PatternAI

patternai

AI’s ability to mimic human behavior, emotional intelligence, creativity, and morality while exploring the future of artificial intelligence Artificial intelligence, especially that afforded by ChatGPT, has changed our manner of dealing with machines. AI now puts us in a state of conversation, learns from us, and even attempts to understand the context behind the questions we ask. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. At the core of AIs like ChatGPT is a neural network, a complex web of algorithms meant to mimic the functioning of the human brain. The algorithms learn about patterns, make predictions, and improve over time through a process known as machine learning. However, there is quite a gap between teaching patterns and being human. But being human isn’t just about patterns or predictions and it is about emotions, experience, understanding of culture, making moral judgments, and the ability for truly original creativity. Interacting with AI today may often generate human-like responses that are interesting and even insightful at times. Yet, what generates those responses — patterns found in large datasets, not quite the sort of personal contemplation or emotive depth carried into every encounter by humans. To answer if ChatGPT can be trained to be human, we must consider what exactly it means for something to be human. For this argument, let’s break it down into several such components: emotional intelligence, creativity, morality, memory, and learning from experiences. Some of these are important facets of human behavior and cognition. Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Emotional Intelligence Emotional intelligence is the ability to sense how others feel and manage one’s own emotions. Our words usually carry emotional intensity from subtle sarcasm to overt anger or happiness. Humans use tone, context, and body language to communicate and interpret emotions. ChatGPT will simulate aspects of emotional intelligence because it can generate text that carries an emotional flavor for a conversation. If you express frustration, then it produces a response that would appear empathetic; if you are ebullient, it can echo some lighthearted repartee. Does this mean it “knows” or understands emotions? No. Instead, what ChatGPT does is recognize patterns in language as they reflect emotion, but it does not feel emotions. It doesn’t have the physiological and psychological mechanisms that underlie human emotion. Training ChatGPT to “be human” in this way would take more than feeding it datasets on emotional expression. It would rather require an architecture that allowed for some authentic experiences of emotions — that is, something beyond the scope of current developments in AI. Even if we can, in some way, simulate emotional reactions in AI, they will still be fundamentally different from human emotions, which are tied closely with consciousness, biology, and experience. Creativity Creativity- the mark of human intelligence: the ability to come up with new ideas, solve problems in innovative ways, and create art that vibrates at a deeply emotional or intellectual level. Is ChatGPT creative? In this respect, yes. ChatGPT can write, for instance, poems, short stories, code- whatever you want it to do. All the same, this creativity is based on the pattern rather than on actual thinking outside of the box that may reflect human creativity. Some of the most creative solutions or artistic expressions are often born out of personal experience, emotional ups and downs, moments of inspiration, those things that cannot easily be expressed and replicated in an algorithm. Although AI can generate creative content, it remains without experiential or emotional depth to inform human creativity. This means it offers content based on learned patterns rather than any personal experience or emotional journey. That is the main point of explaining why, despite impressive outputs, ChatGPT is fundamentally different from a human mind. Morality The other very important element of being human is morality, for which decisions are generally guided by the moral compass of and through upbringing, culture, experiences, and societal norms. More often than not, humans struggle with conflicting values, weigh the consequences of their deeds, and sometimes, change their beliefs over time. Could ChatGPT make moral decisions? Not at all. While it may be able to be programmed to adhere to certain guidelines of morality or avoid harmful content, its understanding of morality is superficial. It does not possess personal values or understand right and wrong in the way humans do. Instead, it responds based on patterns in data that often simply mimic moral reasoning without truly engaging in the complex, human process of ethical decision-making. It is a huge task to train ChatGPT to be human in this regard since even humans are unable to accept one sense of moral principle. Morality also relates to consciousness, which AI lacks. Though AI systems can follow rules, they are not able to understand the sense of moral dilemmas that define human experience. Memory and Experience Humans are a product of their memories and experiences. Every conversation, every relationship, and triumph and every failure leave an impression on who we are and how we approach the world. AI does not have any kind of real memory of past interactions unless it has been explicitly programmed to retain specific data. Each interaction with ChatGPT is unique, and while it can process historical data, it doesn’t “remember” interactions the same way a human does. It would give this form so that ChatGPT can learn through experiences, creating a sense of identity and personal history over time. Now, it does not necessarily imply that it should remember anything but interpret it to affect the subsequent actions; the same way humans do in life, growing and changing according to their past. Current AI models, including ChatGPT, do not have this ability. Though they might learn from big datasets, they lack personal experiences and do not change according to individual interaction. Because of this, for ChatGPT to be closer to “being human,” it requires something more than the capability to store and accumulate information but must also develop the capability to reflect on that same information and to change in meaningful ways relative to past experiences. Learning by Experience Human intelligence is reinforced by the ability to reflect upon mistakes and utilize those as learning experiences. We sometimes reflect on our actions, question our decisions, and make alterations in behavior based on our experiences. ChatGPT, though, can be trained through machine learning models, though it learns very differently compared to humans. Improvement of AI in machine learning is through wide training on various datasets and adjustment of weights and biases for better outcomes. However, the methodology itself is not like that of human learning based on trial and error and is contextual, emotionally and socially tuned, and based on personal experience and interactions with other humans. In contrast, the learning process of ChatGPT is abstract and data-driven. While AI may learn to make improvements in its responses, it is without a human ability to reflect on itself. Human learning isn’t just adding new information, but building that new information off of what we already know and thinking about what the implications are. Sometimes, what we learn is questioned. ChatGPT learns to do this task better at getting statistically likely responses without any understanding or reflection about what it is learning. The Future of AI So, can ChatGPT be made human? Currently, as of the technology, the answer is no. In full, that is. While still able to mimic and even generate human-like conversations, produce creative content, and maintain adherence to all sorts of ethical directives, AI continues to lack depth, consciousness, and emotional complexities of what it means to be human. However, the future of AI is no longer so static. Researchers are more and more pushing the frontiers of what could be done with AI: find new approaches to making machines more intelligent, adaptive, and even emotionally intelligent. Some believe that with domains such as neuro-symbolic AI-machine learning coupled with symbolic reasoning or even quantum computation, we may eventually build one day the essence of human-like cognition in an AI. But yet, all of this raises a basic question: Do we want AI to be human? The development of machines that could think and feel like humans raises profound questions in the realms of ethics and philosophy. Where do we draw that line between the machine and the person if AI becomes too human?
