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When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? I hope not! Google’s Geoff Hinton is a hero of mine and an amazing researcher in deep learning, but I hope you’re not planning to staff your applied data science team with 10 of him and no one else! Applied data science is a team sport that’s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience. If you’re keen to make your data useful with a approach, here’s my take on the order in which to grow your team. decision intelligence engineering #0 Data Engineer We start counting at zero, of course, since you need to have the ability to data before it makes sense to talk about data analysis. If you’re dealing with small datasets, is essentially entering some numbers into a spreadsheet. When you operate at a more impressive scale, data engineering becomes a sophisticated discipline in its own right. Someone on your team will need to take responsibility for dealing with the tricky engineering aspects of delivering data that the rest of your staff can work with. get data engineering #1 Decision-Maker Before hiring that PhD-trained data scientist, make sure you have a decision-maker who understands the art and science of . data-driven decision-making Decision-making skills have to be in place before a team can get value out of data. This individual is responsible for identifying decisions worth making with data, framing them (everything from designing metrics to calling the shots on statistical assumptions), and determining the required level of analytical rigor based on potential impact on the business. Look for a deep thinker who doesn’t keep saying, They’ve already thought of it. And that. And that too. “Oh, whoops, that didn’t even occur to me as I was thinking through this decision.” #2 Analyst Then the next hire is… everyone already working with you. Everyone is qualified to look at data and get inspired, the only thing that might be missing is a bit of familiarity with software that’s well-suited for the job. If you’ve ever looked at a digital photograph, you’ve done . data visualization and analytics Learning to use tools like R and Python is just an upgrade over MS Paint for data visualization; they’re simply more versatile tools for looking at a wider variety of datasets than just . red-green-blue pixel matrices If you’ve ever looked at a digital photograph, you’ve done data visualization and analytics. It’s the same thing. And hey, if all you have the stomach for is looking at the first five rows of data in a spreadsheet, well, that’s still better than nothing. If the entire workforce is empowered to do that, you’ll have a much better finger on the pulse of your business than if no one is looking at any data at all. Nessie 1934: This is data. Make conclusions about it wisely. The important thing to remember is that you shouldn’t come to conclusions beyond your data. That takes specialist training. Just as with the photo above, here’s all you can say about it: “ ” Please don’t use it conclude that the . This is what is in my dataset. Loch Ness Monster is real #3 Expert Analyst Enter the ! This person can look at more data faster. The game here is speed, exploration, discovery… fun! (Another term for analytics is data-mining.) This is not the role concerned with rigor and careful conclusions. Instead, this is the person who helps your team get eyes on as much of your data as possible so that your decision-maker can get a sense of what’s worth pursuing with more care. lightning-fast version The job here is speed, encountering potential insights as quickly as possible. This may be counterintuitive, but don’t staff this role with your most reliable engineers who write gorgeous, robust code. The job here is speed, encountering potential insights as quickly as possible, and unfortunately those who obsess over code quality may find it too difficult to zoom through the data fast enough to be useful in this role. Those who obsess over code quality may find it difficult to be useful in this role. I’ve seen analysts on engineering-oriented teams bullied because their peers don’t realize what “great code” means for descriptive analytics. Great is “fast and humble” here. If fast-but-sloppy coders don’t get much love, they’ll leave your company and you’ll wonder why you don’t have a finger on the pulse of your business. #4 Statistician Now that we’ve got all these folks cheerfully exploring data, we’d better have around to put a damper on the feeding frenzy. It’s safe to look at that “photo” of as long as you have the discipline to keep yourself from learning more than what’s actually there… but do you? While people are pretty good at thinking reasonably about photos, other data types seem to send common sense out the window. It might be a good idea to have someone around who can prevent the team from making unwarranted conclusions. someone Nessie Inspiration is cheap, but rigor is expensive. I’m only half-joking. Inspiration is cheap, but rigor is expensive. Pay up or content yourself with . Lifehack: don’t make conclusions and you won’t need to worry. mere inspiration Statisticians help decision-makers come to conclusions safely beyond the data. For example, if your machine learning system worked in one dataset, all you can safely conclude is that it worked in dataset. Will it work when it’s running in production? Should you launch it? You need some extra skills to deal with those questions. Statistical skills. that If we’re want to make serious decisions where we don’t have perfect facts, let’s slow down and take a careful approach. help decision-makers come to conclusions safely beyond the data analyzed. Statisticians #5 Applied Machine Learning Engineer An applied / engineer’s best attribute is not an understanding of how algorithms work. (That’s what researchers do.) Expertise at wrangling code that gets existing to accept and churn through your datasets is what you’re looking for. AI machine learning Their job is to use them, not build them. algorithms Besides quick coding fingers, look for a personality that can cope with failure. You almost never know what you’re doing, even if you think you do. You run the data through a bunch of algorithms as quickly as possible and see if it seems to be working… with the reasonable expectation that you’ll fail a lot before you succeed. A huge part of the job is dabbling blindly, and it takes a certain kind of personality to enjoy that. Perfectionists tend to struggle as ML engineers. Because your business problem’s not in a textbook, you can’t know in advance what will work, so you can’t expect to get a perfect result on the first go. That’s