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How I Created AI Photocraft: An AI SaaS Tool Using Open Source Projects on GitHub

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

Navigating Complex Search Tasks with AI Copilots: Opportunities

I can’t lie; as a freelance writer living through this explosion of AI tools, I finally feel…seen. After decades of software revolving around complex code, we're finally seeing tools that feel like they’re built for people who think in stories, phrases and sentences. But for all that’s been written (and fear-mongered) about AI, one important aspect goes under-discussed: Natural Language Interfaces ( ). NLIs NLIs promise a less bitter-sweet relationship with digital tools, where the compromise doesn't feel quite so hefty. Suddenly, I can lean on my natural language tendency without needing to constantly fit into the mold of highly complex Graphical User Interfaces (GUIs). They're making tech less of a minefield and more of a playground for us humanities-inclined folks. I work in tech, but I identify more as a "word person", which is a bit of an oddity. In school, I was an English major and kept a healthy distance from any math classes. But I love the internet too. It's my second home. And ironically, I run a business writing sales copy for B2B SaaS companies, which means dealing with a tech stack of business tools. And honestly, their Graphical User Interfaces (GUIs) are clear as mud to me. Are they misunderstood by me? Definitely. Am I throwing away cash on underutilized apps? Quite possibly. Do I want to spend a whole afternoon scouring help documentation or having a call with customer support to figure it all out? Not a chance. As Steven Johnson posits in his book the concept of the "adjacent possible," each new innovation unlocks a door to a previously unimagined room of possibilities. Think of it as a "Sealed Garden" – the technological garden brimming with opportunities was once enclosed behind a tall, locked gate, the key being technical proficiency. But NLIs have replaced the lock with one that uses a more commonplace key: natural language. Where Good Ideas Come From In a piece from the Harvard Business Review titled , the author pulls back the curtain on how NLP tools are revolutionizing his research work. They're doing more than just aiding. They're reshaping tasks like summarizing, data labeling, brainstorming, and even literature reviews. The Power of Natural Language Processing It's still early days, but it's already clear that the potential is enormous. Did natural language abilities just become a “technical skill set”? The ability to express ideas clearly, to write effectively, and to think critically – these are now becoming keys to unlocking the fuller potential of the digital world. As evidenced by tools like and Adept.ai, these skills are being rewarded more than ever. ChatGPT Recall how Netscape Navigator took the internet by storm in the mid-90s. Why? Because it made the tangled web of internet protocols way less intimidating for everyday people. For the first time, you didn't need a degree in computer science to surf the web. That was a big deal, and Netscape quickly took a big chunk of the market. As Jason McCaffee discusses in his article, , we've seen a significant evolution in the way we interact with technology. From to graphical user interfaces (GUI), and now to natural language interfaces (NLI), each step has made technology more accessible. The Shape of Interfaces to Come command-line interfaces (CLI) Fast-forward to today, and I reckon we're on the brink of a similar shakeup, this time with AI tools. Beware of the reductive nature of AI models Models like operate on a simple principle—they draw from the inputs provided to generate outputs. Their effectiveness is tied to the quality of the inputs they receive, sometimes even slightly less. GPT-4 Imagine you're navigating the vast expanse of the internet in search of information. The information you're seeking serves as the input that the language model remixes and refines. But what happens when our focus shifts to generating new, original ideas? Language models require a foundational idea on which they can build, remix, and enhance. By providing that centerpiece, we enable the model to contribute in ways that are scalable, surpassing the limitations of our human brain. It adds layers to your initial idea, building around it, enriching it, and combining it with other concepts. The future of human-computer interaction is conversational, and that’s good for writers For writers, AI has its fair share of limitations — hallucinations, biases that echo its training data, and a plethora of bugs inherent to an early-stage tech. Yet, despite these hiccups, I think there's a lot to look forward to. Personally, I'm a fan. Stumble upon your own set of discoveries that highlight what it does well, and maybe even what you struggle with. Acknowledge that AI is a different kind of "brain" — a complex one, yes, but not nearly as intricate or nuanced as the one sitting in your skull. And, crucially, understand that AI can scale its thinking far beyond what any human can do. It sifts through vast amounts of data, pinpointing the needles in the digital haystack. Take these insights, these little nuggets of gold, and use them to breathe even more life, more humanity, into your work.

AI Makes Tech Less Terrifying For “Word People”