Should AI have rights when it can feel? These are questions that society will have to grapple with as AI continues its evolution. AI’s ability to mimic human behavior, emotional intelligence, creativity, and morality while exploring the future of artificial intelligence Artificial intelligence, especially that afforded by ChatGPT, has changed our manner of dealing with machines. AI now puts us in a state of conversation, learns from us, and even attempts to understand the context behind the questions we ask. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. At the core of AIs like ChatGPT is a neural network, a complex web of algorithms meant to mimic the functioning of the human brain. The algorithms learn about patterns, make predictions, and improve over time through a process known as machine learning. However, there is quite a gap between teaching patterns and being human. But being human isn’t just about patterns or predictions and it is about emotions, experience, understanding of culture, making moral judgments, and the ability for truly original creativity. Interacting with AI today may often generate human-like responses that are interesting and even insightful at times. Yet, what generates those responses — patterns found in large datasets, not quite the sort of personal contemplation or emotive depth carried into every encounter by humans. To answer if ChatGPT can be trained to be human, we must consider what exactly it means for something to be human. For this argument, let’s break it down into several such components: emotional intelligence, creativity, morality, memory, and learning from experiences. Some of these are important facets of human behavior and cognition. Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Emotional Intelligence Emotional intelligence is the ability to sense how others feel and manage one’s own emotions. Our words usually carry emotional intensity from subtle sarcasm to overt anger or happiness. Humans use tone, context, and body language to communicate and interpret emotions. ChatGPT will simulate aspects of emotional intelligence because it can generate text that carries an emotional flavor for a conversation. If you express frustration, then it produces a response that would appear empathetic; if you are ebullient, it can echo some lighthearted repartee. Does this mean it “knows” or understands emotions? No. Instead, what ChatGPT does is recognize patterns in language as they reflect emotion, but it does not feel emotions. It doesn’t have the physiological and psychological mechanisms that underlie human emotion. Training ChatGPT to “be human” in this way would take more than feeding it datasets on emotional expression. It would rather require an architecture that allowed for some authentic experiences of emotions — that is, something beyond the scope of current developments in AI. Even if we can, in some way, simulate emotional reactions in AI, they will still be fundamentally different from human emotions, which are tied closely with consciousness, biology, and experience. Creativity Creativity- the mark of human intelligence: the ability to come up with new ideas, solve problems in innovative ways, and create art that vibrates at a deeply emotional or intellectual level. Is ChatGPT creative? In this respect, yes. ChatGPT can write, for instance, poems, short stories, code- whatever you want it to do. All the same, this creativity is based on the pattern rather than on actual thinking outside of the box that may reflect human creativity. Some of the most creative solutions or artistic expressions are often born out of personal experience, emotional ups and downs, moments of inspiration, those things that cannot easily be expressed and replicated in an algorithm. Although AI can generate creative content, it remains without experiential or emotional depth to inform human creativity. This means it offers content based on learned patterns rather than any personal experience or emotional journey. That is the main point of explaining why, despite impressive outputs, ChatGPT is fundamentally different from a human mind. Morality The other very important element of being human is morality, for which decisions are generally guided by the moral compass of and through upbringing, culture, experiences, and societal norms. More often than not, humans struggle with conflicting values, weigh the consequences of their deeds, and sometimes, change their beliefs over time. Could ChatGPT make moral decisions? Not at all. While it may be able to be programmed to adhere to certain guidelines of morality or avoid harmful content, its understanding of morality is superficial. It does not possess personal values or understand right and wrong in the way humans do. Instead, it responds based on patterns in data that often simply mimic moral reasoning without truly engaging in the complex, human process of ethical decision-making. It is a huge task to train ChatGPT to be human in this regard since even humans are unable to accept one sense of moral principle. Morality also relates to consciousness, which AI lacks. Though AI systems can follow rules, they are not able to understand the sense of moral dilemmas that define human experience. Memory and Experience Humans are a product of their memories and experiences. Every conversation, every relationship, and triumph and every failure leave an impression on who we are and how we approach the world. AI does not have any kind of real memory of past interactions unless it has been explicitly programmed to retain specific data. Each interaction with ChatGPT is unique, and while it can process historical data, it doesn’t “remember” interactions the same way a human does. It would give this form so that ChatGPT can learn through experiences, creating a sense of identity and personal history over time. Now, it does not necessarily imply that it should remember anything but interpret it to affect the subsequent actions; the same way humans do in life, growing and changing according to their past. Current AI models, including ChatGPT, do not have this ability. Though they might learn from big datasets, they lack personal experiences and do not change according to individual interaction. Because of this, for ChatGPT to be closer to “being human,” it requires something more than the capability to store and accumulate information but must also develop the capability to reflect on that same information and to change in meaningful ways relative to past experiences. Learning by Experience Human intelligence is reinforced by the ability to reflect upon mistakes and utilize those as learning experiences. We sometimes reflect on our actions, question our decisions, and make alterations in behavior based on our experiences. ChatGPT, though, can be trained through machine learning models, though it learns very differently compared to humans. Improvement of AI in machine learning is through wide training on various datasets and adjustment of weights and biases for better outcomes. However, the methodology itself is not like that of human learning based on trial and error and is contextual, emotionally and socially tuned, and based on personal experience and interactions with other humans. In contrast, the learning process of ChatGPT is abstract and data-driven. While AI may learn to make improvements in its responses, it is without a human ability to reflect on itself. Human learning isn’t just adding new information, but building that new information off of what we already know and thinking about what the implications are. Sometimes, what we learn is questioned. ChatGPT learns to do this task better at getting statistically likely responses without any understanding or reflection about what it is learning. The Future of AI So, can ChatGPT be made human? Currently, as of the technology, the answer is no. So, can ChatGPT be made human? Currently, as of the technology, the answer is no. So, can ChatGPT be made human? Currently, as of the technology, the answer is no. In full, that is. While still able to mimic and even generate human-like conversations, produce creative content, and maintain adherence to all sorts of ethical directives, AI continues to lack depth, consciousness, and emotional complexities of what it means to be human. However, the future of AI is no longer so static. Researchers are more and more pushing the frontiers of what could be done with AI: find new approaches to making machines more intelligent, adaptive, and even emotionally intelligent. Some believe that with domains such as neuro-symbolic AI-machine learning coupled with symbolic reasoning or even quantum computation, we may eventually build one day the essence of human-like cognition in an AI. But yet, all of this raises a basic question: Do we want AI to be human? The development of machines that could think and feel like humans raises profound questions in the realms of ethics and philosophy. Where do we draw that line between the machine and the person if AI becomes too human? Should AI have rights when it can feel? Where do we draw that line between the machine and the person if AI becomes too human? Should AI have rights when it can feel? These are questions that society will have to grapple with as AI continues its evolution.