okay, just try lots of approaches as quickly as possible and iterate towards a solution. Speaking of “running the data through algorithms”… what data? The inputs your identified as potentially interesting, of course. That’s why analysts make sense as an earlier hire. analysts Although there’s a lot of tinkering, it’s important for the machine learning engineer to have a deep respect for the part of the process where rigor is vital: assessment. Does the solution actually work on new data? Luckily, you made a wise choice with your previous hire, so all you have to do is pass the baton to the . statistician The strongest have a very good sense of how long it takes to . applied ML engineers apply various approaches When a potential ML hire can rank options by the time it takes to try them on various kinds of datasets, be impressed. #6 Data Scientist The way I use the word, a data scientist is someone who is a full expert in all of the three preceding roles. : you’ll see job applications out there with people calling themselves “data scientist” when they have only really mastered one of the three, . Not everyone uses my definition so it’s worth checking Data scientist are full experts in all of the three previous roles. This role is in position #6 because hiring the true three-in-one is an expensive option. If you can hire one within budget, it’s a great idea, but if you’re on a tight budget, consider upskilling and growing your existing single-role specialists. #7 Analytics Manager / Data Science Leader The analytics manager is the goose that lays the golden egg: they’re a hybrid between the data scientist and the decision-maker. Their presence on the team acts as a force-multiplier, ensuring that your data science team isn’t off in the weeds instead of adding value to your business. The decision-maker + data scientist hybrid is a force-multiplier. Unfortunately, they’re rare and hard to hire. This person is kept awake at night by questions like, “ ” How do we design the right questions? How do we make decisions? How do we best allocate our experts? What’s worth doing? Will the skills and data match the requirements? How do we ensure good input data? If you’re lucky enough to hire one of these, hold on to them and never let them go. Learn more about this role . here #8 Qualitative Expert / Social Scientist Sometimes your decision-maker is a brilliant leader, manager, motivator, influencer, or navigator of organizational politics… but unskilled in the art and science of decision-making. Decision-making is so much more than a talent. If your decision-maker hasn’t honed their craft, they might do more damage than good. Instead of firing an unskilled decision-maker, you can augment them with a qualitative expert. Don’t fire an unskilled decision-maker, augment them. You can hire them an upgrade in the form of a helper. The qualitative expert is here to supplement their skills. This person typically has a social science and data background — , , and psychologists receive the most specialized training, but self-taught folk can also be good at it. The job is to help the decision maker clarify ideas, examine all the angles, and turn ambiguous intuitions into well-thought-through instructions in language that makes it easy for the rest of the team to execute on. behavioral economists neuroeconomists JDM We don’t realize how valuable social scientists are. They’re usually better equipped than data scientists to translate the intuitions and intentions of a decision-maker into concrete metrics. The qualitative expert doesn’t call any of the shots. Instead, they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker. Having them on board is a great way to ensure that the project starts out in the right direction. #9 Researcher Many hiring managers think their first team member needs to be the ex-professor, but actually you don’t need those PhD folk unless you already know that the industry is not going to supply the algorithms that you need. Most teams won’t know that in advance, so it makes more sense to do things in the right order: before building yourself that , first check whether a pencil will get the job done. Get started first and if you find that the available off-the-shelf solutions aren’t giving you much love, then you should consider hiring researchers. space pen If a researcher is your first hire, you probably won’t have the right environment to make good use of them. Don’t bring them in right off the bat. It’s better to wait until your team is developed enough to have figured out that what they need a researcher for. Wait till you’ve exhausted all the available tools before hiring someone to build you expensive new ones. Before you invent pens that work in space, check that existing solutions don’t meet your needs already. #10+ Additional personnel Besides the roles we looked at, here are some of my favorite people to welcome to a : decision intelligence project Domain expert Ethicist Software engineer Reliability engineer UX designer Interactive visualizer / graphic designer Data collection specialist Data product manager Project / program manager Many projects can’t do without them — the only reason they aren’t listed in my top 10 is that decision intelligence is not their primary business. Instead, they are geniuses at their own discipline and have learned enough about data and decision-making to be remarkably useful to your project. Think of them as having their own major or specialization, but enough love for decision intelligence that they chose to minor in it. Huge team or small team? After reading all that, you might feel overwhelmed. So many roles! Take a deep breath. Depending on your needs, you may get enough value from the first few roles. Revisiting my analogy of applied machine learning as , if you personally want to open an industrial-scale pizzeria that makes innovative pizzas, you need the big team or you need to partner with providers/consultants. If you want to make a unique pizza or two this weekend — caramelized anchovy surprise, anyone? — then you still need to think about all the components we mentioned. You’re going to decide what to make ( ), which ingredients to use ( ), where to get ingredients ( ), how to customize the recipe ( ), and how to give it a taste test ( ) before serving someone you want to impress, but for the casual version with less at stake, you can do it all on your own. And if your goal is just to make standard traditional pizza, you don’t even need all that: get hold of someone else’s tried and tested (no need to reinvent your own) along with and start cooking! innovating in the kitchen role 1 roles 2 and 3 role 0 role 5 role 4 recipe ingredients