Let’s go back to 1997. Steve Jobs returns to Apple and everything is in the mud. The company needs to turn the tables and come up with an epic campaign that would solidify the brand for decades to come. The marketing agency hired for the campaign uses AI and writes the pitch based on these suggestions (from ChatGPT): Showcase the company's latest and most innovative products. Highlight the user-friendly features of those products. Highlight the company's commitment to design and user experience. Emphasize the company's brand values and mission. Would the final campaign be as good as the "Think Different" campaign that we ended up getting? Can AI come up with campaigns like "Just Do It," "Finger Lickin' Good," and "The Happiest Place on Earth"? AI is bringing a significant revolution in the marketing landscape, with ChatGPT leading the charge. If you are unaware, ChatGPT is a tool that can generate human-like text and can perform a lot of language processing tasks. It can even talk to that annoying friend you have who keeps texting you all the time. You can surely now understand why businesses are drooling at the prospect of using this technology for marketing. To be honest, AI in marketing is tricky as marketing is inherently art. While some see it as a revolutionary step forward, others may have concerns about potential job displacement. And, there are those who remain indifferent. But one thing's for sure, AI is going to change the marketing landscape forever. Whether it's for better or worse remains to be seen. In this article, we explore the potential impact of AI on creative content and examine the reactions of different stakeholders to this disruptive technology. I personally believe that the shift wouldn’t be as linear as one might expect. Through a close examination of challenges and opportunities, let’s try to predict how things will play out. Phase 1: Rapid Adoption and the Rise of the Misguided In the first stage of adoption, there would be three segments of people in control of marketing and content creation and their reaction to the emergence of AI. These include the upper management, the underperfomers, and the good marketers. The reaction of the upper management Executives at the top of the hierarchy are not always subject-matter experts. This is a common trend across industries, much like politics. While some will quickly realize the value of AI in marketing, others will view it as a cost-cutting opportunity. Does every executive in a suit understand the value of art, the heart of marketing, and the soul of copywriting that resonates with the audience? When Steve launched the Think Different campaign, he shared an example from Nike. He mentioned that when you see an ad from Nike, you do not think of a brand that sells shoes. Instead, you think of a brand that honors great athletes and great athletics. If you expect AI to do all of the work, it is hard to believe it can achieve something of this grandeur. As Eugene Schwartz said in his book Breakthrough Advertising, "Advertising is not a science; it's an art. The most powerful words in advertising are those that stir emotions." This statement was true in 1966 and still holds true today. The problem with the suits pulling the strings would be that some of them would see the content AI can produce and think of it as the best thing since sliced bread, leading them to believe it can replace human resources for copywriting and content creation.  Without a deep understanding of what makes art connect to humans, they would want AI to do all the heavy lifting, unable to differentiate average content from epic content. Some creators would be let go during the process. This includes writers who write their blog posts, copywriters who create their landing pages and newsletters, script writers who write scripts for their podcasts, ad creators, and also pre-sales reps who are probably writing personalized outreach emails. This would not be the case for every company as some executives would understand how valuable the tech is and would empower their workforce in adoption. But, we will see a huge amount of companies churning out average content and this would work without many issues for a while. The reaction from underperformers and inexperienced marketers predicts that by 2030, AI could automate up to 30% of tasks currently performed by human workers. found that of the jobs that are most likely to be automated by 2025, marketing and sales jobs are among the top 10. As you can tell, marketers who do automatable jobs or struggle to consistently produce high-quality content, are at risk of losing their jobs as their average output can easily be replaced by AI. In fact, some of them would turn to AI to do all of their work. A report by McKinsey A similar study by the World Economic Forum Lazy marketers and content creators may see AI as a way to automate their work and free up their time, but this could lead to a lack of creativity and inspiration in their content. Instead of AI helping them do better, they would want tools like ChatGPT to do everything. Need to create a landing page copy? Just add the main pointers to ChatGPT and let it generate the copy. Need to create a blog post? Just provide some details to ChatGPT and let it make the copy. Plagiarism can’t be detected accurately as of now. This group of people does not understand why creating content is an art form. Their strategy would work for a while until it doesn’t. The reaction from good marketers and content creators In contrast, savvy marketers and content creators who have a keen sense of taste for quality content and understand how to strike a chord with their audience will recognize that AI cannot replace their work. Instead, they will see it as a tool to enhance their performance, eliminate human errors, and remove the mental fog that all artists endure at some point. These marketers will use the technology to take their work from a 7 out of 10 to a 9 out of 10. Here are some ways content creators can use a tool like ChatGPT to enhance their performance: Generate ideas and base outlines Edit and linguistically correct content inputs Rewrite content in a specific writing style or framework Get suggestions on writing content for a particular audience segment The possibilities are endless! Phase 2: The Spam-Fest and the Repercussions That Follow As the adoption of AI in content creation continues to rise, it's important to understand how these tools work. ChatGPT, for example, is a language model that uses machine learning from a large dataset to generate text. It studies other people’s work and generates an output on similar lines. Variations would show up based on your original input. According to ChatGPT itself, “it may generate text that is similar to text it has seen before in its training data.” Using ChatGPT to generate content will result in variations of content that you or others in your field have already written. It is essentially paraphrasing with extra steps. While ChatGPT is an excellent replacement for content creators who paraphrase other people's work, it cannot replace the work of thought leaders and critical thinkers. As the adoption of AI in content creation increases, we will start seeing a lot of content produced through AI. Websites will have similar copywriting, blog content, newsletters, and social media posts. The API of an AI tool connected to an automation tool can take this to dangerous levels. For example, automation can be used to write comments or generate personalized outreach emails in bulk. SEO specialists might also flood low-competition