Can We Train AI to Become More Human-Like?

So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Doc: I still call myself a data analyst since that was my gateway into tech. But I’ve worn many hats over the years: researcher, software developer, entrepreneur and technologist. My body of work spans human rights, public health, food security, political risk and fighting disinformation and infodemics through the use of computational social science, social listening, remote sensing, artificial intelligence and data engineering. My current focus is on AI literacy and AI safety, especially around Generative AI. I’m also passionate about reaching audiences not typically targeted by tech events and content. So yes, that’s me with the nuns in that photo. The Daughters of St. Paul. And they are passionate about AI. Interesting! What was your latest Hackernoon Top story about? Doc: I wrote about the current fixation on Retrieval Augmented Generation (RAG) - which is basically a hacky way of making Large Language Models (LLMs) smarter. RAG still is an amazing pattern to use, but I noticed the dev-types are abusing RAG without realizing it. Seems RAG is a hammer, and everything is a nail these days. Ironically I think these RAG abusers are the same devs who probably also stigmatize Prompt Engineering - not realizing RAG is not a substitute for proper prompting, but rather prompting is the heart and soul of it. That will be another piece I will write about. Do you usually write on similar topics? If not, what do you usually write about? Doc: I have recently been writing about various topics around Generative AI - but trying to look at the technology from a more pragmatic lens. I also write about the social impact of this tech, good and bad, and try to demystify the lore around it. People who follow my work cite this - the ability to break down complex topics into digestable bits - as the single biggest reason they like hearing me talk. Great! What is your usual writing routine like (if you have one?) Doc: I’m a knowledge hoarder! I’ve set up massive aggregators of content of all sorts about topics - Generative AI being the current obsession - and from this treasure trove of info I let my ideas percolate. I don’t have pressures to write for or against any vendor or technology, and this gives me the flexibility of being more authentic about my message. I also write articles that I would love to read myself - since in the current hype-filled environment around AI, there’s literally a lot of garbage out there (not counting AI-generated crap). I also use AI to help me brainstorm ideas and pitches - it would be ironic if I didn’t. I also nurture threads for multiple articles - since new information always presents itself. People who read me will notice that while I keep to a few key messages over the years, the messaging continues to evolve. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Doc: Allocating time is the biggest hurdle. I consider communication (speeches, articles, podcasts) to be one of my strong suits, but it takes full-on dedication to put yourself out there. Criticisms are also expected - in fact, I live on feedback, good or bad. But I have to admit, after long stretches, this stuff knocks me out, too. Need to recharge to re-ignite the ideas. What is the next thing you hope to achieve in your career? Doc: As of this writing, I’ve been in tech for nearly three decades. Although I could probably still punt an app or two, I humbly admit that younger folks will have the advantage in terms of picking up new tech. So, I feel the best use of my productive years is coalescing all my past experience with tech, combined with some honest views about what’s happening now, into stories that people can relate with and also use for their own journey. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Doc: I love meeting up with like-minded (and occasionally not-like-minded) people over coffee or drinks and swapping ideas. Occasionally it gets me a podcast guest, sometimes a business collaboration. But I live for the stories - there are many coffee chats that have become an article at some point. Do you have a non-tech-related hobby? If yes, what is it? Doc: This question almost feels like an attack, haha. But more than tech, books were my first love. Whenever I can, I love browsing used book stores (there are less and less of them these days), and occasionally find a gem. In a world where the internet and AI can spoil you with fast information, finding time to settle on a good book is an unparalleled luxury, kinda like enjoying a good bottle of wine (which ties to the guilty pleasure above). What can the Hacker Noon community expect to read from you next? Doc: Hype cycles all end the same way - a crash. Then, hopefully, enlightenment. It happened to all major tech trends in the past, and it will happen to Gen AI. As it happens, I will be there to talk about it. What’s your opinion on HackerNoon as a platform for writers? Doc: It’s certainly underrated. But the quality of the information on HN has stayed pretty good over the years, which is a massive achievement in itself. I hope to write and read more on HN. Thanks for taking time to join our “Meet the writer” series. It was a pleasure. Do you have any closing words? Doc: Pro-tip on writing: cliches are the easiest fodder for good writing. No one will get tired of reading pieces that surface the cliches and dismantle them. So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. Doc: I still call myself a data analyst since that was my gateway into tech. But I’ve worn many hats over the years: researcher, software developer, entrepreneur and technologist. My body of work spans human rights, public health, food security, political risk and fighting disinformation and infodemics through the use of computational social science, social listening, remote sensing, artificial intelligence and data engineering. My current focus is on AI literacy and AI safety, especially around Generative AI. I’m also passionate about reaching audiences not typically targeted by tech events and content. So yes, that’s me with the nuns in that photo. The Daughters of St. Paul. And they are passionate about AI. Interesting! What was your latest Hackernoon Top story about? Doc: I wrote about the current fixation on Retrieval Augmented Generation (RAG) - which is basically a hacky way of making Large Language Models (LLMs) smarter. RAG still is an amazing pattern to use, but I noticed the dev-types are abusing RAG without realizing it. Seems RAG is a hammer, and everything is a nail these days. Ironically I think these RAG abusers are the same devs who probably also stigmatize Prompt Engineering - not realizing RAG is not a substitute for proper prompting, but rather prompting is the heart and soul of it. That will be another piece I will write about. Do you usually write on similar topics? If not, what do you usually write about? Doc: I have recently been writing about various topics around Generative AI - but trying to look at the technology from a more pragmatic lens. I also write about the social impact of this tech, good and bad, and try to demystify the lore around it. People who follow my work cite this - the ability to break down complex topics into digestable bits - as the single biggest reason they like hearing me talk. Great! What is your usual writing routine like (if you have one?) Doc: I’m a knowledge hoarder! I’ve set up massive aggregators of content of all sorts about topics - Generative AI being the current obsession - and from this treasure trove of info I let my ideas percolate. I don’t have pressures to write for or against any vendor or technology, and this gives me the flexibility of being more authentic about my message. I also write articles that I would love to read myself - since in the current hype-filled environment around AI, there’s literally a lot of garbage out there (not counting AI-generated crap). I also use AI to help me brainstorm ideas and pitches - it would be ironic if I didn’t. I also nurture threads for multiple articles - since new information always presents itself. People who read me will notice that while I keep to a few key messages over the years, the messaging continues to evolve. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? Doc: Allocating time is the biggest hurdle. I consider communication (speeches, articles, podcasts) to be one of my strong suits, but it takes full-on dedication to put yourself out there. Criticisms are also expected - in fact, I live on feedback, good or bad. But I have to admit, after long stretches, this stuff knocks me out, too. Need to recharge to re-ignite the ideas. What is the next thing you hope to achieve in your career? Doc: As of this writing, I’ve been in tech for nearly three decades. Although I could probably still punt an app or two, I humbly admit that younger folks will have the advantage in terms of picking up new tech. So, I feel the best use of my productive years is coalescing all my past experience with tech, combined with some honest views about what’s happening now, into stories that people can relate with and also use for their own journey. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Doc: I love meeting up with like-minded (and occasionally not-like-minded) people over coffee or drinks and swapping ideas. Occasionally it gets me a podcast guest, sometimes a business collaboration. But I live for the stories - there are many coffee chats that have become an article at some point. Do you have a non-tech-related hobby? If yes, what is it? Doc: This question almost feels like an attack, haha. But more than tech, books were my first love. Whenever I can, I love browsing used book stores (there are less and less of them these days), and occasionally find a gem. In a world where the internet and AI can spoil you with fast information, finding time to settle on a good book is an unparalleled luxury, kinda like enjoying a good bottle of wine (which ties to the guilty pleasure above). What can the Hacker Noon community expect to read from you next? Doc: Hype cycles all end the same way - a crash. Then, hopefully, enlightenment. It happened to all major tech trends in the past, and it will happen to Gen AI. As it happens, I will be there to talk about it. What’s your opinion on HackerNoon as a platform for writers? Doc: It’s certainly underrated. But the quality of the information on HN has stayed pretty good over the years, which is a massive achievement in itself. I hope to write and read more on HN. Thanks for taking time to join our “ Meet the writer ” series. It was a pleasure. Do you have any closing words? Meet the writer Doc: Pro-tip on writing: cliches are the easiest fodder for good writing. No one will get tired of reading pieces that surface the cliches and dismantle them.