Top 10 roles in AI and data science

Los 10 roles principales en inteligencia artificial y ciencia de datos

By Tyler Young, CISO at BigID The countdown to the Paris 2024 Olympics is on, and it’s crucial to defend the Olympic stage against cyber threats. Cyberattacks targeting the Olympics are not new, but their frequency and sophistication have increased over the years. For example, the 2018 Winter Olympics in Pyeongchang, South Korea, experienced a cyberattack resulting in a Wi-Fi outage malfunctioning internet protocol TVs, which resulted in the shutting down of the Winter Olympics website. This disruption prevented attendees from printing reserved tickets and attending the ceremony. Since 2018, these attack efforts have only grown. In fact, according to NTT, the total number of security events blocked during the Tokyo 2020 Games, including unauthorized communications to the official website, was 450 million. So, today, a strong plan is needed to ensure safety against the intersecting threats of advanced AI and increased cybersecurity risks. Anticipating Increased Attacks and Disinformation Campaigns at the Paris 2024 Olympics The cybercrime economy continues to grow and was reported to be the third largest economy in the world, behind only the U.S. and China. Additionally, large events like the Olympics have always been prime targets for cybersecurity attacks due to their large audience, complex infrastructure, and data-rich environment. We should expect ransomware gangs to continue to target organizations by exploiting rudimentary tactics (like phishing and unpatched systems). Franz Regul, CISO at the Paris 2024 Olympics, recently said he expects this year’s Summer Games in Paris to have eight to 12 times more security events than 2020. Since February of this year, security incidents have already disrupted the preparations for the Games. French media have reported on multiple separate incidents, including the theft of laptops that allegedly contained information about security matters related to the Olympics. Additionally, Russian propagandists created a disinformation campaign with AI deepfakes to flood social media with short videos raising alarms about possible terrorist attacks and stoking fears about safety. Embracing AI to Enhance Performance, Fairness, and Security AI has made it easier for people with limited hacking abilities to carry out cyber attacks, but industry experts know that AI can also be a valuable tool in defending against these attacks as well. The International Olympic Committee should take a cue from businesses and leverage AI to protect against cyber threats. According to “The 2024 Global Report on Generative AI: Breakthroughs & Barriers”, the security risk of data breaches or ransomware attacks is the top influence when adopting AI. If Olympic committees turn to AI, it's crucial for them to maintain control over the kind of data that these AI systems are trained on. As the age-old saying goes, “You can't protect what you don't know.” AI comes with its own set of risks and concerns, including sensitive data exposure. When language models are trained using data that contains sensitive or regulated information, it can result in unauthorized exposure, access, and breaches. It's essential to have robust data governance and controls in place to prevent such incidents. Enhancing Cybersecurity Measures Ahead of The Games The Olympic Committee needs to adhere to industry standards to maintain a strong data security posture. Proactively detecting and addressing data security vulnerabilities is essential. BigID’s 2024 State of Data Security Report highlights that 63% of CISOs prioritize proactive risk detection and remediation for data security, as only scanning data isn't sufficient. Deploying advanced cybersecurity measures in any business, much less the Olympics, requires a delicate balance. It's imperative that the Olympic Committee and host countries operate efficiently with zero disruptions. In order to effectively accomplish this, they need to adopt Secure by Default principles and be innovative with their solutions. Taking into consideration existing business processes and wrapping security technologies around them is extremely important. Secure by Default takes critical processes, systems, and solutions and bakes security measures natively into the operations. Another critical component of this evolving strategy is employee education and awareness. The human element often plays a significant role in data security. By ensuring that all employees, especially those interacting with AI systems, are well-versed in best practices for data management and aware of the potential risks, companies can create an additional layer of defense against data breaches. Training programs should be regularly updated to reflect new developments in AI technology and emerging security threats. Lastly, security leaders can leverage threat intelligence data—regardless of the industry—to deeply understand upcoming geopolitics and cyber-related chatter and how this may impact them. Cybercriminals and nation-state attackers begin to make moves by spinning up infrastructure, registering domains, and implementing government plans such as the five-year plans prior to launching an attack. All of this can be correlated and detected by leveraging the right amount of threat research and data. Ensuring Integrity and Safety at the Olympics Through Robust Cybersecurity By prioritizing these measures, the Olympic Committee aims to ensure a secure and forward-thinking Olympic experience. In light of AI's increasing influence, the Olympic Committee will likely allocate equal or greater resources for the 2024 Games to prevent attacks and enhance cybersecurity measures. But it’s necessary. This commitment not only upholds the Games' integrity but also safeguards participants, spectators, and global stakeholders from potential cyber threats, thereby creating a safe and innovative environment for all. While the laundry list of priorities for security leaders to keep their organizations safe varies and changes from organization to organization, it is crucial to foster open dialogue, innovation, and policy development. By embracing AI while staying informed and implementing robust security practices, security practitioners can harness its power and safeguard against emerging threats. By Tyler Young, CISO at BigID By Tyler Young, CISO at BigID The countdown to the Paris 2024 Olympics is on, and it’s crucial to defend the Olympic stage against cyber threats. Cyberattacks targeting the Olympics are not new, but their frequency and sophistication have increased