Introduction

Netflix is one of the examples of a machine learning application while AlphaGo DeepMind is Google’s Deep Learning.

The phrases artificial intelligence (AI), machine learning, and deep learning have become increasingly commonplace, even outside of data science. The two terms are often used synonymously. While they share some common ground, these phrases signify different things when discussing autonomous vehicles.

In the broader context of artificial intelligence, deep learning may be thought of as a subset of machine learning. Artificial intelligence (AI) would be at the center, followed by machine learning and finally deep learning, all of which would overlap. To put it another way, artificial intelligence (AI) is not the same thing as deep learning.

Let’s compare ML/DL companies

Other Distinctive Features of Deep Learning versus Machine Learning

Without being explicitly programmed, Machine Learning allows computers to learn from data using algorithms to complete a task. Deep Learning employs an intricate network of algorithms meant to mimic the human brain. Unstructured data may now be processed, including documents, photos, and text.

Read: What Is Augmented Reality?

As we saw, deep learning is a special case of machine learning, and both are branches of AI. Deep learning is often equated with traditional machine learning. Although they are connected, there are some distinctions between the two.

Let’s talk it over!

• A specific type of machine learning is known as “deep learning”. The field of artificial intelligence deals with machine learning.
• When it comes to drawing judgments and conducting analyses, deep learning algorithms rely on their neural networks.
• While models trained using machine learning can improve their performance on certain tasks, they still need human supervision.
• ML can train on smaller data sets, while DL requires large amounts of data.
• ML requires more human intervention to correct and learn, while DL learns on its own from the environment and past mistakes.
• Since deep learning attempts to mimic the functioning of the human brain, the ANN’s structure is far more intricate and interwoven.
• Simpler structures, such as decision trees or linear regression, are used in machine learning algorithms. Since deep learning attempts to mimic the functioning of the human brain, the ANN’s structure is far more intricate and interwoven.
• For difficult issues that require extensive data, machine learning is not as effective.
• ML makes simple, linear correlations while DL makes non-linear, complex correlations.
• Artificial neural networks are the backbone of deep learning systems. Structured data is a prerequisite for most machine learning algorithms.

Nutshell

Machine learning is often confused with deep learning, and vice versa.

Both deep learning and supervised learning are closely related subfields in artificial intelligence. If there is one thing we hope you take away from this piece, it’s that deep learning is a subset of machine learning. The purpose of machine learning is to train computers to increasingly function with minimal human input. Optimizing computers’ cognitive and behavioral processes in ways that mimic the human brain is the focus of deep learning. Spending more time understanding m

Machine learning and deep learning will set you apart from the competition.

New opportunities for machine advancement arise as AI continues to improve. Both Deep Learning and Machine Learning fall under the umbrella term “Artificial Intelligence,” yet they are distinct fields in and of themselves. Machine Learning and Deep Learning are both specialized algorithms that can complete a range of different jobs, each with its own set of benefits. While deep learning doesn’t require much assistance thanks to its basic emulation of human brain workflow and understanding of the context, machine learning algorithms still require some human assistance to analyze and learn from the provided data and arrive at a final decision.

Read the Latest blog from us: AI And Cloud- The Perfect Match

The post 21 Key Differences Of Deep Learning vs Machine Learning appeared first on AiThority.

What Is an Intelligent Virtual Assistant (IVA)?

By 2025, the virtual assistant market size is expected to grow to $25.63 billion.

An intelligent virtual assistant (IVA) is a piece of conversational software that is driven by AI that employs analytics and machine learning to have natural-sounding conversations with users to aid them in locating information, performing an action, or finishing a job. Information gleaned from databases, client histories, connected apps, and prior contacts is used by IVAs to tailor their chats to each user.

Using natural language understanding (NLU), the system can have more nuanced conversations with consumers via digital and audio channels, answering more questions and fulfilling more requests than a chatbot could. IVAs can speak with users in a variety of languages and translate what they say.

The Intelligent Virtual Assistant (IVA) is a chatbot powered by AI that can provide customized replies to each user based on their profile data, prior interactions, and geographic location, all while drawing on the company’s knowledge base and human expertise.
The Intelligence Assistant works as part of the Intelligence Unit’s tactical and administrative intelligence capabilities to effectively identify issues and risks across all aspects of local trading standards operations.

The post How AI Is Propelling the Intelligent Virtual Assistants Into a New Epoch? appeared first on AiThority.

AI – A Bird’s Overview

The term “artificial intelligence” (AI) is used to describe a wide range of computer programs that attempt to simulate human intelligence to solve complicated problems and improve over time.

To solve difficulties, AI software can mimic human brain activity. The end objective is to create a smart machine that can handle difficult workloads. There is a vast potential market for AI. To simulate human judgment, AI integrates many technologies into a system. Artificial intelligence can process any kind of data, including those that are only partially organized.
To learn, reason, and self-correct, AI systems make use of logic and decision trees.

Read: What Is Augmented Reality?

ML – A Bird’s Overview

Machine learning falls outside of the realm of artificial intelligence, which is concerned with creating machines that are capable of human-level cognitive function. The goal of machine learning is to train a computer to carry out a task by itself, producing reliable results by recognizing patterns. For example, you may ask your Google Nest, “How long is my commute today?”

This graph, sourced from Deloitte, illustrates how important data transformation is for ML. The proper format of data is essential for the complete deployment of ML. Machine learning is only useful with massive volumes of data, which are tedious to gather, organize, and keep. Most people who took the survey think that developing models, transforming data, and managing and monitoring models are the most labor-intensive parts of artificial intelligence.

You may ask a machine how long it will take to go to work, and it will give you an estimate. The end aim here is for the gadget to do something useful for you, something you may have to do manually in the real world.

ML’s inclusion in the larger system is not intended to improve its functionality in this scenario. Foreseeing traffic volume and density, for instance, may require training algorithms to monitor real-time transit and traffic data. However, this analysis is restricted to learning from the data to achieve optimal performance on the targeted task and discovering trends based on how accurate the prediction was.

You may think of ML as a subset of both AI and Data Science. Siri from Apple, Google Assistant, Tesla self-driving vehicles, Amazon Alexa, etc. are all good instances of artificial intelligence. Google’s search engine, Twitter’s sentiment analysis, stock prediction, news classification, etc., are all excellent instances of machine learning in action.