Meet the Writer: HackerNoon's Contributor Dominic Ligot, AI Technologist

Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring. AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking. But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer
The volume of data
The destination IP address
The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs. Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. filled But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring . AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. continuous log monitoring Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. massive But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. could Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking . But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. play Galaga when bosses aren’t looking #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. duplication It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer The volume of data The destination IP address The type of data being transferred The timing of the data transfer The volume of data The destination IP address The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. threat detection and investigation #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. GitLab report privacy and IP So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs.

Maximizing Log Value with AI: 8 Ways to Revolutionize DevSecOps Monitoring

How Artificial Intelligence Can Make Our Smart Homes, Smarter

কীভাবে কৃত্রিম বুদ্ধিমত্তা আমাদের স্মার্ট হোমগুলিকে আরও স্মার্ট করে তুলতে পারে

Wie künstliche Intelligenz unsere Smart Homes intelligenter machen kann

Cómo la inteligencia artificial puede hacer que nuestras casas inteligentes sean más inteligentes

Comment l’intelligence artificielle peut rendre nos maisons intelligentes plus intelligentes

आर्टिफिशियल इंटेलिजेंस कैसे हमारे स्मार्ट घरों को और भी स्मार्ट बना सकता है

人工知能がスマートホームをさらにスマートにする方法

인공 지능이 스마트 홈을 더욱 스마트하게 만드는 방법

Como a inteligência artificial pode tornar nossas casas inteligentes mais inteligentes

Как искусственный интеллект может сделать наши умные дома умнее

Yapay Zeka Akıllı Evlerimizi Nasıl Daha Akıllı Hale Getirebilir?