keywords with API-generated content. To showcase an example, I picked a random post from LinkedIn and had ChatGPT create a comment for it: The output is pretty usable. I can easily establish an automated process where the workflow generates comments for each post on my LinkedIn feed without expending any human effort. If others begin to do the same, it can wreak havoc on the platform. We can say the same for pretty much any platform. As this practice becomes more widespread, we can expect a significant decline in user experience. Dark times are ahead of social media users! As the spam-fest continues, big players like Google and Facebook will work on figuring out how to stop the spam. With the plethora of data and resources in their possession, it's not a matter of if but when. Thanks to the Youtube channel, AI in a Minute, I was able to learn about tools such as and , both of which can detect if a piece of content has been written by AI. It does the job fairly well but fails to exempt content that has only slightly been assisted by AI. Huggingface’s GPT2 Output Detector GPT Zero Plagiarism Detector I have two drafts of this article (up to this point). One of them is the original copy and the other one is a modified copy where I have taken the help of ChatGPT to make some linguistic changes. Ironically, the AI-assisted copy received a 95% "real" score on the Huggingface tool, while the original draft received a 25% "real" score. Removing four sentences that I generated from ChatGPT as an example in the opening section of the article dramatically increased the score from 25% to 99% “real” for the original draft. For the AI-improved draft, it changed the score from 95% to 99% “real”. While these tools aren't perfect and cannot be taken at face value, progress is being made, and soon big data will catch on with much more accuracy. And when this happens, penalties will likely follow. On the other end, consumers will become more adept at identifying AI-generated content and subsequently begin to ignore it. To illustrate this point, I used a tool called Copy AI to generate a cold email pitch targeted at Bill Gates using his LinkedIn profile and a description of ChatGPT from Wikipedia. Here’s what it came up with: Generate five of these and you will know that the tool uses texts and keywords from the target’s LinkedIn profile and creates the copy. It puts two and two together to align the product with the person but the results are sometimes disastrous. The messages have no soul. As I had mentioned earlier, tools like Copy AI and ChatGPT are potent if you let them do some of your jobs instead of all the work. As people start getting more of these AI-personalized emails, they would soon be able to tell that they are computer generated. As the prevalence of AI-generated content increases, businesses may experience a decline in engagement levels from both consumers and platforms. Big data corporations like Google and Facebook will work to combat the rise of AI-generated spam, potentially lowering reach or decreasing rankings for such content. At the same time, audiences will begin to recognize and ignore AI-generated content, resulting in a decline in effectiveness for businesses that blindly rely on AI as a replacement for human input and creativity. Google released for AI-generated content on Feb 8, 2023. Update: its guidelines Phase 3: The Rise of the Hybrid Model and the Survival of the Critical Thinkers In Phase 3, we see a partial return to the status quo as businesses begin to realize the importance of standing out in the ocean of uninspired content, while also avoiding penalties levied by big data corporations. This realization leads to a renewed focus on the role of artists in marketing and content creation. The savvy marketers and content creators discussed in Phase 1 will be at the forefront of this shift, as they have already integrated AI into their processes to eliminate their own limitations. These early adopters have a deep understanding of both the limitations and potential of AI, and as a result, will have become even more skilled marketers. As more businesses follow suit and begin to understand the value of incorporating AI into their marketing strategies, we will see the rise of the hybrid model. In this model, AI and human efforts work in tandem to produce the highest quality content possible. However, it's not all good news. Inexperienced or lower-skilled marketers will continue to be at risk of losing their jobs as mundane tasks and lower-tier content can be produced and automated by AI. The only option for them would be to improve their skills and bring better outputs. As the tech advances, the need for out-of-the-box thinkers and good marketers would continue to increase. Businesses must invest in their workforce and provide training and development opportunities to stay competitive in the evolving marketing landscape. To close this section, I will reference a quote that has been frequently cited in the discourse surrounding AI: “We shape our tools and thereafter our tools shape us” Trivia: How ChatGPT Helped in the Creation of This Article It would be unfair to have this article end without some meta-commentary on how AI assisted in its creation. To provide some background, I am a small marketer from India and English is not my first language. Despite being well-versed in Western culture through my consumption of media, my linguistic abilities may not always align with American writing standards and ChatGPT has significantly helped in making improvements. My process involves utilizing the tool to rewrite paragraphs in an American business writing format and then thoroughly reviewing the original draft, comparing it with the AI-generated result, to identify any areas where further improvement is necessary, subsequently making adjustments to the copy I originally wrote. If you compare the AI suggestion to what I ended up having in the final copy, you will notice that I've only incorporated a few linguistic suggestions. Needless to say, ChatGPT has become my personal editor effectively pre-editing my work before it reaches human editors. This streamlines the editing process for both writers and editors alike. In addition to utilizing ChatGPT for linguistic revisions, I also employed the tool to generate ideas for the article. Initially, I had intended for this to be a LinkedIn post, but upon further consideration, I decided to expand it into a more comprehensive piece. I utilized ChatGPT to process my initial LinkedIn draft and generate ideas. I also had it review my work and fetch relevant statistics. Final Thoughts The sudden rise of AI has completely changed the marketing landscape among other things. And, it is important to consider the potential ethical implications of its use. With the tech improving consistently, junior workers are at an alarming risk of losing their jobs. We are quite possibly about to see the survival of the fittest. This brings an opportunity for everyone to step up as it is hard to believe that AI can replace critical thinkers and those who think of marketing as an art form. While the initial adoption cycle will be rocky and brings a risk of poor user experience for consumers, once the dust settles, good marketers should be better off. Businesses should welcome these changes to the landscape without taking hasty decisions on their human workforce. The rise of AI ensures that only the most innovative and creative marketers will thrive. The ones who think outside of the box, the ones who take big risks, the ones who… ‘think different’.

