics or simply prediction, they are different. Detection refers to mining insights or information in a data pool when it is being processed. This can be the detection of objects, fraudulent behaviors, and practices, anomalies, etc. Whereas, forecasting is a process of predicting or estimating future events based on past and present data and most commonly by analysis of trends or data patterns. Unlike predictions, it is not vague and is defined by logic. Prediction or predictive analysis employs probability based on the data analyses and processing. Out of the three, it is the more uncertain, complicated, and expensive process.A paper published by MIT states how detection can help businesses via a smoke detector-crystal ball analogy. Here, smoke detector and crystal ball are metaphorically examples of how detection and prediction work. Smoke detectors issue warning signals of an impending fire hazard. They don’t predict the possibility of a fire accident. Based on early warning, we are presented options: whether to extinguish the fire/smoke source or escape the scene. Similarly, businesses can benefit from detecting issues quickly, even if they are unpredicted. By leveraging detection algorithms of AI, companies always have the chance to act and manage outcomes and other functions even when they might have missed the opportunity to prevent any shortcomings or bottlenecks. Detection always encourages action using multiple solutions. Further, it is always definite as they offer some value, unlike the uncertainty offset of predictive analytics . This can help to boost ROI at minimal costs. One use case is, instead of trying to predict which customers will churn, managers, can shift to detect better which customers are dissatisfied. The implications may be similar, but changes in satisfaction are measurable while customers who were going to leave but didn’t are. Also, detection models can be used in every stage of the business pipeline, just like smoke detectors in every flat in an apartment. They help us to make sense of the activities and business insights. These can be identifying where data signals are currently missing. Where data signals have poor quality? Where are data signals giving false alarms causing system fatigue? All these go in the long run in enlightening ways to augment and enhance the productivity channels.Coming to forecasting, Business leveraging Artificial Intelligence-based forecasting models, can figure out trends that shall dominate the market in the coming days. Forecasting relies on the input of base data to arrive at an outcome. The quality of this data affects the results, unlike prediction or predictive models that have no separate input or output variable. Typically, forecasting is all about the numbers and using level and trend and seasonality observations to predict outcomes; predictive analytics is more about understanding consumer behavior. Even though forecasting is considered as projective of predictive models, the former is based on temporal information. It is scientific and free from intuition and personal bias, whereas prediction is subjective, arbitrary, and fatalistic by nature. This is why we have weather forecast instead of weather prediction. We need to strike a balance when employing these algorithms in Business. For, e.g., forecasting can help in marketing and promotional planning, but predictions can help estimate sales for targeting chúng tôi bottom line is that businesses need to understand the key differences and use cases of predictive analytics, detection algorithms, and forecasting models of Artificial Intelligence. Then they can employ them as per their requirement to achieve brand goals.

Artificial Intelligence: Digital Marketing’s Benefits

Artificial intelligence refers to the creation of intelligent machines capable of performing cognitive tasks. Their ability to think like humans will increase once they have enough data. Digital marketing is a key area where artificial intelligence, data, and analytics are important.

Any online venture must be able to extract the right insights from data in order to succeed. It is therefore logical to assume that AI will become a key component of digital marketing. This is especially true considering the huge growth in data and sources digital marketers need to understand.

Experts predict that the volume of data collected across these newer customer touchpoints will become overwhelming. As businesses grow, this will continue to happen over the next few years. Artificial intelligence (AI), which is used to analyze data and make decisions for digital marketing, is more important than ever. Here are some reasons AI tools and technology have access to huge amounts of data that is not easily accessible. AI can transform this data into useful insights that allow for immediate decisions.

AI-Driven Content Marketing

Artificial intelligence can help you determine the content that interests your clients and current customers. It can also determine the best ways to reach them.

AI can create visuals and material that it expects to be appreciated by its target audience and is increasingly capable of managing the entire content creation process. Personalization allows clients to receive material that is tailored specifically for them. AI uses data and references to help it understand what clients are looking for. Personalization is an industry buzzword.

Real-Time Tracking

Platforms that integrate AI allow users to see the effectiveness of their content and adjust their strategy in real-time. This means that digital marketers can instantly see the results and adjust their next strategy.