The range of uses for machine learning is somewhat small. Models for future outcomes are generated by ML’s self-learning algorithms. Structured and semi-structured data are required for ML. Machine learning (ML) systems use statistical models for learning and correction and may improve themselves with additional data.

Read the Latest blog from us: AI And Cloud- The Perfect Match

The post Top 21 Differences Between AI And ML appeared first on AiThority.

Virtual reality (VR) and augmented reality (AR) are two distinct but related computing paradigms. Compared to virtual reality, augmented reality occurs in the real world. Augmented reality users have command over their physical presence, whereas virtual reality users are at the mercy of technology.

As of 2023, there are 65.9 million VR users and 110.1 million AR users in the U.S.

Virtual reality and augmented reality are frequently used interchangeably. AR apps, games like Pokemon Go, and VR headgear, like Meta Quest 2 and the Valve Index, are becoming increasingly common. They have a similar ring to them, and as technology advances, they blend in many ways. This is obvious in Apple’s forthcoming Vision Pro headset (release date: early 2024).

However, virtual reality (VR) and augmented reality (AR) are still not the same thing.

What Is AR?

Augmented reality is more effective than virtual reality as a branding and gaming tool since it is accessible to virtually anybody with a smartphone. The camera or video viewer on a smartphone is used to project digital images and characters onto the real world, transforming it into a vibrant, surreal environment. Simply said, augmented reality enhances the user’s current surroundings. To fully use AR, you need to use certain gadgets. For instance, Smart Glasses are often used to get the info through Smart Glasses software.

What Is VR?

The global virtual reality (VR) is expected to grow at a CAGR of 27.5% from 2023 to 2030

The next level of these components is achieved in virtual reality by creating a computer-generated representation of a different universe. Using technology like computers, sensors, headphones, and gloves, these simulations can transport the player to practically any setting they can imagine.

Virtual reality is all about making a new world seem real. With a VR screen, the person can see and communicate with the digital world. Two lenses must be put between the person and the screen for this to work. They figure out what the eye movements mean and change how the person moves to fit the VR. So, in this case, a lot of gear is needed to cut the person off from the real world.

Read the Latest blog from us: Risks Of IT Integration

Jobs in the VR and AR Industry

It has created innumerable opportunities for businesses and workers. Virtual reality and augmented reality are revolutionizing several fields, including computer programming, graphic design, scientific study, and more.

A bachelor’s degree in computer science might help you learn how the technology works. People who work with AR and VR must know how to use tools like 3D MAX and Autodesk 3D and be familiar with video game creation systems like Unreal and Unity that recreate environments in 3D.

Read: What Is Augmented Reality?

Augmented or Virtual Reality? A direct comparison!

• The two technologies—AR and VR—differ significantly. This striking contrast does not, however, suggest that one technology is superior to the other. Instead, each technology excels in a distinct field of use:
• While VR transports users to an imaginary realm, AR adds digital details to the actual world.
• Virtual reality is 75% virtual whereas augmented reality is just 25% virtual.
• Virtual reality headsets are necessary for use, although augmented reality devices are not.
• Virtual reality (VR) users move in an entirely fabricated environment, whereas augmented reality (AR) users interact with the actual world.
• More bandwidth is needed for AR than VR.
• The goal of augmented reality is to make both the digital and physical worlds better. Virtual reality (VR) is a technology that simulates an alternative environment to improve video games.
• Both AR and VR employ a real-world environment, whereas VR is entirely simulated.
• Users of augmented reality have command over their physical presence, whereas those of virtual reality are at the mercy of the technology.
• While virtual reality headsets are required to experience AR, all you need is a smartphone.
• While virtual reality can only improve upon an artificial reality, augmented reality can improve upon both.

Read the Latest blog from us: AI And Cloud- The Perfect Match

The Synergy Between Augmented and Virtual Reality

When augmented reality and virtual reality are used together, they form a harmonious system. They each function independently, but when used together, they provide a richer, more exciting experience for the user. The idea is to build a fictitious setting that is open to outside influences.TeamViewer provides excellent software options for integrating AR and VR into your business.

Future-Oriented Thinking

As mixed reality becomes more prevalent among VR headsets, the border between augmented and virtual reality will blur temporarily if not permanently. Over time, advancements in outward-facing camera and environment-scanning technologies will allow you to see more clearly while wearing a headset, transforming any virtual reality headset into a mixed reality headset.

Read: AI and Machine Learning Are Changing Business Forever

The post Augmented Reality Or Virtual Reality- Which Is Better? appeared first on AiThority.

Data Is Getting The Fuel that Powers Your AI Journey

Watching Netflix, searching Google, booking an Uber, shutting off the lights with Alexa, unlocking your phone, and even finding the appropriate shade of cosmetics – these are just a few instances of how we engage with artificial intelligence and machine learning (AI/ML) every single day.

60% of all business data is kept in the cloud. The cloud storage is estimated to exceed 100 zettabytes. 54.62% of consumers utilize 3 distinct cloud storage providers. Google Drive has nearly 1 billion users, whereas Dropbox has over 700 million claimed users.

AI and ML boil down to recognizing patterns. Real-time pattern recognition offers vast potential to enhance operational efficiency, company results, and individual lives. IDC projects that the worldwide AI industry, comprising software, hardware, and services, will approach the $900 billion mark in 2026, with a compound annual growth rate (CAGR) of 18.6 percent in the 2022-2026 timeframe.

New: 10 AI ML In Personal Healthcare Trends To Look Out For In 2024

Artificial intelligence (AI) allows machines to mimic human thought processes. Companies across industries are racing to harness this quality to advance their products and services and remain competitive. Any failing business may be saved via the strategic use of AI and the exploitation of the insights it provides.

The data is the engine that drives AI. The danger is that it will become stuck or preserved in a state that makes it difficult or expensive to use, maintain, or expand.

Artificial intelligence is limited by the quality of the data it is fed. Companies need to know the full scope of their data production, the value of that data, how to eliminate unwanted data, and how long that data will persist. Organizations must also have the means to govern, catalog, optimize, and audit their data for compliance purposes. Each of these is a major roadblock.