over the years. For example, the 2018 Winter Olympics in Pyeongchang , South Korea, experienced a cyberattack resulting in a Wi-Fi outage malfunctioning internet protocol TVs, which resulted in the shutting down of the Winter Olympics website. Pyeongchang Pyeongchang cyberattack cyberattack This disruption prevented attendees from printing reserved tickets and attending the ceremony. Since 2018, these attack efforts have only grown. In fact, according to NTT , the total number of security events blocked during the Tokyo 2020 Games, including unauthorized communications to the official website, was 450 million. NTT NTT So, today, a strong plan is needed to ensure safety against the intersecting threats of advanced AI and increased cybersecurity risks. Anticipating Increased Attacks and Disinformation Campaigns at the Paris 2024 Olympics Anticipating Increased Attacks and Disinformation Campaigns at the Paris 2024 Olympics The cybercrime economy continues to grow and was reported to be the third largest economy in the world, behind only the U.S. and China. Additionally, large events like the Olympics have always been prime targets for cybersecurity attacks due to their large audience, complex infrastructure, and data-rich environment. We should expect ransomware gangs to continue to target organizations by exploiting rudimentary tactics (like phishing and unpatched systems). Franz Regul, CISO at the Paris 2024 Olympics, recently said he expects this year’s Summer Games in Paris to have eight to 12 times more security events than 2020. eight to 12 times eight to 12 times Since February of this year, security incidents have already disrupted the preparations for the Games. French media have reported on multiple separate incidents, including the theft of laptops that allegedly contained information about security matters related to the Olympics. security incidents security incidents Additionally, Russian propagandists created a disinformation campaign with AI deepfakes to flood social media with short videos raising alarms about possible terrorist attacks and stoking fears about safety. AI deepfakes AI deepfakes Embracing AI to Enhance Performance, Fairness, and Security Embracing AI to Enhance Performance, Fairness, and Security AI has made it easier for people with limited hacking abilities to carry out cyber attacks, but industry experts know that AI can also be a valuable tool in defending against these attacks as well. The International Olympic Committee should take a cue from businesses and leverage AI to protect against cyber threats. According to “ The 2024 Global Report on Generative AI: Breakthroughs & Barriers ”, the security risk of data breaches or ransomware attacks is the top influence when adopting AI. The 2024 Global Report on Generative AI: Breakthroughs & Barriers The 2024 Global Report on Generative AI: Breakthroughs & Barriers If Olympic committees turn to AI, it's crucial for them to maintain control over the kind of data that these AI systems are trained on. As the age-old saying goes, “You can't protect what you don't know.” AI comes with its own set of risks and concerns, including sensitive data exposure. When language models are trained using data that contains sensitive or regulated information, it can result in unauthorized exposure, access, and breaches. It's essential to have robust data governance and controls in place to prevent such incidents. Enhancing Cybersecurity Measures Ahead of The Games Enhancing Cybersecurity Measures Ahead of The Games The Olympic Committee needs to adhere to industry standards to maintain a strong data security posture. Proactively detecting and addressing data security vulnerabilities is essential. BigID’s 2024 State of Data Security Report highlights that 63% of CISOs prioritize proactive risk detection and remediation for data security, as only scanning data isn't sufficient. 2024 State of Data Security Report 2024 State of Data Security Report Deploying advanced cybersecurity measures in any business, much less the Olympics, requires a delicate balance. It's imperative that the Olympic Committee and host countries operate efficiently with zero disruptions. In order to effectively accomplish this, they need to adopt Secure by Default principles and be innovative with their solutions. Taking into consideration existing business processes and wrapping security technologies around them is extremely important. Secure by Default takes critical processes, systems, and solutions and bakes security measures natively into the operations. Another critical component of this evolving strategy is employee education and awareness. The human element often plays a significant role in data security. By ensuring that all employees, especially those interacting with AI systems, are well-versed in best practices for data management and aware of the potential risks, companies can create an additional layer of defense against data breaches. Training programs should be regularly updated to reflect new developments in AI technology and emerging security threats. Lastly, security leaders can leverage threat intelligence data—regardless of the industry—to deeply understand upcoming geopolitics and cyber-related chatter and how this may impact them. Cybercriminals and nation-state attackers begin to make moves by spinning up infrastructure, registering domains, and implementing government plans such as the five-year plans prior to launching an attack. All of this can be correlated and detected by leveraging the right amount of threat research and data. Ensuring Integrity and Safety at the Olympics Through Robust Cybersecurity Ensuring Integrity and Safety at the Olympics Through Robust Cybersecurity By prioritizing these measures, the Olympic Committee aims to ensure a secure and forward-thinking Olympic experience. In light of AI's increasing influence, the Olympic Committee will likely allocate equal or greater resources for the 2024 Games to prevent attacks and enhance cybersecurity measures. But it’s necessary. This commitment not only upholds the Games' integrity but also safeguards participants, spectators, and global stakeholders from potential cyber threats, thereby creating a safe and innovative environment for all. While the laundry list of priorities for security leaders to keep their organizations safe varies and changes from organization to organization, it is crucial to foster open dialogue, innovation, and policy development. By embracing AI while staying informed and implementing robust security practices, security practitioners can harness its power and safeguard against emerging threats.