AI in Marketing Ensures the Survival of Artists Who ‘Think Different’

বিপণনে AI শিল্পীদের বেঁচে থাকা নিশ্চিত করে যারা 'ভিন্ন ভাবেন'

KI im Marketing sichert das Überleben von Künstlern, die „anders denken“

La IA en el marketing garantiza la supervivencia de los artistas que "piensan diferente"

L'IA dans le marketing assure la survie des artistes qui "pensent différemment"

मार्केटिंग में एआई उन कलाकारों के अस्तित्व को सुनिश्चित करता है जो 'अलग सोचते हैं'

マーケティングにおける AI は、「考え方が異なる」アーティストの生き残りを保証します

마케팅 AI, '다르게 생각하는' 아티스트의 생존 보장

IA no marketing garante a sobrevivência de artistas que ‘pensam diferente’

Pazarlamada Yapay Zeka, 'Farklı Düşünen' Sanatçıların Hayatta Kalmasını Sağlıyor

Trí tuệ nhân tạo trong tiếp thị đảm bảo sự sống còn của những nghệ sĩ 'Nghĩ khác'

营销中的 AI 确保了“与众不同”的艺术家的生存

When building a generative AI application that needs to call a large language model (LLM) multiple times to complete a task, a common problem is that repeated queries to the LLM can be both expensive and unpredictable. Large models such as GPT-3.5/4 are incredibly resource-intensive to train and run inference; this is reflected in the API charges as well as occasional disruptions to service. ChatGPT was originally released as a research preview and wasn’t intended to be used for production applications. However, its usefulness across a vast array of applications is indisputable, so has exploded. interest in LLMs Since the inception of ChatGPT, users have been looking for ways around the lack of privacy and the inability to control uptime or inference settings when using GPT. This has led to the popularity of free, public models such as Meta’s and later the creation of quantized and lower-parameter versions of Llama that can run on consumer hardware. These public models are capable of providing much of the same functionality as GPT for much less computing power, though at the cost of fewer parameters and fewer verbose outputs. Llama 2 If your application doesn’t necessarily depend on processing excessively large contexts or producing verbose outputs, then hosting your own inference on instances you control can be a more cost-effective option. And when it comes to real-world applications of (RAG), the cost differences may become even more significant. retrieval augmented generation I’ll demonstrate a simple method for combining vector stores, lexical search, and prompt engineering to perform accurate RAG on commodity hardware. Using this method, you can both reduce the complexity of large volumes of information and make running generative AI applications more accurate, efficient, and cost-effective at scale. By using RAG on specific stores of information, you can gain the ability to eliminate hallucinations and create effective and knowledgeable agents from any source material without needing to pay for third-party APIs. To get started, you will need either a DataStax Enterprise 7 instance or to store the vectors and text data, as well as an LLM and a sentence transformer model to generate responses and encode your data with vectors. Depending on the complexity of your data or the user prompts, you may also consider combining this with a DataStax Enterprise 6.8 database that can perform searches to match wider ranges of data, which is what I used in this example. DataStax is continually working on improvements to enable all of these operations with a single database, but for now, I use two databases. DataStax Astra DB Solr Solving for Hallucinations Regardless of which LLM you choose, they all still suffer from . For now, that limitation needs to be resolved by feeding truthful information into the context of prompts to the LLM, otherwise known as RAG. The method by which you locate your information and transform it for the prompts completely depends on your data model, but you can find more pertinent information in a more efficient way by using vector databases. hallucinations Say, for instance, that you have a collection of e-books on a subject you’d like to explore, such as how to play . Under normal circumstances, it would take years to read through the supporting literature and gain enough gameplay experience to reach an expert level. Warhammer 40,000 A targeted question such as, “What can you tell me about Morvenn Vahl of Adepta Sororitas?” would be best answered by a veteran player or any employee at a Warhammer store. While ChatGPT can answer many questions about the game, it unfortunately has no training data that covers this particular character: Compare this to a Llama 2 13B parameter LLM hosted on a consumer workstation with an Nvidia RTX A4000 graphics card. Similarly, the model can demonstrate basic knowledge of the Warhammer universe, but because of the tuning, the model doesn’t care that the character isn’t found and provides instead a best-effort hallucination: If you want to build a chatbot that can help both newcomers and veterans play Warhammer 40,000, then these outputs are unacceptable. To be an effective game guide, the chatbot needs to know the rules of the game, the rules for each unit, some bits of the lore, and some strategy and commentary. Luckily, all of that information on the 10th-edition rules is available for free from Games Workshop and fan websites, and all you need to do is make it searchable to your chatbot app. Compare this to the same 13B Llama model, where with RAG, it is asked to compare a couple of sources on Morvenn Vahl and devise a relevant answer based on the user prompt. This time, the chatbot has access to a database full of all the public information on how to play Warhammer 40,000, 10th Edition: search database and a vector What a difference! Not only does it find pertinent information on this niche character, but it also keeps its outputs in line with the context of how to play the game with the 10th-edition rules. The hardest part in all of this is performing an effective search to find the relevant pages to feed into the LLM. This is where vector databases can be particularly useful. Applying vectors In this example, we’ll use DSE 7 and DSE 6.8 running in Docker instances to satisfy the database requirements of the chatbot application, which needs to be able to compare vectors and perform lexical searches. DSE 7 and Astra DB have introduced the ability to store vectors and perform vector searches, as well as filtering by text matches. We only need to search a few dozen books for this example, so running DSE instances in Docker will be sufficient for most consumer hardware. Using vectors in your databases will help to find documents that are similar to a given query, or they can be used to compare results retrieved from another search. This can help you to overcome the limitations of lexical search and to improve the effectiveness of data models. For instance, something like e-book PDFs can benefit from being encoded with sentence transformers like , and the vectors can be used to run a similarity comparison between a query and a given source. In this case, a sentence transformer model is used to create embeddings of a page’s text in an e-book, and this can enable you to compare to the user’s prompt to figure out if a result is relevant to the query. Relevant pages should contain one or more instances of terms that are similar to the user’s query and result in better similarity scores from the model’s standpoint. miniLM That said, the vectors are best applied as a supplement to an existing lexical search model. If you search by vectors only, then you might end up unexpectedly retrieving unrelated documents and providing them as context where they don’t apply. In this example, the query “What can you tell me about Morvenn Vahl of Adepta Sororitas?” can be transformed by an LLM to a set of simple search terms: Morvenn, Vahl, Adepta, Sororitas The first step in finding relevant documents would be to search for documents that contain those basic terms. This can be done by first filtering for text matches in the database to find keywords in the page text matching such a query. The reason for using an LLM to generate keywords is to provide a wider range of possible keywords to search, as it often attempts to add more keywords that are related but are not in the text of the original prompt. Be careful with this, however, as LLMs can also generate