Trí tuệ nhân tạo có thể làm cho ngôi nhà thông minh của chúng ta trở nên thông minh hơn như thế nào

人工智能如何让我们的智能家居更加智能

Finding AI-Generated Faces in the Wild: Discussion, Acknowledgements, and References

বন্যের মধ্যে এআই-উত্পন্ন মুখগুলি সন্ধান করা: আলোচনা, স্বীকৃতি এবং উল্লেখ

KI-generierte Gesichter in freier Wildbahn finden: Diskussion, Danksagungen und Referenzen

Encontrar rostros generados por IA en la naturaleza: debates, agradecimientos y referencias

Trouver des visages générés par l'IA dans la nature : discussion, remerciements et références

जंगल में एआई-जनरेटेड चेहरे खोजना: चर्चा, आभार और संदर्भ

AI 生成の顔の発見: 議論、謝辞、参考文献

야생에서 AI가 생성한 얼굴 찾기: 토론, 감사의 말, 참고자료

Encontrando rostos gerados por IA na natureza: discussão, agradecimentos e referências

Поиск лиц, созданных искусственным интеллектом, в дикой природе: обсуждение, благодарности и ссылки

Vahşi Doğada Yapay Zeka Tarafından Üretilen Yüzleri Bulma: Tartışma, Teşekkür ve Referanslar

Tìm kiếm khuôn mặt do AI tạo ra trong thế giới hoang dã: Thảo luận, Lời cảm ơn và Tài liệu tham khảo

在野外寻找人工智能生成的人脸：讨论、致谢和参考文献

have been all the rage for the past decade, but in the last year or two, the hype around AI has grown to unimaginable levels. Artificial intelligence and machine learning While artificial intelligence is capable of performing a wide variety of tasks, like recognizing and tracking objects in photos and videos, generating human-like text, extracting information and extrapolating from large amounts of data, and much more, it’s not the magic pill for every ailment. AI systems, while being widespread, may still not be the perfect fit for solving a given problem. One of the main reasons behind companies rejecting AI is cost. Artificial systems may seem omnipresent, thus inducing the feeling of them being easy and affordable to create, but this is far from the truth. Even the most simple AI-powered app of the system requires a significant time and money investment, and it’s not always obvious just how much of a commitment artificial systems really are. In this article, I aim to help businesses assess the cost of AI development more accurately as well as evaluate whether AI is truly going to save money in the long run. The Cost Of AI Development One of the main misconceptions about AI is that due to its popularity, it’s affordable and easily attainable. Artificial intelligence software, despite being widely used in many business operations, is complex to develop and takes a lot of time and, consequently, money. It’s crucial to calculate as precisely as possible the cost of AI development beforehand to avoid wasting time looking for an AI development team and disappointment when the total project cost doesn’t fit your budget. Calculating the development project is not the main step in the process of assessing the cost of AI development. In my opinion, calculating how much money it takes to complete a task you’re looking to automate now, using manual labor, is far more important. On the surface, it may seem like automating a certain task is the most sensible approach, when in reality, implementing an AI system to replace human labor is more often than not too expensive and not as effective compared to human labor. Calculate The Current Cost Before looking for AI developers, you must understand your business processes and have a clear look at how much it costs to complete a task you are looking to automate with AI. For example, a complex AI surveillance system that counts how many clients visit a store in a day may seem like a good idea on the surface: the counting process is automatic, and AI can not only count the people in the store but also track their purchases or general behavior. Falling for the AI hype, a business owner may consider getting such a system developed to get insights into their business operations. However, the cost of developing a real-time video processing AI for object detection is likely to be far more expensive, especially if your business is on a smaller side, than hiring an additional employee who will complete the same tasks for much less money. On the other hand, certain tasks may require a lot more human labor. For example, a company working with a lot of paperwork may find itself with 50+ employees sorting through hundreds of paper documents daily, looking for relevant information, and punching it into the systems manually. The cost of labor for over 50 employees, in the long run, is considerably more than the cost of developing an AI-powered OCR system for automatic document processing. Even with the upfront cost of system development, the AI solution remains the more sensible option for the business money-wise. This step may seem obvious, but many business owners seem to skip it entirely, falling for the AI hype. AI remains a complex technology that is rather expensive to develop and requires lots of data, lots of testing, and lots of time. While AI is superior to humans in completing certain tasks and may seem like the ideal choice for a certain business scenario, it may not make sense economically even in the long run. Calculating the cost of labor a certain task requires is the first step in developing any AI project for a business. Estimate Project Scope Some AI systems are smaller and less complex than others, and understanding where your project lies on this spectrum is the best way to get a precise project cost estimation. Here are a few questions to ask yourself to understand the size of the project at hand: Define The Project Goal Define a precise goal of the AI system, whether it’s looking for a specific piece of information within a document, detecting forest fires, or facial recognition for an onboarding process. It’s better to define the goal as precisely as possible, e.g. “an AI for detecting if a document has been signed and by who” instead of “an AI for processing documents automatically”. Define The AI Type Object recognition and classification, OCR, natural language processing, data analysis are all different in what type of AI models are used and how complex they are. Defining what type of an AI model your project requires will help with estimating the total project complexity and, therefore, its cost. Assess The Dataset A dataset is the cornerstone of any AI project. A dataset is used for training and testing machine learning models, and is often one of the most time-consuming and challenging parts of a project. Datasets are not small collections of images: to get accurate results, you need to include hundreds, better thousands of images, videos, audio recordings, scanned documents, etc. Estimate if you already have a dataset, for example, an archive of scanned paper documents or videos from surveillance cameras, and if not, estimate the time it would take to gather the needed data. Calculate Project Cost A precise cost calculation of any AI project can only be done by a professional AI development company that will assess your project and your business needs, test and choose ML models to get the best accuracy and provide you with a breakdown of different development stages. There are certain guidelines when it comes to AI project costs. Here’s how much time a project requires depending on its complexity: A tiny AI project with very limited functionality and a predictive AI, e.g. a model that identifies patterns in past events and makes predictions about future events, takes , at least 100 hours to develop A small AI project, including object recognition and classification, , starts at 2000 to 2500 hours Larger AI projects can easily . span across multiple years The hourly rate of any project is the main metric when it comes to calculating how much money the project will require. By multiplying this number by the amount of hours a project takes, you can get an assessment of the project cost. The rate of AI developers varies greatly based on the region, the developer’s qualification and their experience. Make sure to research the rates of AI projects in your area (or in the area where you are planning to outsource the development) to get a more accurate cost assessment for your project. For the sake of example, I will calculate the approximate AI project cost based on an average AI developer rate. According to Salary.com, the average hourly rate of an AI developer is around $50. Let’s look at the hours I’ve listed earlier for different AI projects. For example, a very small AI project with a simple pattern detection and event prediction model takes at least 100 hours. Multiplying this number by the average developer’s rate, we get $5000 — the approximate cost of this type of project. Performing this very simple calculation with the rest of the hourly estimates I’ve listed earlier results in the following numbers: A very small AI project with a simple pattern detection and event prediction model costs at least $5000, A small AI project with an object detection model starts at $100.000, A large AI project with multiple starts at $200.000. There are rough estimates, but they can be a start at assessing the cost of your AI project. Keep in mind that the hourly rate of AI developers depends in the region and county and can vary, significantly affecting the total cost of development. Additional costs, like the hourly rate of a project manager, servers, etc. also add up to the total project cost. Assess Additional Costs While additional costs are not the main chunk of the total investment, they are important to keep in mind when assessing whether an AI system is worth it for your business. A lot of custom software runs in-cloud, meaning you need to pay a monthly or yearly fee for renting servers. These costs are usually small enough to not have a large impact on the overall cost of development, but AI systems are different in this regard. AI systems require servers with powerful GPUs to run efficiently, and those tend to be more expensive, thus increasing the cost of monthly system upkeep. If you’re looking to develop a complex AI system or an AI system that will be used by dozens of employees, you need to account for tech support to guide users through the system and fix any bugs or malfunctions a system may experience. Another important cost to consider is the cost of additional development. During the system operation you may develop a need for additional functionality. For example, you may need to start processing new document types, or detect more objects on a video. These additions can be costly, so you need to keep them in mind when calculating the total project budget. The Cost Of AI vs Human Labor After you roughly estimate the cost of the AI system in question, compare it to the money you now spend on completing the same task. You may be surprised, but more often than not the price of human labor is much cheaper than the price of a custom-made AI system. Even with all the hype around AI, it’s important to keep your head cool and hype-free when looking into having an AI developed. Tempting as it is, implementing AI is not always a sensible idea due to economics.