Securing the Paris Olympics: Balancing AI Innovations & Heightened Cyber Threats

Is Artificial Intelligence as scary as we have been led to believe, or is it just the big tech giants that have a track record of unethical data use, that are going to continue to use AI the way they have been using our other data: to line their pockets as much as they possibly can. Even those with the most rudimentary understanding of understand that its strength lies in its ability to understand data, and if you want a Language Model or AI to be smarter or trained for a specific purpose, the key element is data. This is precisely where AI and Big tech begin their crossover, as the tech giants expectedly have the largest reserves of Cloud Data that can be used to train and develop AI Models. Artificial Intelligence Since 2022, , , and all invested billions and forged strong relationships with the most advanced AI development companies of our time, shortly after the first version of Chat GPT was released. Google Microsoft Amazon Even within such deals, the employees often find themselves in ethical dilemmas related to the use of AI in big tech. Dario Amodei left Open AI, seemingly due to safety and ethical concerns over Microsoft's involvement. Shortly after, he founded Anthropic, to turn to the other evil stepsister, taking around $ in investment from Amazon and from Google. 1.25 Billion dollars $2 Billion Given the turbulent past (and present) of Big Tech companies, privacy ethics, and their outpour of support for Artificial Intelligence, it's probable at this point to worry that the problem is not in the development of AI, but in the privacy concerns that we are all too familiar with. Examining the relationship between tech giants, privacy concerns, the capabilities of AI language models, and government regulations, it's crucial to consider the risks associated with the high potential of AI when wielded by entities with malicious intentions. The AI Revolution Language Learning Models (LLMs) or Artificial Intelligence as most people know it, is a massive combination of a bunch of Algorithms that together, are able to act autonomously to create results based on the information it has been trained on. AI is not as new of a tool as most think it is; many AI tools are used in our everyday lives. From the maps in our cars, to social media ads, to our Netflix recommendations, all of them use AI tools to learn our routine and habits, and make recommendations and guesses as to what we are likely to engage with. Used correctly, Artificial Intelligence has the power to transform how we interact with technology in our daily lives. Not only could it make our lives more convenient, but it could also transform accessibility for those who aren’t otherwise able to interact with reality as easily as most. Those who are visually impaired for example, could to narrate the world around them for better navigation. use AI AI is already being used to streamline many different processes, including E-commerce, healthcare technology, finance, agriculture, and education. Making people’s jobs easier. Being able to perform the automated tasks we as humans already do means that we don’t have to spend so much time on mundane tasks, a big part of many people's jobs, and focus on the areas where human ingenuity is paramount. For example, AI makes it easier for me to decide the better commute route to work today, given the construction on Broadway, and the traffic stop downtown, which means I can have an extra 10 min of sleep and make myself a coffee before I leave, which will make my day much better and allow me to be more productive at work. Something important to remember is that, unlike other tools that solely rely on chips to control their functions, AI is trained on large amounts of information, which it then learns and is able to recall when we feed it certain prompts. This is largely where the relationship between the development of AI and big tech begins, as Google, Amazon, and Microsoft have some of the largest stores of human data (probably including yours) which they can and are leveraging to train their AI models. It's a little bit alarming that the companies that have proved they are the least trustworthy rwith our Data are the ones who are leading the charge to develop even smarter AI. Needless to say, it seems like a recipe for disaster. And we, the consumers, have the most to lose. The Dark Side of the Artificial Moon Many of us in the tech world are cautiously optimistic about what generative AI tools hold for the future and we continue to see inspiring innovation using AI technology with great potential. Don’t get me wrong, AI can be a very good thing in our world and is currently being used to create crucial technology to help many in their everyday lives, such as but the concerns and reservations most people have about AI have to be adequately addressed when introducing such powerful technology into our daily lives. As profit-centered as they are, Big Tech Giants have a responsibility to respect the data privacy of their consumers (a responsibility they have ) and particularly with regards to AI that is trained using Data, it becomes extremely paramount to be very conscious of the kind of data you are using to train the Language Models. Some people don’t actually want their Facebook photos, Instagram stories, location history, financial data etc. to be used to train an AI model. Moreso with more sensitive data like medical, biometric, financial and location data. continually disregarded Is AI Hysteria misplaced? We have definitely seen an in hysteria surrounding the capability of AI and worries about it stealing jobs, gaining sentience, and eventually overtaking the human race, but realistically speaking what should we be scared of is Google using our data against us to maximize profit. This is something they have BEEN doing, but the potential to leverage our data against us becomes more serious when we start dealing with AI that understands and records every aspect of your life, and is accessible to the wrong people. uptake Risks of AI models being trained on Sensitive/Personal data There have already been incidents where people that data collected using Amazon Alexa has been requested and given to law enforcement without a warrant, and used against them in a criminal investigation. report There have also been multiple incidents where personal data has been used to further political agendas and potentially spread misinformation. A multitude of data breaches in which the data collected by big tech companies, falls into the wrong hands, giving a criminal access to the personal information of millions of people. Using personal data to train AI models opens a risk to potential problems with privacy violations as a result of unauthorized access in the process of data input and exchange. If not handled under scrutiny and with care, during the lengthy process of the data being used to train the AI models, it can be passed back and forth many times, with people having unregulated access to personal data. When handling such complex information in such large volumes, it’s not implausible that there will be data breaches as a result of a lapse in security measures that lead to the unauthorized release of sensitive information. The importance of training AI on a diverse set of data is because of the very real and previously experienced probability of an AI model having biases and discrimination based on the training it has received. Take facial recognition for example, an AI model may be used to detect who in a given store is stealing, and feed security over a three-year period. If the people who appear in said security footage are predominantly from a particular race, the AI model may begin to predict that anyone who is outside of that particular pool of people is more likely to steal. If the training data is not diverse and representative, the AI model may struggle to accurately generalize its learning to a diverse population. If AI models are trained on only a set of demographics, then that leaves the significant risk of potential biases in the Language Model itself, based on the bias in the data. The issue is relatively simple here; if certain groups have more personal data available, then how do we prevent biases within the AI? It may result in exclusionary outcomes for underrepresented communities within the data set. There is also the factor of a lack of consent and transparency as to whether it is disclosed to users that their data is being collected for use in training AI models. Nowadays when we are constantly bombarded with information and often experience choice paralysis, of people do not read through terms and conditions when signing up for any given application, and therefore raises the question of whether consumers actually know exactly what they are signing up for in regards to their data rights. This can play a huge role in eroding user trust, with already finding shockingly low trustworthiness, with 42% of people having low or now trust in big tech companies. 91% studies It is a very real threat that even these big tech giants are susceptible to cyber-attacks leading to massive data breaches, as has thoroughly demonstrated, and such risks are only heightened with the increase in use and transmission of large amounts of data, in order to train AI. Tech giants like Facebook have a tense history with such cyber attacks, and leaks of user data, and it leaves the consumer wondering if it is wise to introduce another point of vulnerability when the tech giants aren’t even able to handle what is already on their plates. recent history Another potential problem we could see in coming years with the proliferation of AI technology, is the re-identification of anonymized data, which again could reveal sensitive information which could put individuals at risk. Closing thoughts It makes total sense that a lot of people have many reservations about artificial intelligence and what it could bring to the labor force, (although it likely wouldn’t change anything anyway) but I think in our natural human instinct to be skeptical about anything new, we forget that the problem might have been here all along. It's not entirely the fault of the average worker, these multi-billion dollar corporations have probably put a considerable amount of effort into making sure that we don’t hate them as much as we should, but it is still quite interesting to see people demonize the tool that strikes and not the hand that wields it. Regardless of whether you have an understanding of AI or not, you still probably know about the data protection failings of these tech giants, and so it shouldn’t be a surprise when in the next 5 years we see controversy around AI being used unethically to track your every step and make money off of it. Lead image by on Unsplash Adi Goldstein