Read: Top 15 AI Trends In 5G Technology

By 2028, the global market for AI-powered storage is projected to rise to $66.5 billion, expanding at a compound annual growth rate (CAGR) of 24.5% between 2018 and 2028.

This is when having access to an AI-powered storage solution comes in handy. AI-enabled storage optimizes data and conducts other clever automated functions without requiring human input, providing continuous real-time updates from a wide range of business data sources.

Read:10 AI In Energy Management Trends To Look Out For In 2024

Companies Profiled

• IBM Corporation
• Microsoft Corporation
• Amazon Web Services, Inc. (Amazon.com, Inc.)
• Google LLC (Alphabet Inc.)
• Cisco Systems, Inc.
• Hewlett-Packard Enterprise Company
• Intel Corporation
• Dell Technologies, Inc.
• Lenovo Group Limited
• NetApp, Inc.

Expert Insights

Exclusively by Reggie Jerath, CEO of Hydro.

We anticipate unprecedented strides in natural language processing in 2024, allowing for more innate interactions with technology.  Real-time decision-making will be enabled by the convergence of edge computing and AI, revolutionizing entire industries.  Explainable AI (XAI) will become more popular, improving AI system transparency and confidence.

By pushing the limits of computational efficiency, quantum machine learning will open up new avenues. AI’s ethical implications will become more prominent, necessitating strict rules and guidelines.

In short, 2024 will be a turning point in the evolution of AI, paving the way for a day when smart technologies are a seamless part of our everyday lives.

How does AI-based Storage Management work?  

Artificial intelligence (AI) and machine learning (ML) applications require data to function properly. Training simulations and models with massive quantities of data helps in decision-making and enhances the accuracy and utility of any insights.

To function, AI systems need not just massive volumes of data, but also access to that data via fast, reliable, and scalable storage choices. As a result, data storage especially intended for machine learning and AI applications was invented.

For such storage to be an efficient part of AI workloads, it must include a few essential components:

• Quick connection to the outermost region of the cloud:

Artificial intelligence relies on a complex cloud architecture. Everything from files to programs to data-gathering mechanisms may be hosted remotely on the cloud. AI storage will allow for instantaneous access to data throughout the whole cloud, regardless of where the data now resides or is headed.

• Automation:

A variety of critical operations, including the dynamic management of file and resource rights, identity and access control configurations, routing and load balancing strategies, and processing directives, will need to be carried out by storage allocated for AI workloads. AI storage will automate these processes to boost the system’s efficiency. It would make sense for AI to be the driving force behind this automation.

• Artificial intelligence (AI) storage requires scalability and performance

Period. Storage for AI workloads must be flexible enough to meet shifting needs without slowing them down. Although adequate storage might alleviate scaling or performance issues, in these systems, storage often becomes the limiting factor.

• Complexity

Due to the complexity and scope of AI applications, mismanagement of resources can lead to spiraling costs. AI storage should provide some degree of cost-effectiveness, either through bulk storage management or adequate scalability, to prevent acquiring and maintaining resources that aren’t necessary.

AI storage relies on several factors, not the least of which are effective storage management, cost management, and scalability.

Read Top 20 Uses of Artificial Intelligence In Cloud Computing For 2024

10 AI ML in Data Storage Trends to look out for in 2024

 AI and ML are increasingly integrated into data storage and management solutions to optimize data processing, enhance efficiency, and improve data security. While it’s challenging to predict specific trends for 2024, here are some areas in AI and ML within data storage to watch for: 

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

1. Intelligent Data Tiering: AI and ML will be used to automatically classify and tier data based on its value and access patterns. This ensures that frequently used data is stored in high-performance storage tiers, while less frequently accessed data is moved to cost-effective, lower-tier storage. 

2. Predictive Storage Analytics: AI-driven analytics will offer predictive insights into storage capacity, performance, and potential issues, enabling proactive maintenance and optimization. In an enterprise data center, predictive storage analytics uses machine learning to analyze historical performance data, forecast future storage needs, and automate alerts for potential issues. This enables proactive resource allocation, optimizing storage efficiency, reducing downtime, and achieving cost savings through informed capacity planning. 

3. Data Compression and deduplication: ML algorithms will be used to improve data compression and deduplication techniques, reducing storage costs and optimizing data transfer speeds. Compressing data further eliminates unnecessary information inside each data block, whereas deduplication eliminates superfluous data blocks. When combined, these methods drastically cut down on data storage space needs. Want to know about the two main ways that data may be compressed? Both lossy and lossless compression techniques exist. By erasing some of the original material permanently, lossy compressed files. By deleting extraneous information, lossless compression makes files smaller. IS GETTING

4. Smart Data Archiving: AI systems will assist in identifying which data should be archived, when it should be archived, and where it should be stored, improving long-term data management and retrieval. The acronym “AIOps” (artificial intelligence for IT operations) is what Gartner is calling this. From a low of less than 10% in 2020, Gartner projects that by 2025, 40% of all infrastructure product installations, including storage and hyper-converged systems, will be AIOps-enabled. By analyzing capacity and performance in advance, forecasting difficulties that can interrupt data services, and offering practical suggestions for fixing Level 1 concerns, the new tools improve storage utilization efficiency.

5. Data Security and Privacy: AI and ML will enhance data security by identifying and mitigating security threats and ensuring compliance with data privacy regulations. Anomaly detection will play a significant role in identifying unauthorized access or data breaches. The dramatic growth in product capabilities and pent-up demand for more stringent data security measures will cause 30% of organizations to have used bDSP by 2025, up from 10% in 2021.

6. Data Governance: AI will assist in creating and enforcing data governance policies, ensuring that data is appropriately classified, tagged, and handled throughout its lifecycle. Potentially supported areas for expansion include capacity-based pricing, more granular control of the many types of flash storage available, and increased support for hybrid settings. You can already factor in data egress and influx costs using tools like Virtana and vSAN.

7. Storage Resource Allocation: ML algorithms will continuously monitor and adjust storage resources, ensuring that data storage is optimally allocated based on evolving usage patterns. Information on the storage infrastructure’s availability, capacity, and performance, as well as management of devices, problem identification, configuration planning, and change management, is provided by storage resource management (SRM) software in near real-time and historical form. With the data provided by SRM software, applications, business units, or users may monitor storage use, availability, and performance. This information can then be utilized for IT consumption tracking and chargeback in both homogeneous and heterogeneous contexts.