AI Is More Expensive Than You Think

AI আপনার ধারণার চেয়ে বেশি ব্যয়বহুল

KI ist teurer als Sie denken

La IA es más cara de lo que crees

L’IA coûte plus cher que vous ne le pensez

AI आपके विचार से कहीं अधिक महंगा है

AI は思っているよりも高価です

AI는 생각보다 비싸다

IA é mais cara do que você pensa

ИИ дороже, чем вы думаете

Yapay Zeka Düşündüğünüzden Daha Pahalı

AI đắt hơn bạn nghĩ

人工智能比你想象的更昂贵

GPT-Like LLM With No GPU? Yes! Legislation Analysis With LLMWare

There is no doubt that AI and Robotics are making human life better. The way these technologies are helping mankind is beyond our imagination. Here are some insights on the impact of AI and Robotics in the healthcare industry. Early Diagnosis AI is being used to diagnose the diseases such as different kinds of cancers. For example. The impressive IBM Watson has an accuracy rate of 99% in diagnosing cancer. (Source: Futurism ) And another endoscopic system from Japan can detect colon cancer, although the accuracy rate is 86%. (Source: Futurism ) Besides these robots, AI systems in the shape of wearables and other devices are also helpful in managing and analyzing early heart diseases. This enables the chances for doctors and caregivers to provide better monitoring and treating the life-threatening conditions soon and in treatable stages. Improved healthcare As we all are aware that Robots don’t have any feelings. They can’t get distracted and don’t know what tiredness is. That makes it more potential for healthcare. The Waldo robots are being used by many of the top hospitals to perform surgeries. They can perform the task with excellent precision. The robots also have more strength with no quivers of the knife. The main thing here to notice is that the program must be set correctly for the undergoing procedure. And throughout the surgery, the human surgeon helps the process as the secondary supervisor. Medical Research AI and Robotics both improved the research field of healthcare too. As per the , the drugs take 12 years to reach the patient from the research lab. It is also remarked that only 5 from the quantity of 5000 makes it to the preclinical testing, and just 1 out of these five is approved for human use. CBRA The estimated amount of $359 million will cost the company to develop a new drug and pass the process. The latest growth and development in AI modernize the discovery of drugs and repurposing the method to cut the time to market the new drug and its cost too. Better decision making These AI tools are helping too much in recording data and using it for better analysis. Now because of this upgrade, the healthcare experts can align big data with accuracy and make a decision precisely on time. This also helps in extrapolative analytics and can support clinical assumptions. This also helps to import the best administrative tasks. AI is now used to identify patients that are at the risk of developing a condition by using the pattern. AI is beginning to take over the grasp in healthcare. Helping hands Secondary robots are the helpers. Hospitals run 24/7. There is no doubt that the nurses, ward boys, and other staff have too much to do. That is why secondary robots were invented. The robots such as the Moxi robot by Diligent Robotics help the hospital staff restock, bring the items, clean, and do other works. This enables the human staff to spend more time with the patient to offer a friendly environment while leaving the never-ending work to the robots. You can book a demo of Moxi Robot directly on their . website Another excellent example of those secondary robots is the UV light disinfection robot. It is stated that the robot doesn’t leave the room until it is totally germs-free. AI Assistants This might surprise you (or maybe not), but some robots are made to assist patients. This is a great help for medical staff. Robots like exoskeleton help the patients who are paralyzed. They assist them to walk again. AI technology like smart prostheses makes a great impact in . The limbs are reactive and more exact than the original body parts. It adds the possibility to cover them with skin and connect them to muscles. healthcare Some medical robots in the healthcare industry The Xenex Germ-Zapping Robot The TUG The da Vinci Surgical Robot The CyberKnife The PARO Therapeutic Robot These are the five famous robots that are being used in the healthcare field most. Wrap Up From surgical operation room to inventory, the tools and robots are being used. This not only helps the doctors but the patients too, and as we discussed above, some robots help them regain their strength. There is more to come in healthcare because of AI and Robotics.