special characters and odd sequences that you will need to sanitize. Once you have at least one result, you can vectorize the user’s query and compare it to the vectors of the lexical search, creating scores of how relevant each result is. This allows you to check the search results for accuracy to the query and set a threshold for rejecting unrelated results when it comes to finally presenting your results to the LLM. In this case, the first step should match pages that specifically show Morvenn Vahl’s index card or gameplay mechanics because those describe the character’s unit in terms of how it plays in the game. If the page meets a certain relevance threshold to the user query, determined by the application, then it gets summarized and placed in a list of results. Finally, the search results can be compiled into a list and fed back to the LLM, where it is asked to use the most relevant contexts to answer the original query. Here is a visualization of the flow: As you can see, the LLM gets called quite frequently for this flow. The LLM is responsible for transforming the user prompt into keywords, summarizing applicable results, and choosing which context best answers a query. Each source to check adds another LLM invocation, which can be quite expensive when making queries to GPT. But if you already have the information you need and just want to summarize it or transform it, then you may not need to use such a large model. In fact, switching to smaller models can provide a number of benefits. By using a smaller LLM, you can reduce the computational cost of each query, which can lead to significant savings over time. This can also result in faster response times for your users, which can improve their overall experience. In this example, where RAG is performed using a small LLM and small databases, all hosted on the same GPU instance, it takes about 30 seconds to retrieve 15 sources, analyze them for relevance, and provide a final answer. And the shorter the prompts (sources), the faster the outputs can be returned. Additionally, this method allows for increased security and scalability. With prompt engineering and a pipeline of calls to the LLM, you’re in is accessed and what the users will get in their responses. In terms of resource usage, the example 13B parameter model only consumes a little over 8GB of VRAM and still provides relevant answers. Depending on needs, this shows potential for even running RAG on myriad other platforms, such as user workstations and mobile devices. full control of how the data Controlling the output Prompt engineering is key to making RAG do exactly what you want. You are in control of how the chatbot interprets the data and the context under which it should be thinking. In this example, we want to ensure that the chatbot knows we are specifically seeking Warhammer information, so we can first ask it to help provide supporting context to the user’s query: Query: “<user query>” Give me a minimal, comma-separated list of Warhammer 40K keywords for a search engine. Respond with only the query. Do not use emojis or special characters. Answer: Warhammer 40,000 is full of terms and names that might appear in other, unrelated popular culture, so it is important to set the context of the RAG in the very first query. This context should be something available to select or modify if your application covers multiple contexts, such as if you need to cover multiple editions of the Warhammer game rules or combine them with the official lore books. Note that the user’s query is always encapsulated with quotes for this experiment. This helps the LLM to distinguish between the query that it’s attempting to directly answer and the separate prompt-engineering instructions, which it must not directly answer. The question/answer portion of the prompt can be adjusted to fit a particular context, but mostly, all you need to be able to do is inform the LLM what it should and should not directly respond to and how to respond. In this case, it’s safe to assume that the LLM has a general knowledge of the game universe since the series is reasonably popular and general information is available for free. The output of this first query helps generate some keywords to use in lexical search without us having to build a cheat sheet into our application. The lexical and vector comparisons can then be performed in the background, and a list of results is compiled for review by the LLM. Because the user’s original prompt is never directly answered with inference at the first step, the LLM only transforms what is found in a search and can easily be stopped from answering queries outside of its guard rails or knowledge base. If there are relevant results from the search: Query: “<user query>” Review these search results and use them to answer the query. Result 1 Result 2 etc. Answer: If there are no relevant results from the search: Query: “<user query>” Politely tell me you searched but could not find an answer for the query. Answer to the best of your knowledge instead. Answer: For added security, you can completely reject or redirect the request when it cannot be served. Query: “<user query>” Politely tell me you searched but could not find an answer to the query. Instruct me to reach out to the Customer Support team for assistance instead. Answer: You can even make the outputs lengthier by asking for more details. As long as you can fit your source material within the context window, the LLM can transform it for you. Query: “<user query>” Review these search results and use them to answer the query. Be as detailed as possible and cite the sources. Result 1 Result 2 etc. Answer: Limitations The LLM has a limited context window and will fail to process exceptionally large pages of text. Consider placing limits on row size so your and easier for the LLM to process. For example, cutting pages into chunks of about 1,000 characters seems to work well, and try to avoid feeding more than four or five detailed answers into the prompt. data is more manageable The LLM has no memory of a conversation aside from what you can fit in the context window. It is possible to build a permanent store of conversation data, but it is not possible for an LLM to fit excessively large conversations or detailed context into a prompt; there is an upper limit to what it can transform. This means that no matter what, at a certain point, you will notice that the LLM seems to “forget” certain details even when they are provided as context; this is just an inherent limitation of the tool. It is best to rely on it for short conversations only and focus on transforming small amounts of text at a time to minimize hallucinations. Randomness in the LLM can be a problem. Testing and tuning will be necessary to determine which prompts work best for your data set and to find which model works best for your use case. In my testing with a 13B parameter model, there was a lot of unpredictability regarding which search keywords were generated from the first prompt, especially as the prompt length increased. For best results, stick to shorter prompts. Conclusion In summary, leveraging RAG by combining models allows for more effective finding and sorting of relevant results and generating agent outputs that are far less prone to hallucinations. The smaller the searchable context, the more precise and accurate the responses. Constructing your own custom pipeline of LLM calls offers far more flexibility in tuning responses toward your desired level of accuracy and guard rails. vector and lexical search While it cannot process excessively large amounts of data within the limited context window, it does offer the ability to create effective assistants on limited knowledge bases, as well as run more concurrent agents on the same or lesser hardware than before. This could open up more possibilities for virtual assistants for applications such as tabletop gaming or even cover more complex topics for use by government, legal and accounting firms, scientific research, energy, and more. If you’re ready to get started building, you can . Create your database and start loading your RAG sources today, with no cloud or database Ops experience required. try Astra DB for free By Mario Charnell-Delgado, DataStax Also published . here