8. Data Recovery and Backup: AI and ML will improve data recovery processes by identifying critical data and enabling faster, more efficient backups and restoration. Data recovery in storage is evolving with trends like cloud-based solutions for scalability, AI-powered automation for faster and more accurate recovery, and emphasis on ransomware protection. Immutable storage prevents data loss, while hybrid and multi-cloud recovery ensures flexibility. Faster recovery times, endpoint protection, and compliance integration further enhance the efficiency and reliability of data recovery solutions in today’s dynamic storage environments.

9. Storage Optimization for Edge Computing: With the growth of edge computing, AI and ML will play a role in optimizing data storage at edge locations, ensuring efficient use of limited resources. In edge computing, storage optimization is critical for efficient data processing. By employing predictive analytics, edge devices can anticipate local storage demands, ensuring timely and relevant data access. This minimizes latency, optimizes resource usage, and enhances overall system performance. Automated algorithms adjust storage dynamically, allowing edge environments to adapt to varying workloads, improving responsiveness, and enabling more streamlined and effective edge computing operations.

Cognitive search and content management transform data storage by employing AI-driven insights. Using natural language processing and machine learning, these systems enhance search accuracy, extracting meaningful information from unstructured data. They automate content categorization, metadata tagging, and intelligent indexing, enabling streamlined access to relevant information. This improves data discovery, organization, and retrieval, empowering organizations to harness the full potential of their stored data.

Organizations need to monitor these trends and adopt AI and ML solutions that align with their data storage needs. As data continues to grow in volume and complexity, leveraging these technologies is essential to maintain efficient and secure data management practices.

Read the Latest blog from us: AI And Cloud- The Perfect Match

If you employ storage analytics, how can AI change that?

• Automating the process of spotting and fixing possible hardware problems and compliance concerns can help you save time and boost performance.
• Accurately forecast future needs by analyzing data-generation rates in the present and the past, therefore decreasing operational costs and freeing up IT resources for more creative, strategic endeavors.
• Data migration to different on-premises or cloud-based storage tiers may be automated from day one with the help of predictive storage analytics.

Read: IS GETTINGThe Top AiThority Articles Of 2023

Future of Data Storage with AI

The ability of human engineers to manage, monitor, and maintain such massive data storage may be jeopardized in the future as AI increases the size of storage systems. Artificial intelligence-enabled data storage solves this scalability and efficiency problem. While today’s storage systems and apps can collect massive amounts of data and turn it into useful insights, progress is hampered without the intelligence and automation of AI-powered storage solutions.

Technology advancements in artificial intelligence and machine learning promise to have far-reaching effects on businesses. Edge artificial intelligence, decision intelligence, and deep learning are just a few examples of the technologies that the Gartner hype cycle predicts will see widespread use over the next two to five years. The ability of businesses to fully realize the promise of AI/ML applications will be greatly influenced by the storage infrastructure they decide to use as they begin their separate journeys to deploy this formidable new method.

Organizations can improve their decision-making and can automate routine daily tasks and choices. Workers now have more time to devote to “human” pursuits like creativity, teamwork, and interpersonal interaction. To provide unique experiences for customers and employees, and to allow the development of cutting-edge new applications that aren’t readily available today, a data-driven culture is essential. 

Read More: OpenAI Open-Source ASR Model Launched- Whisper 3

The post 10 AI ML In Data Storage Trends To Look Out For In 2024 appeared first on AiThority.

Remember? Last year, through a concerted effort by Microsoft, ESET, Black Lotus Labs, Palo Alto Networks, and HealthISAC, the ZLoader botnet was shut down and its malware stopped from spreading.

Remember? Through a new partnership with IBM Security, ASUS is now able to offer their enterprise customers the QRadar Endpoint Detection and Response (EDR) solution, giving their customers access to these capabilities.

AI-based cybersecurity has several advantages, such as improved visibility that reveals security weaknesses and enhanced identity management that prevents unauthorized individuals from accessing sensitive data and networks.

AI-driven cybersecurity solutions save time and improve the accuracy of risk assessments because of their robust analytic capabilities. They also help IT and security teams save time and effort by automating the detection and response to threats, providing them with the information they need to respond swiftly and precisely to cyber assaults.

Read 10 AI In Manufacturing Trends To Look Out For In 2024

So, What Exactly Is Artificial Intelligence Safety?

71.1 million people fall victim to cyber crimes yearly.

Cybersecurity is only one field that has benefited greatly from the advent of AI. AI security solutions have developed as strong tools for recognizing and mitigating possible dangers in today’s digital ecosystem.

AI can analyze massive volumes of data, spot suspicious activities, and better defend businesses from cyberattacks by employing machine learning algorithms and deep learning techniques. In this post, we will examine what artificial intelligence security is, its most prevalent uses, the benefits it provides, and the most important factors to keep in mind when comparing AI cybersecurity providers.

Advanced AI cybersecurity solutions can calculate and analyze enormous datasets, which allows them to discover activity patterns that may signal hostile action. In this way, AI simulates the ability of its human counterparts to spot potential dangers. Automation, triage, collecting alerts, sifting through alerts, automating reactions, and more are all possible thanks to AI’s use in cybersecurity. AI is commonly used to supplement the initial level of analyst work.

Read: Top 15 AI Trends In 5G Technology

Let’s Hear From an Industry Expert:

Use of AI by Threat Actors will Drive Media Stories. Still, Not Actual Threat Actors: The use of AI by cyber criminals to enhance their attacks will continue to inspire authors more than those conducting cyber attacks for one reason: bad guys don’t need to change what they’re doing one iota to achieve success. Until the cyber security community can stop the primary attack vectors bad actors used in 2023, 2022, 2021, etc., to increase ransomware payouts and incidences of successful breaches continually, cybercriminals have no incentive to leverage AI or any other new technology for that matter, to achieve their objectives. Necessity may be the mother of invention, but where’s the evidence of necessity for bad actors?

Gabriella Bussien  CEO of tech-first financial crime prevention organization Trapets, a Nordic market leader since 2000, has some insights.