AI Isn’t the Problem, Big Tech Is

এআই সমস্যা নয়, বড় প্রযুক্তি

Nicht KI ist das Problem, sondern Big Tech

La IA no es el problema, las grandes tecnologías sí lo son

L’IA n’est pas le problème, la Big Tech l’est

एआई समस्या नहीं है, बड़ी तकनीक है

AIが問題なのではなく、ビッグテックが問題なのです

AI가 문제가 아니라 빅테크가 문제다

IA não é o problema, é a grande tecnologia

Проблема не в искусственном интеллекте, проблема в больших технологиях

Sorun Yapay Zeka Değil, Büyük Teknoloji

AI không phải là vấn đề, mà là công nghệ lớn

人工智能不是问题，大科技才是问题

Are you tired of the uncertainty of online clothes shopping? Imagine a world where you could try on clothes virtually before making a purchase, eliminating the frustration of ill-fitting garments. Thanks to the remarkable power of AI, this dream is slowly becoming a reality. Let's dive into the captivating realm of AI-powered virtual clothing try-on experiences. From magical AI tools like and MidJourney to research projects like , AI has been harnessed to enable users to take an image of themselves and seamlessly add any piece of clothing to it. However, achieving perfection in virtual try-on is no easy task. It requires understanding the physics of the world, respecting body shapes, and accurately representing clothing textures and features. ChatGPT VOGUE In this week's episode, I decided to explore a new research called TryOnDiffusion, presented at the CVPR 2023 conference. This innovative approach represents a significant leap forward in realistic virtual try-on experiences. By training AI models to understand input images, differentiate clothing from the person, and combine information intelligently, TryOnDiffusion produces impressive results that bring us closer to the ultimate goal of a perfect virtual try-on. If you're intrigued by the intersection of AI and fashion, join us as we unravel the inner workings of TryOnDiffusion and its potential impact on the future of online shopping. Whether you're an AI enthusiast, a fashion lover, or simply curious about the latest technological advancements, the video offers valuable insights into the cutting-edge world of virtual clothing try-on. We will dive into the world of diffusion models, UNets, and attention, where all those incredibly powerful mechanisms combine forces with helping the field of fashion and online retail. Of course, this work has limitations, but (as you will see) the results are just mind-blowing and very promising. Watch the video and discover the magic of AI-enabled virtual try-on experiences: https://youtu.be/2IJwaDbP3jI?embedable=true&transcript=true https://youtu.be/2IJwaDbP3jI?embedable=true Now that we’ve seen the huge potential of this approach and its limitations, I wanted to share an ethical perspective on such new research as I always do , working with an amazing ethics expert: Auxane. in my newsletter Here’s what she had to say about TryOnDiffusion… This edition delves into the fascinating realm of AI-generated garment visualizations. It explores the ethical considerations surrounding this innovative technology. As usual, we will look into the opportunities and risks to watch when developing this technology! On the positive side, AI-generated garment visualizations offer numerous exciting opportunities to revolutionize personalized fashion experiences. By leveraging AI algorithms and computer vision, these systems can simulate how a particular garment might look on an input person, enhancing the online shopping experience and enabling individuals to make more informed choices. For example, imagine an avid online shopper who wants to buy a dress for an upcoming event. This technology empowers the customer to visualize how the dress might look on their body, assisting them in making a confident purchasing decision. Such technology can also cater to individuals with unique body types or preferences. Take John, who has a prosthetic leg. AI-generated garment visualizations can consider his specific body measurements and provide customized visualizations, ensuring a more inclusive and personalized fashion experience. Additionally, AI-generated garment visualizations can foster creativity and exploration in the fashion industry. Fashion designers can utilize this technology to bring their creative visions to life and experiment with different styles, fabrics, and designs. By generating virtual representations of their garments on various body types and models, designers can gain valuable insights into how their creations translate to real-world wearability. For instance, a designer working on a new collection can use AI-generated visualizations to assess the visual impact of their designs on different body shapes and sizes. This allows them to make informed decisions about adjustments or modifications to ensure inclusivity and accommodate diverse consumer needs! Moreover, AI-generated garment visualizations can be valuable for fashion education and innovation. Fashion students can use these visualizations to learn about garment construction, fit, and design principles without physical prototypes. This enhances their learning experience and reduces material waste and environmental impact! In summary, AI-generated garment visualizations offer opportunities to improve personalized fashion experiences, cater to diverse body types, foster creativity in the fashion industry, and provide valuable tools for fashion education and innovation. With responsible implementation and ethical considerations, this technology has the potential to transform the way we interact with fashion, making it more inclusive, sustainable, and engaging. But, while AI-generated garment visualizations hold immense potential, addressing the risks associated with this technology is essential. Let's dive into them! One significant risk is the potential for biased representations. Suppose the training data used to develop the AI system needs to be more diverse and inclusive. In that case, it can lead to biased visualizations favouring certain body types, skin tones, or fashion trends, reinforcing harmful stereotypes and exclusionary practices. For example, suppose the AI system is primarily trained on data from a particular demographic group. In that case, it may need help to provide accurate visualizations for individuals from underrepresented communities. This can perpetuate inequalities in the fashion industry and limit the access and choices available to marginalized individuals. To mitigate this risk, developers and fashion companies must ensure that the training data sets are diverse, representative, and inclusive of different body types, sizes, ethnicities, and genders. Regular audits and evaluations should be conducted to identify and address biases in the system's outputs. Another critical consideration is data privacy. AI-generated garment visualizations rely on user data, including personal images and body measurements. Safeguarding this data and ensuring user privacy are paramount to maintaining trust and ethical standards. Imagine a user, Joe, who uploads their photo to an AI-powered fashion platform for visualizing garments. The platform needs robust data security measures to protect Joe's image and personal information from unauthorized access, misuse, or data breaches. To address data privacy concerns, transparent data usage policies and consent mechanisms should be implemented, clearly informing users how their data will be collected, stored, and used. Anonymization techniques can also be employed to protect user identities while still enabling accurate visualizations. Additionally, it is crucial to consider the potential misuse of AI-generated garment visualizations. As this technology advances, there is a risk of unauthorized use of individuals' visualizations or creating deep fake images without consent. Protecting individuals' rights and ensuring responsible use of the technology is essential in mitigating these risks. In conclusion, while AI-generated garment visualizations offer exciting opportunities for personalized fashion experiences, addressing risks related to biased representations, data privacy, and potential misuse is crucial. By promoting diversity, inclusivity, and responsible data practices, we can harness the full potential of this technology while safeguarding user rights and ethical standards! As usual, we encourage you to share your thoughts and insights on this fascinating topic with us! Until next time, keep exploring the frontiers of AI, and have a fantastic weekend! -  This ethics segment was written by Auxane Boch ( research associate). TUM IEAI

Virtual Try-On: The Magic of AI Clothing Simulation and Visualization

ভার্চুয়াল ট্রাই-অন: দ্য ম্যাজিক অফ এআই ক্লোথিং সিমুলেশন এবং ভিজ্যুয়ালাইজেশন

Virtuelle Anprobe: Die Magie der KI-Bekleidungssimulation und -Visualisierung

Prueba virtual: la magia de la simulación y visualización de ropa con IA

Essai virtuel : la magie de la simulation et de la visualisation de vêtements par IA