AI, Robotics, and the Future of Healthcare

Why AI Will Bring an Explosion of New Jobs

Biometric authentication technology has been an important industry trend for years, especially in 2021 due to the latest AI innovations available on the market. According to IBM, are caused by compromised credentials. Worse, it can take an average of 287 days to identify and respond to a data breach. 20% of breaches AI-based security is increasing in usage and will be necessary to remain competitive in any industry. IBM reports that as of 2021, 25% of businesses have completed deployment of AI-based security, while 40% are partially deployed. The remaining 35% have not begun this process, and if your business falls into this category, you may be placing your clients at great risk for dangerous data breaches. Investing in AI-based security can save a business up to $3.81 million in 2021. Being able to use artificial intelligence to identify and automatically respond to data breaches is incredibly important for protecting the data and privacy of a company and its customers. AI-based biometrics authentication provides yet another safeguard against a data breach that is essential for businesses of any scale. Not only that, but IBM’s study shows that correct use of AI security platforms will cost businesses less. It is extremely important to understand and consult experts in the field to develop the solution that is best for your business. With more advanced biometric authentication, verification, and identification technology at our disposal in 2021 than ever before, we can make industry systems more secure while maintaining efficiency and accessibility. Let’s talk about how biometric authentication works and the most important new trends, such as artificial intelligence biometrics. Biometrics Technology Overview At first glance, biometrics may appear to simply be a way of accessing a cell phone more quickly while retaining security, but there are many more applications than this. However, at its core, the objective of biometric security technology is to improve security using parts of our bodies that cannot be easily replicated. Although machine learning and quantum computing may be able to , biometric information like fingerprints, faces, and irises are not so easily determined or emulated. guess a password According to Statista, the biometric technology market will value at before 2027. $55.42 billion Over the range of 10 years from 2017 to 2027, worldwide spending in the identity verification market is forecasted to grow by more than 13 billion U.S. dollars, increasing from 4.93 billion in 2017 to over in 2027. Identity verification is an important aspect of information security, as it ensures that only the people with legitimate authority to access information are able to do so, thereby preventing harmful or malicious intrusion to protected information. $18 billion Types of Biometrics Biometrics technology can be split into three domains: identification, verification, and authentication. Identification is used when the system wants to know who the user is. Verification is about using that biometric information to determine if any other information is associated with the user. Finally, the goal of authentication is to understand if the identity the user claims to be is correct and authorized to access the services and data they are requesting. An example of biometric identification may be used to determine the identity of a user against a database of faces. Law enforcement facial recognition databases use this technology to a great degree and can help investigate security camera evidence footage with better accuracy than a human. Verification is helpful for ensuring that the user and the information stored in the database about them match. Authentication is perhaps the most critical of the three, enabling authorized users to access sensitive information while restricting unauthorized persons. The table below shows the various characteristics of each type of biometric technology. One of the most important takeaways for any business considering biometric security methods is that it is not always wise to rely on only one form of biometric technology, i.e., unimodal. Instead, a multimodal approach that uses more than one type of biometrics is much more secure. Facial Recognition Technology The facial recognition market was estimated at in 2020. The market is projected to grow, reaching 8.5 billion U.S. dollars by 2025. $3.8 billion Biometric authentication technology that uses facial recognition has been a valued feature for security for years, making its way onto millions of iPhone and Android smartphones. With advances in augmented reality and machine learning aiding computer recognition of human faces, facial recognition technology has become more advanced than ever before in 2021. One of the reasons why facial recognition technology has improved for identification and authorization this year is because of the necessities of the pandemic. Due to the increased usage of face coverings like masks, facial recognition has become less accessible. However, many facial recognition solutions now can account for obstacles like face coverings. This is not only good for consumer convenience, but also helpful for law enforcement identification of criminals. By matching the face of the subject with a database, users can be identified even if they are wearing a mask. Since 3D cameras can get much more information about a human face than a two-dimensional camera would, facial recognition is much more secure than before as a biometric authentication technology. Previously, it was much easier for computers to be fooled by a picture of someone’s face or a similar-looking face. However, machine learning has helped narrow the margin of error to prevent hackers from exploiting facial recognition-based security, and various anti-spoofing methods now exist. Voice Recognition Technology The global voice recognition market size was to grow from $10.7 billion in 2020 to $27.16 billion by 2026. forecasted Hands-free communication has been enriched greatly by voice recognition biometrics technology. However, voice recognition has another purpose: identification. Smart home device ecosystems like Google Home, when trained, can identify members of a household-based on their voice. This is very helpful for displaying information pertinent to that individual, such as their calendar and notifications. Voice recognition has other applications in authentication and verification. Being able to identify someone and verify their access based on their voice can save a lot of time, but we are getting to the point where voices may not be as secure as other biometric methods. Deep learning has made it possible to realistically mimic a voice using a computer, a technique called vocal synthesis. ‘Deep fake’ voices have been used in successful that, in one case, cost an energy firm in the United Kingdom $250,000 in 2019. fraudulent activity As deep learning technology improves, the more realistic vocal synthesis will become. Unless technology improves to better recognize legitimate vs. fake voices, voice recognition biometrics technology is better suited for non-security applications, particularly identification. This may be for consumer-based systems like smart homes or enterprise solutions such as automatic transcriptions of meeting recordings that can attribute speakers by name. Iris Scanning Technology This form of biometrics technology is often seen in science fiction. It is similar to facial recognition in that it is contactless but focuses only on one particular feature, the eye. One advantage is that there is less complexity to account for. Advanced facial recognition systems are not needed, only something that can recognize the features of the iris. You don’t necessarily have to be close to an eye to scan the iris. At 40 feet away, tests at Carnegie Mellon University . can scan an iris One advantage of this technology is that because it uses infrared sensors to analyze the iris, scanning the eye can be done in low light conditions. Law enforcement applications of this technology for the identification of suspects are definitely one of the first things that may come to mind. However, this can also be a great authentication and verification method for both consumer and enterprise applications. Samsung’s Galaxy S9 and Note 9 smartphones both have iris scanning technology, demonstrating that this biometric method can be . This can be useful not only for consumers but also for enterprise-level device security. used on portable devices When comparing against a single authorized user, an iris scanner can authenticate a user nearly instantaneously. When identifying or verifying a user against a large database of hundreds of thousands of subjects, less than is sufficient to authenticate. 