Top Companies in This Domain

• Crowdstrike
• Cybereason
• SparkCognition
• Tessian
• Palo Alto Networks
• Check Point Software Technologies
• Darktrace
• Fortinet
• Anomali
• Vectra AI

New: 10 AI ML In Personal Healthcare Trends To Look Out For In 2024

Let’s Understand Some Numbers

The average cost of a data breach to small businesses can range from $120,000 to $1.24 million.

Before getting into the precise sorts of cyber assaults, you need to understand how much data is involved. In 2025, the total amount of information created by humans will be 175 zettabytes (175 followed by 21 zeros). Streaming media, d***** applications, and medical records are all examples of this type of data. Securing this information is crucial.

Attacks on the supply chain have become a major issue in cybersecurity during the past few years. Cyber catastrophes, such as the breach at software management provider SolarWinds and Log4j in the open source realm, put enterprises throughout the globe at risk. By 2025, 45% of global enterprises, according to Gartner, will be affected by a supply chain assault.

As technical attacks grow increasingly hard, attackers are targeting human nature. The substantial advancements in cyber protection are sometimes overshadowed by the attention paid to cybersecurity accidents.

Read: 10 AI In Energy Management Trends To Look Out For In 2024

This is because hackers are continually looking for new methods to get beyond defenses, while security experts are always adapting to new forms of cyber assault. While it’s important to acknowledge where attackers succeed, it’s also important to acknowledge where defenders succeed in making things more difficult for attackers.

The global annual cost of cybercrime is estimated to be $6 trillion per year.

For instance, new cloud computing platforms have dramatically improved cyber threat detection thanks to the size and performance of their providers. This results in innovative breakthroughs in neural machine learning and analytics. In the poll Voice of the Enterprise: Information Security, Technology Roadmap 2022 conducted by 451 Research, respondents said they intended to “significantly increase” their expenditure on cyber security analytics. These advancements pave the way for more convenient methods of authentication for users of digital resources, such as facial recognition and other biometric processes.

Read 50 Key Points Of The Gartner Symposium, Spain 2023

Threats Are Changing

AI has several advantages and uses in many technology areas, including cybersecurity and data ops. AI and ML tools have become the quintessential ammunitions to track and take down cyber criminals and hackers, automate threat detection, and respond more efficiently than with traditional software-driven or manual methods.

Cybercrime up 600% Due to COVID-19 Pandemic

Artificial intelligence (AI) and machine learning (ML) are becoming increasingly valuable in IT security, assisting businesses in safeguarding their systems and data against advanced internet attacks. Predicting particular trends for 2024 is challenging, but here are some areas to keep an eye on in AI and ML within IT security: ML will be used to continually monitor and evaluate user behavior, devices, and network traffic for any deviations from the norm, as Zero Trust models continue to gain popularity.

Read: The Top AiThority Articles Of 2023

10 AI ML In IT Security Trends To Look Out For In 2024

This graphic image has been taken from IBM for the year 2017-2023.

Zero Trust Security with ML: Zero Trust models will continue to gain traction, with ML being used to continuously monitor and analyze user behavior, devices, and network traffic for any deviations from the norm.

Let’s hear from Jasson Casey, Chief Executive Officer at Beyond Identity:

There is a high degree of urgency around zero trust today, especially when it comes to the first two pillars of the ZTMM – identity and devices. Research shows reusing stolen credentials and phishing remain the primary ways attackers access an organization to deploy ransomware, steal data, and access customer accounts. An increasing number of hackers are exploiting passwords and bypassing weak “legacy” multi-factor authentication (MFA), so strengthening an organization’s identity and device pillars right off the bat can make a substantial risk reduction difference. 

AI-Enhanced Threat Detection: AI-driven threat detection will become more sophisticated, with systems capable of identifying even highly evasive and previously unknown threats by analyzing patterns and anomalies in network traffic and system behavior. Ransomware is an increasing menace despite being around for 20 years. Hackers have gotten skilled at disguising dangerous code, and there are over 120 ransomware families. Hackers can easily profit from ransomware, which explains its ascent. Another element was COVID-19. Many firms’ rapid digitalization and remote working made them ransomware targets. The number of attacks and demands rose.

Security Automation and Orchestration: ML-driven automation will become a fundamental component of incident response, helping security teams rapidly analyze and respond to security incidents. Security automation and orchestration automate cybersecurity processes. Security procedures may be simplified and streamlined using automation. Security orchestration creates a process from security tools. Security automation simplifies and improves security operations by handling different types of routine tasks, while security orchestration integrates all of your security tools into a fast, efficient workflow.

Predictive Threat Intelligence: ML will enhance threat intelligence by predicting potential threats based on historical data and global threat trends, allowing organizations to proactively defend against emerging cyberthreats. Cruise control, engine timing, door locks, airbags, and sophisticated driver support systems are connected by automatic software in modern automobiles. Bluetooth and WiFi allow these cars to interact, making them vulnerable to hackers. More autonomous cars are predicted to increase vehicle control and eavesdropping microphone use in 2023. Autonomous cars have a more complicated process that demands strong cybersecurity.

Cloud Security: AI and ML will continue to be deployed in cloud security solutions to monitor cloud infrastructure, detect misconfigurations, and defend against cloud-specific threats. Cloud vulnerability is a major cyber security trend. After the pandemic, remote working became common, increasing the need for cloud-based services and infrastructure and posing security risks to enterprises. Cloud services are scalable, efficient, and cost-effective. They are also key targets for attackers. Cloud misconfigurations cause data breaches, illegal access, insecure interfaces, and account hijacking. Organizations must reduce cloud dangers since data breaches cost $3.86 million on an average.

Deepfake Detection: With the rise of deepfake technology, AI and ML will play a critical role in detecting and mitigating the risks associated with deepfake content, particularly in sensitive areas like video authentication and voice recognition. Extended Detection and Response (XDR) is a vendor-specific SaaS threat detection and incident response technology that natively connects several security products. This modern cyber security plan usually includes prevention, detection, response, and recovery for new cyber threats. XDR models reduce reaction times to spot risks, automate forensic investigation, and duplicate data online for faster access. XDR bundles cyber security products utilizing a SaaS to gather and correlate advanced threat data, evaluate, prioritize, hunt, and remediate attacks to avoid security breaches. The targeted targeting and prevention of breaches by XDR reduces threat reaction time and offers unified insight across numerous attack channels.