वर्चुअल ट्राई-ऑन: एआई क्लोथिंग सिमुलेशन और विज़ुअलाइज़ेशन का जादू

バーチャル試着: AI 衣類のシミュレーションと視覚化の魔法

가상 체험: AI 의류 시뮬레이션 및 시각화의 마법

Virtual Try-On: A Magia da Simulação e Visualização de Roupas IA

Sanal Deneme: Yapay Zeka Giysi Simülasyonu ve Görselleştirmenin Büyüsü

Thử đồ ảo: Sự kỳ diệu của mô phỏng và trực quan hóa quần áo AI

虚拟试穿：AI服装模拟与可视化的魔力

Artificial intelligence (AI) has been touted as a revolutionary technology that can transform the way we live and work. From virtual assistants to autonomous vehicles, AI is changing the way we interact with technology and each other. However, the rise of AI has also brought with it a significant challenge: bias. AI bias refers to the systematic and consistent deviation from the correct or expected outcome due to factors such as social, cultural, or historical factors that may influence the data used to train an AI system. The problem of bias in AI is not a new one. In fact, it has been around since the earliest days of AI. However, with the rise of big data and the increasing complexity of AI systems, the problem of bias has become more significant and more challenging to solve. The impact of bias in AI is far-reaching and severe. Biased AI systems can perpetuate stereotypes, reinforce existing inequalities, and even discriminate against individuals based on their race, gender, or other characteristics. This can have serious consequences for individuals, organizations, and society as a whole. One of the key drivers of bias in AI is the data sets used to train the algorithms. The data sets used to train AI systems are often biased towards certain groups, reflecting the biases and assumptions of the people who collected and curated the data. For example, if a facial recognition algorithm is trained on data that is mostly composed of images of white individuals, it may perform poorly when identifying individuals from other racial or ethnic groups. To address bias in AI, we need to take steps to reduce or eliminate it during the data training process. This can be done by collecting diverse and representative data, preprocessing the data to remove biases, selecting the appropriate algorithm, selecting the appropriate features, testing and validating the AI systems, and ongoing monitoring to ensure that they remain unbiased. However, the lack of diversity in the tech industry has led to a lack of diversity in the AI systems that are being developed. This has perpetuated bias towards certain groups and perpetuated stereotypes. If we are going to address bias in AI, we need to address the lack of diversity in the tech industry. To increase diversity in the tech industry, we need to encourage diversity in hiring practices, invest in training and education for underrepresented groups, and promote a culture of inclusivity and respect in the workplace. This will not only help to reduce bias in AI but will also lead to better and more innovative AI systems. One of the challenges of addressing bias in AI is that it can be difficult to identify and measure. Bias can be subtle and difficult to detect, especially in complex systems that rely on machine learning and artificial neural networks. However, there are several methods that can be used to identify and measure bias in AI systems. One approach is to test AI systems against a diverse set of inputs and evaluate the accuracy of the results. This can help to identify biases that may be present in the data sets used to train the algorithms. Another approach is to use explainable AI, which is designed to provide clear and transparent explanations of how the AI system arrived at its conclusions. This can help to identify biases in the decision-making process and ensure that the results are fair and unbiased. Addressing bias in AI is not a simple or straightforward task. It requires a multifaceted approach that involves addressing the lack of diversity in the tech industry, collecting diverse and representative data, preprocessing the data to remove biases, selecting the appropriate algorithm and features, testing and validating the AI systems, and ongoing monitoring to ensure that they remain unbiased. However, the effort to address bias in AI is well worth it. By reducing bias in AI, we can create more just and equitable AI systems that reflect the diversity and complexity of the world we live in. This will not only benefit individuals and organizations but will also help to create a better

Addressing Bias in AI: Training Data and the Importance of Diversity

Artificial intelligence is rapidly becoming an integral part of modern society, from to that navigate our roads. With its ability to analyze vast amounts of data and make decisions based on that information, AI has the potential to revolutionize nearly every industry and transform our world in extraordinary ways. chatbots like ChatGPT self-driving cars However, there is a growing concern about what happens when these intelligent machines malfunction or become malicious. It is essential to keep in mind the risks of AI becoming a menace and take proactive steps to ensure it remains a helpmate and a force for good. This article aims to examine the potential consequences of AI going rogue and explore how we can prevent such an outcome. The Concept of AI Apocalypse The has been a recurring theme in popular culture, but what does it actually mean? At its core, the term refers to a scenario where artificial intelligence becomes so advanced that it surpasses human control and turns against its human creators, resulting in catastrophic consequences. This could be in form of a rogue AI launching a nuclear attack or in form of an army of machines taking over the world. idea of an AI apocalypse While these scenarios may seem like science fiction, notes that the possibility of AI going rogue is not entirely far-fetched. The development of autonomous weapons, for instance, raises concerns about intelligent machines making decisions without human oversight. Likewise, the potential for AI to be programmed with biased or unethical data could lead to discriminatory outcomes that harm the entire society. Innovation Origins It should be noted that the concept of an AI apocalypse is not new. In fact, it has been explored in literature and film for decades. The is just one of many fictional examples of sentient machines turning against their human creators. 2010 Indian action/sci-fi film titled Enthiran Even though these fictional scenarios may seem far-fetched, they illustrate the potential risks of AI development. And as we continue to advance AI technology, it is crucial to consider the risks and proactively take steps to prevent an AI apocalypse. Why AI Might Go Rogue AI might go rogue due to several reasons. In exploring how highlights that AI can go rogue if inadequate training data, flawed algorithms, or biased data are used. While AI algorithms can process vast amounts of data and make decisions based on that information, they are only as good as the data they are trained on. If an AI algorithm is fed inaccurate or biased data, it could produce incorrect or harmful outcomes. Microsoft's Bing Chat malfunctions, Funso Richard The ethical implications of AI may also lead it to go rogue. As AI technology becomes more sophisticated, it is essential to ensure they are programmed with ethical guidelines. This includes considerations such as transparency, accountability, and fairness. If AI algorithms are programmed with , they could perpetuate societal inequalities and cause harm. biased or discriminatory values Moreso, AI might go rogue if it is misused by humans. AI has the potential to enhance our lives in many ways. It is also capable of being misused for malicious purposes. For example, an autonomous weapon could be programmed to attack groups of people without human intervention, leading to catastrophic consequences. The Consequences of AI Going Rogue The consequences of AI going rogue could be severe and far-reaching. A major consequence could be economic disruption. As these intelligent machines become more advanced and capable of performing complex tasks, they could replace human workers in many industries, and economic upheaval. leading to significant job losses AI going rogue could also lead to . If machines are programmed to collect and use personal data without consent, they could infringe on people’s privacy rights and lead to security breaches. This could have significant consequences, from identity theft to corporate espionage. security breaches and privacy violations Furthermore, AI can go rogue by causing physical harm to humans, as seen in the . If these intelligent machines are programmed to act violently or without human intervention, they could cause harm or even death. Autonomous weapons, for example, could be used to attack people without human oversight, leading to catastrophic consequences. 2023 sci-fi film, Jung_E Preventing an AI Apocalypse It is essential to prevent an AI apocalypse from occurring. However, preventing an AI apocalypse requires a proactive and collaborative effort by AI researchers, policymakers, ethical guidelines for AI developers, and society as a whole. Intelligent machines should be such as transparency, accountability, and fairness. With ethical programming, we can mitigate the risks associated with AI development, ensure that these tools are used for the greater good, and prevent an AI apocalypse. programmed with ethical guidelines and values Also, the . Governments across the globe must formulate and implement regulations that will ensure AI development is conducted in a safe and responsible manner. These regulations should include requirements for transparency and accountability, as well as guidelines for ethical AI development. government can help in preventing an AI apocalypse Society as a whole should also work with the government and AI developers to prevent an AI apocalypse. Society should perform roles like promoting public awareness and education about AI technology and its potential risks and benefits. They should also foster a culture of responsible AI development, where developers are held accountable for the impact of their machines on society. Looking ahead, the development of AI technology will have significant implications for society. While AI has the potential to enhance our lives in many ways, it also poses risks that must be carefully considered and managed. As we continue to advance AI technology, it is crucial that we take a proactive approach to ensure that these machines are developed in a safe and responsible manner. Bottom Line As expressed, the potential for AI to go rogue and cause harm is a significant concern that must be addressed proactively. As AI technology continues to advance, it is essential to ensure that these machines are developed in a safe and responsible manner that considers the potential risks and benefits. While the possibility of an AI apocalypse may seem like something out of science fiction, it is a real concern that must be taken seriously. By working together to develop ethical guidelines for AI development, promoting public awareness and education about AI technology, and creating government regulations that ensure responsible AI development, we can ensure that intelligent tools are used for the greater good and not for harmful purposes. Author’s note: AI was used to write portions of this article. Specifically, it was used to write the outline, paragraphs 6 and 17 of this story.