20 seconds Fingerprints Authentication Technology Fingerprints have been in use for for identification. They have particularly been useful in forensics technology, but recently they have become extremely useful for consumer and enterprise security solutions. In 2003, the pioneered the very first cell phone with a fingerprint sensor. It would later be popularized with Touch ID on Apple iPhones and various scanners on Android phones. thousands of years Fujitsu F505i Fingerprint authentication technology works just like any other biometric method. By comparing the grooves and valleys of an individual’s finger against a standard subject or a database of subjects, they can be identified, verified, and/or authenticated. This can be done very quickly by the user without disrupting their experience. However, one downside to fingerprint scanning is that it most often is not contactless. The user has to press their finger against the sensor in order for it to register. In some circumstances, this may not be desirable due to health concerns, especially with the global spread of the SARS-CoV-2 virus. However, it is possible to scan a fingerprint or entire hand via contactless methods. The smartphone uses a time of flight (ToF) sensor and infrared camera to detect the blood vessels in the hand of the user, all without requiring the user to press their hand against a surface. LG G8 ThinQ Another downside to fingerprinting is that it can be inaccessible for some groups of people with disabilities. Because of this limitation, it may be wise to invest in multiple forms of biometric authentication to maintain accessibility. How AI Can Improve Biometric Authentication Systems When it comes to security, improving the accuracy and efficiency of biometric authentication systems cannot always be done with human programming. Artificial intelligence and machine learning can help us make our systems more secure and efficient. Biometric technologies can be split into two domains, physical and behavioral. Physical Biometric Technology Physical biometrics include everything that we have already discussed in this article. These are objective characteristics of a person like their face or fingerprints, DNA, and more. This must be converted into data that can be analyzed by the AI system and compared against a database for authentication purposes. One of the cases where AI and ML prove most helpful is with facial recognition. Used extensively with augmented reality solutions, AI can help make facial recognition by computers much easier by analyzing facial features and matching them with a database. Behavioral Biometric Technology One of the most interesting trends that artificial intelligence makes possible is . This uses unique behavioral characteristics of how they interact with the world, things that the user may not even realize about themselves. This is one of the most effective lines of defense against deepfake fraud attempts. Some of the more popular ways to measure behavioral biometrics are: behavioral biometric technology Mouse activity Keystroke movement Touch screen press size, area, and pressure Mobile device motion The Biometrics and Data Pattern Analytics Lab from the Autonomous University of Madrid , a system for bot detection that uses behavioral biometrics. It’s entirely possible that behavioral biometrics can be used without the user’s knowledge, eliminating the need for annoying tests of the user’s humanity as CAPTCHA challenges have done for years. In the next few years, users may never have to click on pictures of crosswalks and stop lights to prove they are ‘not a robot’ again while browsing the Internet. developed BeCAPTCHA Importantly, behavioral biometrics can assist in ensuring security throughout an entire session. For example, if a user authenticates themselves and then leaves the room, unwittingly enabling an unauthorized user to gain access to their computer, this would be a serious security risk. However, behavioral biometrics can detect the inconsistent behavior of the second user and restrict their access dynamically. How Multimodal Biometric Authentication Solutions Work Every form of biometrics technology has pros and cons. They each are also not universally effective. To improve the accuracy and effectiveness of biometric authentication technology, it’s important to layer security with multimodal biometric recognition solutions. We can see this in action in the following diagram: *Neural networks on mobile applications can be challenging to develop and implement. Neural networks can be integrated into applications using TensorFlow Lite; however, there are various limitations. Training your neural network models with the TensorFlow library can help. You will need to thoughtfully design the architecture of the application beforehand and put those requirements into consideration. ** If a native application is not possible, this process can be offloaded to the cloud to process the data like Rest API. However, this will incur additional networking resources and require an Internet connection. There is an Nvidia Docker that can simplify the deployment of the system, while a service provider (such as AWS) can provide an uninterrupted communication channel, computing power for neural networks, and a convenient interface for scaling your system. Test No. 1 - Facial Recognition The user creates a photo-imprint that’s stored on the device using a camera. This biometric imprint is converted and normalized using the OpenCV library. The face is identified using a photo, and each of the 64 landmarks detected by OpenCV are highlighted. A biometric verification landmark includes things like a distance from the bridge of the nose to the eye and other facial features. These landmarks and a cutout image of the face are transferred to a deep neural network, which is trained using the TensorFlow library. An eDNA feature vector is formed after neural network processing is completed. The feature vector collects the biometric characteristics of a particular person. The length of the vector is usually 2048 bits. The lengths depend on the DNN architecture. During verification, eDNA is issued and compared with the anchor record that was formed earlier. Reverse engineering is impossible because there is no access to the vector. The biometric system will periodically update this anchor record to match an individual’s changing looks. Test No. 2 - Voice Verification The user provides a voice sample through a microphone that gets processed by the Librosa library. The library reads the audio, transforms and converts it, and then transmits biometrics to the neural network (DNN). An eDNA feature vector (2048 bits) is formed, which takes into account such biometrics as timbre, intonation, tempo, pitch, and other characteristics that the neural network was trained to respond to. Case Study: AI-based Biometric Authentication Solution By combining a microservice-based architecture, WebRTC, and machine learning-powered biometric recognition, we developed a single sign-on (SSO) biometric authentication solution for a US-based firm. Utilizing voice and facial recognition, we developed an enterprise verification as-a-service (EVaas) solution that uses the technologies and trends discussed earlier. This product proved that biometric authentication systems can be highly customizable and easy to use, powering a very simple user interface from behind the scenes. In addition, this example was able to integrate with existing systems via API. Biometric Authentication Solutions Development Securing systems is not a matter to take lightly. When sensitive information is on the line, like protected health information or institutional data is on the line, it is extremely important to take the measures necessary to prevent data breaches. Failure to maintain a high level of security surrounding this information can result in fraud that can cost businesses tens or even hundreds of millions. Written by , AI/ML Solution Architect at Evgeniy Krasnokutsky MobiDev

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