Explainable AI (XAI): Explainable artificial intelligence (XAI) helps humans understand and trust machine learning algorithm output. Explainable AI describes AI models, their effects, and biases. As AI models are increasingly integrated into security, there will be a growing need for transparency and explainability, ensuring that security professionals can understand why a particular decision was made by an AI system.

Adaptive Access Control: ML algorithms will be used to dynamically adjust access control policies, granting or denying access based on real-time risk assessments. According to IBM’s annual Cost of a Data Breach Report, breaches cost more and have more effect than before. All sectors’ average data breach cost is $4.3 million, up 13% in two years. The healthcare business has had the highest breach cost for 12 years, at a little over $10 million.

AI-Enhanced Security Awareness Training: ML algorithms will help organizations tailor security awareness training for employees, identifying areas of weakness and delivering personalized training to improve overall cybersecurity hygiene.

These trends reflect the growing importance of AI and ML in IT security, as the threat landscape continues to evolve. Organizations must stay updated on these trends and invest in the latest security technologies to protect their systems and data effectively. Additionally, compliance with relevant regulations and industry best practices should remain a top priority in IT security.

Behavior-Based Authentication: ML will be used for continuous user authentication based on behavior patterns, making it more challenging for attackers to gain unauthorized access. We all know that insecure passwords may let fraudsters access bank accounts, credit cards, and personal websites. This lets them steal your money, personal data, and digital security. Okta, an identity and access management business, reports that over 55% of organizations utilize MFA for security, rising annually. Look for more customers to use multi-factor authentication (MFA) in 2023 to make account hacking twice as difficult.

Read Top 20 Uses of Artificial Intelligence In Cloud Computing For 2024

Conclusion

The benefits of AI in cybersecurity outlined above are only the tip of the iceberg. The use of AI in this area is promising, but it is not without its drawbacks. Companies should devote far more time, energy, and money to developing and maintaining an AI system. In addition, you’ll need to collect a large volume of unique sets of malware codes, non-malicious codes, and abnormalities because AI systems are educated by utilizing data sets. Acquiring these data sets is time-intensive and demands expenses that most firms cannot afford.

Artificial intelligence systems can make mistakes or produce false positives if they don’t have access to massive amounts of data and events. Inaccurate information from questionable sources might have unintended consequences. The third issue is that there is a significant drawback in that fraudsters may utilize AI to evaluate their virus and conduct more sophisticated attacks. Artificial intelligence is quickly becoming an essential tool for improving the efficiency of IT security teams. It is beyond human capabilities to pose as an omnipresent doorman against newer cyberattacks, such as ransomware and social engineering. Human admins can’t act and respond against enterprise-level surface attacks without AI and predictive monitoring tools. The new-age AI for IT platforms delivers granular-level risk analysis and threat detection to empower security professionals. Together, human intelligence augmented by AI can decrease data risk with an improved security posture at all times.

Read OpenAI Open-Source ASR Model Launched- Whisper 3

The post 10 AI ML In IT Security Trends To Look Out For In 2024 appeared first on AiThority.

Recognizing the imperative to harness the immense potential of Artificial Intelligence (AI) for both social and economic advancement while effectively mitigating associated risks, nasscom has released early insights on the state of Responsible AI (RAI) in India. Based on analyses of the data collected via a survey of 500+ senior executives from across large enterprises, SMEs and startups engaged in the commercial development and/or use of AI in India, the report highlights beliefs and perceptions of the tech industry about its key strengths and critical areas for improvement when it comes to compliance with the benchmarks for RAI adoption.

Recommended AI News: Riding on the Generative AI Hype, CDP Needs a New Definition in 2024

The escalating need for RAI among AI users and stakeholders is compelling industry leaders to invest in advanced RAI tools and strategies while emphasizing transparency in their AI practices. As businesses scale up AI maturity, they also tend to report higher RAI maturity. 60% of the surveyed businesses reported having either matured RAI practices and policies or having initiated formal steps towards RAI adoption. 30% reported having basic awareness of RAI imperatives without a formal strategy or framework. Developers are almost two times more likely than users to report higher levels of RAI maturity.

Large enterprises (with annual revenue > 250 crores) are 2.3 times more likely than start-ups and 1.5 times more likely than SMEs to report matured RAI practices. Moreover, a majority of businesses operating across key industries are progressing to achieve satisfactory levels of RAI maturity. Around two-third of businesses in sectors such as BFSI, TMT and Healthcare reported having either matured RAI practices or having initiated formal steps towards RAI adoption.

Workforce development remains central for businesses to ensure robust RAI implementation. As per the survey findings, 89% of businesses that reported matured RAI practices and policies also reported commitments to continue investments in workforce sensitisation and training for RAI compliance. While over 60% of businesses that reported lower levels of RAI maturity also reported commitments to improve compliance through investments in workforce sensitisation and training.

Recognizing the imperative to harness the immense potential of Artificial Intelligence (AI) for both social and economic advancement while effectively mitigating associated risks, nasscom has today released early insights on the state of Responsible AI (RAI) in India. Based on analyses of the data collected via a survey of 500+ senior executives from across large enterprises, SMEs and startups engaged in the commercial development and/or use of AI in India, the report highlights beliefs and perceptions of the tech industry about its key strengths and critical areas for improvement when it comes to compliance with the benchmarks for RAI adoption.

Recommended AI News: Innovation of Audio: Openrock X by Oneodio Unveiled at CES 2024

The escalating need for RAI among AI users and stakeholders is compelling industry leaders to invest in advanced RAI tools and strategies while emphasizing transparency in their AI practices. As businesses scale up AI maturity, they also tend to report higher RAI maturity. 60% of the surveyed businesses reported having either matured RAI practices and policies or having initiated formal steps towards RAI adoption. 30% reported having basic awareness of RAI imperatives without a formal strategy or framework. Developers are almost two times more likely than users to report higher levels of RAI maturity.

Large enterprises (with annual revenue > 250 crores) are 2.3 times more likely than start-ups and 1.5 times more likely than SMEs to report matured RAI practices. Moreover, a majority of businesses operating across key industries are progressing to achieve satisfactory levels of RAI maturity. Around two-third of businesses in sectors such as BFSI, TMT and Healthcare reported having either matured RAI practices or having initiated formal steps towards RAI adoption.