Dynamic Pricing

Discounts are a great way to increase sales. Some clients might still purchase with a small or no discount.

Artificial intelligence can set product prices dynamically to increase sales and profitability. This is done based on factors such as client profiles, demand, supply, client, and other criteria. The price of each product is shown in a graph. It will show how it changes according to season, consumer demand, and other factors.

A great example of dynamic pricing has been demonstrated by frequent travelers. They book a flight, then return to purchase it a few days later to find that the price had gone up by a few hundred dollars.

Better Security

Biometric authentication systems that use AI technology are among the most secure for transferring and gathering data. It has also increased the efficiency of the sharing process.

Large amounts of data can now be transmitted much more securely than they used to be. Modern data collection and dissemination have made it easier to analyze large amounts of data. This has led to faster decision-making and enhanced insights.

Chatbots for Customer Service

Customers use messaging apps like WhatsApp and Facebook Messenger to communicate with companies. It can be costly to keep active customer service representatives on these platforms.

Chatbots are being used by some businesses to respond to customer queries frequently. Chatbots can provide immediate responses to customers, reducing workload and giving them a faster response. Chatbots can also be trained to provide pre-determined answers to commonly asked questions. Chatbots can also forward complex queries to human operators.

This means that you can reduce customer service time. You also reduce the agent burden by making it easier for them to deal with issues that require a personal response.

Chatbots are cheaper than adding more team members and can deal with customer issues faster. In some cases, they can even be more humane. Bots don’t have bad days like humans. They are friendly, approachable, and easy to like.

Neuromorphic Chips: The Third Wave Of Artificial Intelligence

The age of traditional computers is reaching its limit. Without innovations taking place, it is difficult to move past the technology threshold. Hence it is necessary to bring major design transformation with improved performance that can change the way we view computers. The Moore’s law (named after Gordon Moore, in 1965) states that the number of transistors in a dense integrated circuit doubles about every two years while their price halves. But now the law is losing its validity. Hence hardware and software experts have come up with two solutions: Quantum Computing and Neuromorphic Computing. While quantum computing has made major strides, neuromorphic is still in its lab stage, until recently when Intel announced its neuromorphic chip, Loihi. This may indicate the third wave of Artificial Intelligence. The first generation of AI was marked with defining rules and emulated classical logic to draw reasoned conclusions within a specific, narrowly defined problem domain. It was well suited to monitoring processes and improving efficiency, for example. The second generation was populated by using deep learning networks to analyze the contents and data that were largely concerned with sensing and perception. The third generation is about drawing parallels to the human thought process, like interpretation and autonomous adaptation. In short, it mimics neurons spiking like the nervous system of humans. It relies on densely connected transistors that mimic the activity of ion channels. This allows them to integrate memory, computation, and communication, at higher speed, complexity, and better energy efficiency. Loihi is Intel’s fifth-generation neuromorphic chip. This 14-nanometer chip has a 60-millimeter die size and contains over 2 billion transistors, as well as three managing Lakemont cores for orchestration. It contains a programmable microcode engine for on-chip training of asynchronous spiking neural networks (SNNs). Total, it has 128 cores packs. Each core has a built-in learning module and a total of around 131,000 computational “neurons” that communicate with one another, allowing the chip to understand stimuli. On March 16, Intel and Cornell University showcased a new system, demonstrating the ability of this chip to learn and recognize 10 hazardous materials from the smell. And this can function even in the presence of data noise and occlusion. According to their joint profiled paper in Nature Machine Intelligence, this can be used to detect the presence of explosives, narcotics, polymers and other harmful substances like signs of smoke, carbon monoxide, etc. It can purportedly do this faster, more accurate than sniffer dogs thereby threatening to replace them. They achieved this by training it constructing a circuit diagram of biological olfaction. They drew this insight by creating a dataset by exposing ten hazardous chemicals (including acetone, ammonia, and methane) through a wind tunnel, and a set consisting of the activity of 72 chemical sensors collected the signals. This tech has multifold applications like identifying harmful substances in the airport, detecting the presence of diseases and toxic fumes in the air. The best part is, it constantly re-wires its internal network to allow different types of learning. The futuristic version can transform traditional computers into machines that can learn from experience and make cognitive decisions. Hence it is adaptive like human senses. And to put a cherry on top, it uses a fraction of energy than the current state of art systems in vogue. It is predicted to displace Graphics Processing Units (GPUs). Although Loihi may soon evolve into a household word, it is not the only one. The neuromorphic approach is being investigated by IBM, HPE, MIT, Purdue, Stanford, and others. IBM is in the race with its TrueNorth. It has 4096 cores, each having 256 neurons and each neuron having 256 synapses to communicate with others. Germany’s Jülich Research Centre’s Institute of Neuroscience and Medicine and UK’s Advanced Processor Technologies Group at the University of Manchester are working on a low-grade supercomputer called SpiNNaker. It stands for Spiking Neural Network Architecture. It is believed to stimulate so-called cortical microcircuits, hence the human brain cortex and help us understand complex diseases like Alzheimer’s.