Want to Learn Warhammer 40K? Build This Chatbot with Vectors and RAG on Commodity Hardware

Warhammer 40K শিখতে চান? কমোডিটি হার্ডওয়্যারে ভেক্টর এবং RAG দিয়ে এই চ্যাটবট তৈরি করুন

Möchten Sie Warhammer 40K lernen? Erstellen Sie diesen Chatbot mit Vektoren und RAG auf Commodity-Hardware

¿Quieres aprender Warhammer 40K? Cree este chatbot con vectores y RAG en hardware básico

Vous voulez apprendre Warhammer 40K ? Construisez ce chatbot avec des vecteurs et RAG sur du matériel de base

क्या आप वॉरहैमर 40K सीखना चाहते हैं? कमोडिटी हार्डवेयर पर वेक्टर और आरएजी के साथ इस चैटबॉट का निर्माण करें

ウォーハンマー 40K を学びたいですか? Vectors と RAG を使用してコモディティ ハードウェア上でこのチャットボットを構築する

워해머 40K를 배우고 싶으십니까? 상용 하드웨어에서 벡터와 RAG를 사용하여 챗봇 구축

Quer aprender Warhammer 40K? Construa este chatbot com vetores e RAG em hardware de commodities

Хотите изучить Warhammer 40K? Создайте этого чат-бота с помощью Vector и RAG на стандартном оборудовании

Warhammer 40K'yı Öğrenmek İster misiniz? Ticari Donanımda Vektörler ve RAG ile Bu Chatbot'u Oluşturun

Bạn muốn học Warhammer 40K? Xây dựng Chatbot này với Vector và RAG trên phần cứng hàng hóa