Workforce development remains central for businesses to ensure robust RAI implementation. As per the survey findings, 89% of businesses that reported matured RAI practices and policies also reported commitments to continue investments in workforce sensitisation and training for RAI compliance. While over 60% of businesses that reported lower levels of RAI maturity also reported commitments to improve compliance through investments in workforce sensitisation and training.

A****** AI PC Lenovo ThinkBook Laptops and ThinkCentre neo Desktops Inspire a New Wave of Productive and Creative Power

ecommended AI News: Anviz to Launch AI-Boosted Security Products at Intersec Expo, Dubai

[To share your insights with us, please write to sghosh@martechseries.com]

The post nasscom Releases Early Insights on the State of Responsible AI in India: December 2023 appeared first on AiThority.

LG CEO Redefines AI as ‘Affectionate Intelligence,’ Highlights the Technology’s Key Role in Elevating the Customer Experience and Creating a Better Life for All

LG Electronics (LG) held its LG World Premiere press conference at the Mandalay Bay Convention Center in Las Vegas, Nevada, USA, introducing its new vision and exhibition theme for CES 2024, ‘Reinvent your future.’

CEO William Cho opened the proceedings by restating the company’s ambitious goal – first announced last year – to transform into a Smart Life Solution Company. Having gained a rich understanding of global consumers and their living spaces for close to seven decades, the company is now going beyond the home, expanding its business into diverse spaces including mobility, commercial and virtual. In every arena, the company aims to improve the customer experience by focusing on five key elements dubbed 3C2S: Care, Connectivity, Customization, Servitization and Sustainability.

Recommended AI News: OPENLANE Launches Visual Boost AI to Pinpoint Vehicle Damage

AI Redefined as ‘Affectionate Intelligence’

In its journey to innovate and elevate the customer experience, LG has identified AI as one of the most essential enablers of success. Rather than fixating on the evolution of the technology itself, LG is dedicated to demonstrating how AI can provide tangible benefits in the real world.

The company redefined AI as “Affectionate Intelligence,” revealing a belief that AI can foster a customer experience that is more caring, empathetic and attentive.

Real-Time Life Intelligence

During the press conference, CEO Cho highlighted the unique characteristics of LG’s AI solutions, starting with its capacity to harness a wealth of data, in both scale and quality. Among the 500 to 700 million LG products currently in use worldwide, there are many smart devices equipped with AI-supported intelligent sensors that are optimized to learn and analyze users’ ‘physical’ and ’emotional’ life patterns.

Whereas many companies rely on internet-based data to train their AI, LG has the advantage of being able to draw on ‘real life’ data gathered from billions of connected devices, encompassing LG smart products and a wide range of IoT devices. This dataset can provide valuable insight into customer-device interactions, as well as customers’ environments, behavior patterns and emotional states. This multi-faceted data can also give the company a more complete picture of its customers’ lives at home, enabling the delivery of better and smarter lifestyle solutions.

Orchestrated Intelligence With LG AI Brain

CEO Cho went on to explain the integral role played by the LG AI Brain, a powerful processing engine driven by LG’s large language model. Leveraging the company’s vast repository of user data, the AI Brain forecasts customers’ needs based on user-product interactions and contextual learning, performs advanced reasoning processes and generates optimal solutions through orchestrating the actions of physical devices.

Ultimately, this allows the company to provide intelligent services and experiences for the various spaces in customers’ lives, all delivered in a more intelligent and efficient manner, showcasing the attentiveness embedded in LG’s concept of Affectionate Intelligence.

Commitment to Responsible Intelligence

LG is acutely aware of its responsibility to employ AI in an ethical manner and is dedicated to being accountable for the impacts and consequences of its decisions and actions. The company aims to develop AI systems that benefit all users, promote safe behavior and ensure the security of all collected personal data.

Highlighting this commitment is the company’s robust data security system, LG Shield, designed to protect personal data and information at every stage of the process, from collection and storage to utilization. Dedicated to realizing Responsible Intelligence, LG aims to surpass required industry benchmarks for the implementation of AI.

In his concluding remarks at LG World Premiere, CEO Cho emphasized that LG’s approach to AI is firmly rooted in the belief that customers should have full control. He also added, “Life’s Good. This is our solid and uncompromising promise that motivates us to create a better life for our customers, even in the age of AI.”

Recommended AI News: Riding on the Generative AI Hype, CDP Needs a New Definition in 2024

At the event, CEO Cho was joined on stage by Jung Ki-hyun, vice president and head of LG’s Platform Business Center, and Eun Seok-hyun, president of LG Vehicle component Solutions (VS) Company, who introduced AI-based innovative technologies and strategies.

Starting with the home, vice president Jung presented a blueprint for LG’s AI-based smart home. In line with the vision to transform into a smart life solution company, the company is incorporating LG’s unique ‘Affectionate Intelligence’ technology into the ThinQ platform. Mr. Jung not only unveiled innovative new services, such as ‘ChatThinQ,’ a generative AI chatbot that enables natural conversations with customers, and ‘3D Home View,’ a 3D visualization of the home for integrated control of spaces in an intuitive way, but also revealed plans to launch a new smart home hub.

And, in mobility, the company shared its vision for cars as a ‘living space on wheels’ powered by software-defined vehicle (SDV) solutions. In line with this vision, Eun Seok-hyun, president of LG VS Company, introduced LG AlphaWare, the company’s suite of software solutions for SDV. LG AlphaWare includes versatile software modules to enhance existing vehicle operating systems and to assist in building new platforms; operation solutions to help software developers throughout the SW development process from design to deployment; in-cabin entertainment solutions that enable high definition content viewing and high quality sound; and human-machine interface solutions that utilize AR/MR and AI technologies to provide an immersive in-vehicle experience.

Recommended AI News: WiMi Developed RPSSC Technology With Multiple Advantages in Hyperspectral Image Processing

[To share your insights with us, please write to sghosh@martechseries.com]

The post LG Presents Vision to ‘Reinvent Your Future’ With AI-powered Innovations at LG World Premiere appeared first on AiThority.

Leopard Imaging Inc. (Leopard Imaging), a global leader in intelligent embedded camera design and manufacturing, is going to showcase a Stereo Camera Hawk and a High Speed Camera Owl from Segway Robotics’ Nova Carter robot, powered by the NVIDIA AGX Orin-based AI Box, at CES 2024.