The Promise Of Artificial Intelligence In Precision Medication Dosing

In the United States alone, drug-related problems in patients account for

AI Transforms Dosing and Gives Patients a Personalized Fit

The most compelling approach to solving this important problem to date is with the application of artificial intelligence to enable precision dosing. Precision dosing is an umbrella term that refers to the process of transforming a “one-size-fits-all” therapeutic approach into a targeted one, based on an individual’s demonstrated response to medication Precision dosing has been identified as a crucial method to maximize therapeutic safety and efficacy with significant potential benefits for patients and healthcare providers, and AI-powered solutions have so far proven to be among the most powerful tools to actualize precision dosing. In 2008, Dr. Donald M. Berwick, former Administrator of the Centers for Medicare and Medicaid Services,

Better Decision Support in Dosing Achieved

Despite significant promise, applications of precision dosing have tended to be difficult to scale (

5 Factors That Came Together to Make Now the Right Time for AI-Powered Dosing

Several factors have come together to create the necessary conditions to begin realizing the potential for AI-powered precision dosing: Public familiarity with artificial intelligence as an effective tool for solving complex problems makes physicians comfortable incorporating such tools in clinical settings. Reliable data is now available in electronic medical records and is standardized in a manner that is much more ingestible by algorithms as compared to free-form paper medical records. Big data analytics techniques have also made applying artificial intelligence and control algorithms to complex datasets much more practical and efficient. We can draw on data from millions of patients to design and test algorithms in silico to predict effectiveness and iterate quickly. This is a vast improvement on expert systems that are based on a clinician’s smaller number of patients, possibly in the thousands or hundreds, that are generally only possible to test in much more costly and risky clinical trials. Increasingly complex and powerful drugs have been developed that impact basic physiologic processes. Drugs that impact multiple physiologic processes and have a narrow therapeutic window (the “sweet spot” between toxicity and ineffective therapy) have become more prevalent. These are the types of drugs for which AI-powered drug dosing can provide the most benefit.

Chronic Anemia Offers an Especially Powerful Opportunity to Apply AI-Powered Dosing

AI-powered precision dosing will likely be the standard of care for chronic disease management in the future. Artificial intelligence is a valuable tool that can enhance a physician’s ability to practice and make the best judgements possible, improving the cost of care and the quality of care itself. Dosing anemia drugs is only one, specific example of the impact that AI can have on medication prescribing. Dosis has already begun a trial of an AI-based intravenous iron dosing protocol, as an adjunct to Strategic Anemia Advisor. In addition, Dosis has developed a tool that informs the simultaneous dosing of three different types of medication that are used to manage mineral and bone disorder, a common comorbidity in kidney disease patients. This application will be the first of its kind, modelling three interdependent biological variables and three medications simultaneously that impact these values to return them to normal levels. Once AI for precision drug dosing is widely adopted, it will be extremely unlikely for the industry to revert back to previous dosing methods. The efficacy gap between AI-powered tools and legacy dosing methods will also only widen, as more data is incorporated into these tools. In 10 years, AI-driven dosing models will likely be the standard of care across the healthcare spectrum, used for a wide variety of drugs like warfarin, insulin, and immunosuppressives. Indeed, any drug that is administered chronically and has a narrow therapeutic range is a good candidate for AI-driven dosing. In addition, as more tools are developed and more opportunities to use those tools are identified, we will see exponential growth in the use of AI to drive therapies.

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