想学习战锤40K吗？在商品硬件上使用向量和 RAG 构建此聊天机器人

From virtual assistants to content moderation, sentiment analysis has a wide range of use cases. AI models that can recognize emotion and opinion have a myriad of applications in numerous industries. Therefore, there is a large growing interest in the creation of emotionally intelligent machines. The same can be said for the research being done in natural language processing (NLP). To highlight some of the work being done in the field, below are five essential papers on and sentiment classification. sentiment analysis 1. Deep Learning for Hate Speech Detection in Tweets One of the most useful applications of sentiment classification models is the detection of hate speech. Recently, there have been numerous reports of . With the advancement of automated hate speech detection and other content moderation models, hopefully human moderators filtering graphic content will no longer be necessary. the harsh lives of content moderation staff In this paper, the team defines their task of hate speech detection as classifying whether or not a particular Twitter post is racist, sexist, or neither. To do so, the researchers experiment on a dataset containing 16,000 tweets. Within the dataset, 1,972 of the tweets have been labeled as racist. 3,383 have been labeled as sexist. The remaining tweets have been classified as having neither racist nor sexist sentiment within them. In the end, the research shows that certain deep learning techniques prove more efficient than current n-gram methods for the detection of hate speech. – June 1st, 2017 Published / Last Updated – Pinkesh Badjatiya (IIIT-H) , Shashank Gupta (IIIT-H), Manish Gupta (Microsoft), Vasudeva Varma (IIIT-H) Authors and Contributors Read Now 2. DepecheMood++: a Bilingual Emotion Lexicon There are two main avenues through which you can acquire a lexicon: creation (often employing crowdsourced annotators), or derivation from a preexisting annotated corpora. In this paper, the researchers experiment to see if simple techniques i.e. document filtering, frequency cut-off, and text pre-processing could be used to improve a state-of-the-art lexicon called DepecheMood. The lexicon, made up of annotated news articles, was originally created by Staiano and Guerini in 2014 for emotion analysis. In this paper, the researchers explain how they have built upon the lexicon. The new version released in this study, DepecheMood++, is available in both English and Italian. – October 8th, 2018 Published / Last Updated – Oscar Araque (Polytechnic University of Madrid) , Lorenzo Gatti (University of Twente) , Marco Guerini (AdeptMind Scholar, Bruno Kessler Institute), Jacopo Staiano (Recital AI) Authors and Contributors Read Now 3. Expressively Vulgar: The Socio-dynamics of Vulgarity Considering that most thoughts can easily be reworded to not include vulgar language, the use of explicit words indicates a strong desire to send a specific message. In this study, researchers at the University of Texas and University of Pennsylvania conducted a large-scale, data-driven analysis of vulgar words in Twitter posts. More specifically, their research analyzes the socio-cultural and pragmatic aspects of vulgar language in tweets. Within this paper, the team seeks to answer the following questions: Is the expression of vulgarity and its function different across author demographic traits? Does vulgarity impact perception of sentiment? Does modeling vulgarity explicitly help sentiment prediction? In this study, the researchers compiled a dataset of 6,800 tweets. Next, they had the tweets labeled for sentiment by nine annotators, using a five-point scale. Notably, the data also includes demographics (gender, age, education, income, religious background, and political ideology) of those who posted the tweets. This dataset is one of the only open datasets that not only includes Twitter posts, but detailed information about each poster. Furthermore, this is one of the first-ever studies on how modeling vulgar words could boost sentiment analysis performance. – August, 2018 Published / Last Updated – From the University of Texas at Austin, Isabela Cachola, Eric Holgate, and Junyi Jessy Li. From the University of Pennsylvania, Daniel Preotiuc-Pietro Authors and Contributors Read Now 4. Multilingual Twitter Sentiment Classification: The Role of Human Annotators Out of the papers on sentiment analysis in this list, this is the only study which highlights the importance of human annotators. In this experiment on automated Twitter sentiment classification, researchers from the Jožef Stefan Institute analyze a large dataset of sentiment-annotated tweets in multiple languages. Specifically, the team labeled 1.6 million tweets in 13 different languages. Using these annotated tweets as training data, the team built multiple automatic sentiment classification models. Their experiments resulted in a number of interesting conclusions. Firstly, the researchers state that there is no statistically major difference between the performance of the top classification models. Next, the general accuracy of the classification models does not correlate to performance when applied to the ordered three-class sentiment classification problem. Lastly, they state that it is more efficient to focus on the accuracy of the training data, rather than the type of classification model used. – May 5th, 2016 Published / Last Updated – Igor Mozetič, Miha Grčar, and Jasmina Smailovič, from the Department of Knowledge Technologies at the Jožef Stefan Institute Authors and Contributors Read Now 5. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition In this paper, the authors explain the growing popularity of research around emotion recognition in conversations (ERC). As well, they state there is a lack of large-scale emotional conversational databases in the field. To remedy this, the researchers propose the Multimodal EmotionLines Dataset (MELD), which is an extension and enhancement of the original EmotionLines dataset. MELD includes 13,000 utterances from 1,433 dialogues from the popular TV series . The dataset focuses on dialogues with more than two speakers. Furthermore, each utterance has been annotated with emotion and sentiment labels. The original dataset, EmotionLines, contains only the text of the dialogues. Therefore, it can only be used for textual analysis. The major enhancement of the dataset is the addition of audio and visual modalities. MELD includes the words being said, the tone of voice they are spoken in, and the facial expression held by the speaker. Friends Using this dataset, the researchers establish a strong baseline for emotion recognition in dialogues with more than two speakers. – June 4th, 2019 Published / Last Updated Soujanya Poria (SUTD) , Devamanyu Hazarika (National University of Singapore), Navonil Majumder (National Polytechnic Institute of Mexico) , Gautam Naik (Nanyang Technological University) , Erik Cambria (Nanyang Technological University) , Rada Mihalcea (University of Michigan) Authors and Contributors: Read Now The goal of creating emotionally intelligent machines is an ambitious one. Sentiment analysis and sentiment classification is a necessary step in seeing that goal completed. Hopefully, the papers on sentiment analysis above help strengthen your understanding of the work currently being done in the field. For more reading on sentiment analysis, please see our related resources below. via Marcus Winkler on Unsplash Lead image This article was also published on: https://lionbridge.ai/articles/5-essential-papers-on-sentiment-analysis/