Recommended AI News: OPENLANE Launches Visual Boost AI to Pinpoint Vehicle Damage

Segway Robotics’ Nova Carter is an agile robot designed to proficiently execute tasks including surround perception, 3D mapping, and autonomous navigation. With Leopard Imaging’s Stereo Camera Hawk and High Speed Camera Owl, Nova Carter brings cutting-edge imaging solutions to diverse applications ranging from robotics and autonomous vehicles to healthcare and industrial automation.

At the heart of Leopard Imaging’s showcase is an Autonomous Mobile Robot (AMR) intricately designed to embody the synergy between the Hawk 3D Stereo Camera, Owl High Speed Camera, and Leopard’s NVIDIA Jetson AGX Orin-powered AI Box. This dynamic trio forms the backbone of a cutting-edge robotic system, bringing together state-of-the-art hardware and artificial intelligence to redefine autonomous mobility.

The Hawk 3D Stereo Camera takes center stage as a key component of Leopard Imaging’s innovation. With its depth-sensing capabilities, the Hawk empowers the robot with unparalleled object detection and obstacle avoidance capabilities. This translates to enhanced safety and efficiency, making it a game-changer in real-world applications such as material-handling, retail analytics, healthcare, and beyond.

A distinctive feature of Leopard Imaging’s showcase is the robot with a 360-degree surround view based on Owl cameras. This comprehensive visual perspective, facilitated by 3D cameras strategically placed on the robot, ensures optimal awareness of its surroundings. The result is a robot that can navigate through complex environments with precision and confidence.

Recommended AI News: Riding on the Generative AI Hype, CDP Needs a New Definition in 2024

Leopard Imaging’s AI Box, powered by the NVIDIA Jetson AGX Orin system on module, serves as the brain of the operation, housing a sophisticated artificial intelligence system. This system processes the wealth of data captured by the Hawk 3D Stereo Camera in real time, enabling intelligent decision-making and adaptive responses. The NVIDIA Jetson AGX Orin delivers powerful AI computing capabilities to help the AMR navigate through dynamic environments seamlessly. Leopard is also an Elite member of the NVIDIA Partner Network.

Recommended AI News: Anviz to Launch AI-Boosted Security Products at Intersec Expo, Dubai

[To share your insights with us, please write to sghosh@martechseries.com]

The post Leopard Imaging showcases NVIDIA Isaac Nova Orin-Based Cameras at CES 2024. appeared first on AiThority.

Aqara, a leading provider of smart home products, is thrilled to announce its participation in CES 2024. At the event, Aqara will showcase its latest innovations, including the new Border Router Plug and Smart Lock U300, as well as other advanced smart home solutions and prototypes at its booth in the Venetian Expo (#53513).

Recommended AI News: Innovation of Audio: Openrock X by Oneodio Unveiled at CES 2024

Aqara is an early advocate of the industry-unifying Matter standard and is committed to delivering seamless smart home experiences for users. The Border Router Plug and Smart Lock U300 will be Thread-capable, which will add to Aqara’s growing number of native Matter products, making Aqara devices more interoperable. These two new products previewed at CES are expected to become available for order in the coming months.

Border Router Plug

The Aqara Border Router Plug represents an innovative advancement in smart plug and is among the first smart plugs announced to incorporate Thread Border Router capabilities. Equipped with Thread and dual-band Wi-Fi, this plug enables Matter controllers without Thread capability to manage Thread devices, allowing seamless integration of Thread devices into smart home systems without needing a new Matter controller.

This plug is energy-efficient, particularly in Thread-only mode, which reduces idle consumption while extending the mesh Thread network by routing the data packets. It is also energy-conscious, providing real-time and historical data on home energy consumption, enabling users to automate their electrical devices to reduce energy waste. Additionally, the Border Router Plug can sense the on/off status of the connected appliance and trigger Aqara Home automations accordingly. For example, users can have the curtains close automatically when the TV is on to eliminate potential sun glare.

The Aqara Border Router Plug utilizes an NXP Semiconductors’ secure wireless MCU. As part of the ongoing collaboration between Aqara and NXP Semiconductors, the Border Router Plug will also be featured in NXP’s Smart Home Experience showcase during CES 2024, along with the NXP-powered Aqara Door and Window Sensor P2.

Smart Lock U300

The Aqara Smart Lock U300 is one of the first smart lever locks announced to feature Matter and Thread compatibility, and offers unprecedented interoperability with various smart home platforms. This versatility is coupled with an extended battery life of up to 8 months and enhanced local control, which improves responsiveness, privacy and security.

Designed for both indoor and outdoor use, the U300 replaces the traditional US lever or knob on single-bore doors and is ideal for including side entries, garage entries, home offices, basements and storage rooms. It enables a key-free lifestyle with multiple access options, including fingerprints, PIN codes, NFC, or voice assistants. Homeowners can control the lock remotely, grant temporary access to guests, and receive real-time notifications of who comes and goes – all from a smartphone.

Recommended AI News: Riding on the Generative AI Hype, CDP Needs a New Definition in 2024

Aqara Home App Update

At CES 2024, Aqara will also showcase Home Copilot, the new chatbot interface for the Aqara Home app, which is powered by Gen-AI. Home Copilot is designed to enhance user experiences by utilizing ambient intelligence, which helps convert real-time data and insights into actions. Ultimately, Home Copilot will be able to analyze the usage patterns in an Aqara home and proactively suggest customized automations. It will also understand natural language and configure automations per user requests. Users can automate their homes via simple voice and text instructions, which makes smart home even more intuitive and easy to use.

Initially, Home Copilot will enable Aqara Insights, a daily, weekly and monthly smart home reports for users, and provide tailored plans for energy-saving automation. It will support natural language queries for Aqara devices and automations and offer proactive assistance with device setup and troubleshooting. Home Copilot will also spearhead the AI-powered customer support interface for Aqara users in over 10 languages.

Recommended AI News: Anviz to Launch AI-Boosted Security Products at Intersec Expo, Dubai

[To share your insights with us, please write to sghosh@martechseries.com]

The post Aqara To Unveil New Smart Home Devices at CES 2024 appeared first on AiThority.