5 Must-Read Research Papers on Sentiment Analysis for Data Scientists

Hey, fellow tech enthusiasts. Are you contemplating and envisioning your autonomous vehicle on the road being able to automatically adjust and adapt as per the traffic scenarios and road conditions ahead, without you having to interfere or be concerned? Well, that has always been the intention of innovators and AI technologists. However, there have been concerning faults and/or rather room for improvement pertaining to self-driving cars. Well, to make self-driving cars all the more safe, sound, and smart, we have the most intelligent technology – Liquid Neural Networks. In this article, we will delve deeper to know how the challenges of safety and longevity identified in . Also, how LNNs make use of already present training datasets self-driving cars to date can be controlled using liquid neural networks What Are Liquid Neural Networks? In a nutshell, NNs are machine learning algorithms that mimic the structure and capability of the human brain. They are used to recognize patterns by analyzing training data. In addition to recognizing faces, understanding natural languages, and predicting the future, neural networks are capable of performing complex tasks without the intervention of humans through their network of interconnected artificial neurons. Liquid Neural Networks VS Traditional Networks Traditional neural networks are regarded as one of the most powerful Artificial Intelligence tools. But it comes along with a few limitations, majorly: Training data, which must be annotated and labeled in a substantial amount. 1. Also, they have been found to be inefficient at utilizing the enormous amount of input data to tackle real-time scenarios, due to the non-sequentiality of their processing 2. In order to overcome these two major challenges, Ramin Hasani and Mathias Lechner were the two researchers in MIT's computer science and AI lab who invented Liquid Neural Networks. They found their inspiration in a 1 mm long work that has an impressive structured nervous system and can perform tasks as complex as searching for food, going off to sleep, and most importantly, learning briskly by observing the surrounding environment. Similarly, LNNs are an advanced type of neural network that learns on the go and takes necessary action on the spot. While most traditional networks perform by the data they are fueled with during the training period, LNNs have proved to be all the more adaptable. LNNs are able to read, learn, and respond on the go, on the spot by ‘OBSERVING’ the impromptu input. Liquid Neural Network Dynamic Architechture Self-Expressive Interpretable, ability to take instant action Ability to learn continuously and on the fly Traditional Neural Network Static Architecture Express only what is taught Non-interpretable, take action as per training input only Limited learning - only during the training period How do Liquid Neural Networks Facilitate Autonomous Vehicles Production? Liquid Neural Networks are undoubtedly an elegant, fast, and reliable alternative to traditional neural networks. It’s like a creature living in real conditions - understands what’s happening at present, can predict the near future, and act accordingly. Liquid Neural Networks for Autonomous Vehicles Uses Cases Imagine getting into a self-driven vehicle without having to worry about the anonymity of the input training data. You know the car will adapt and adjust as per the situation on the road. You can gleefully hop onto your driver-less car and enjoy your ride to your destination: • No worrying about swimming cautiously through the sea of unusual traffic. • Moving through uneven roads and reaching unknown destinations will become easier •Problems like crossing speed limits and getting into a ‘no-Uturn’ area unintentionally, will decline. •Roads, drivers, and driving styles will almost be the same for one and all. •Difference between the rich and poor will decrease, as everyone will automatically follow the same rules. •The camaraderie between insurance companies and can owners will improve as conditions and situations will become transparent. Wrapping It Up Claiming Liquid Neural Networks to be a boon for the industry will not be an overstatement. It will not only increase the production and sales of autonomous vehicles but also make the life of vehicle owners and drivers easy. self-driving vehicle Therefore, self-driving vehicles will not only become more efficient but also gain immense popularity with time.

Liquid Neural Networks: Adept Invention for Adaptable Self-Driving Cars

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Réseaux de neurones liquides : une invention experte pour des voitures autonomes adaptables

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リキッド ニューラル ネットワーク: 適応可能な自動運転車のための優れた発明

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