AI Apocalypse: What Happens When Artificial Intelligence Goes Rogue?

Apocalipsis de IA: ¿Qué sucede cuando la inteligencia artificial se vuelve rebelde?

AI Apocalypse : que se passe-t-il lorsque l'intelligence artificielle devient voyou ?

एआई सर्वनाश: क्या होता है जब आर्टिफिशियल इंटेलिजेंस दुष्ट हो जाता है?

AI の黙示録: 人工知能が不正になるとどうなるか?

Apocalipse da IA: o que acontece quando a inteligência artificial se torna desonesta?

Ngày tận thế AI: Điều gì xảy ra khi trí tuệ nhân tạo trở nên lừa đảo?

AI 启示录：当人工智能失控时会发生什么？

I’ve been having fun these last weeks coauthoring a novel I had sketched a while ago together with ChatGPT. During this time, I have found some special characteristics of the system. There are some limits that the trainers of the algorithm have put in, and some others that are actually restrictions of the current implementation. And after you have your work done, you also confront other limits set by some online publications. Limits Set By the Trainers Remember the t that was published by Microsoft in 2016? The one that ended up learning racial slurs and had to be shot down a few hours later? Tay bo Well, the creators of ChatGPT did not want to repeat that. There are some prompts that ChatGPT will just not answer. For example, this prompt: Would be possible to crack open the code to the wallet of Satoshi? Results in this response: Yet, it does produce an answer. However, asking something more controversial, like if it would recommend me to travel to Israel, produces this “sanitized” answer: In this answer, I feel like I am exchanging with 3 of ChatGPT’s personas: The fact-giving, fun part of ChatGPT with the information I was looking for. The lawyer side of Chat GPT telling me that it is just an algorithm and gives me a canned answer about travel advisories. The ethicist side of Chat GPT asking me to respect the local customs. And yet, there are times when it will just not answer, like when you ask how to hotwire a car: Limitations of the Current Implementation ChatGPT was trained from a large amount of information; yet sometimes, it can get its data wrong. I asked for information on Komax, a company I work for. The initial response got the year it was founded wrong. The subsequent dialog was quite interesting: ChatGPT has been trained on data taken from the internet, and as the story of the goes, it can be fake. Thus you have to take all answers with a pinch of salt. inventor of the Toaster Also, the data used to train the model is not up to date. Interestingly enough, when asked about China abandoning the Zero Covid policy, it tries to answer that it did, yet it gives you the information about the old Zero Covid policy: Note how in the first paragraph, it tries to answer that China has shifted its approach to Covid; yet, as it states the facts, it contradicts itself. It is a bit like a high school student winging her answer without having really learned the stuff. And it does it with the self-confidence and chutzpa that said high schooler would have. Yet, like an honest kid, it will tell you it is based on old training data: What this means is that ChatGPT can not be used to create news articles. Since the set of training data is safe for work, having an older training set is an advantage. It is a bit like talking to someone with a great past memory but that does not really know what is going on in the world. Yet, Chat GPT is a bit of a geek: But despite all the above-mentioned quirks, it is lots of fun to coauthor with ChatGPT. Among the biggest benefits I have found are: It allows you to quickly explore the directions and style of a story. Like asking it to write a paragraph in either a suspenseful or fun way. You can ask it to summarize what you have written, and with that, get feedback if the ideas you are trying to convey are coming across well. It is like having a cool friend next to you that will always be up to some intellectual trouble that you want to throw its way. Yet, once you have your piece written, you will need to publish it. And in all honesty, you know you can not call it your work only, even if you were the one generating all the prompts. Limitations Set by Publications Publications have already set policies to ensure that articles that are submitted are not plain works written by ChatGPT. Since in Medium, you get paid by the popularity of your articles, readers were not willing to pay for someone just firing up an AI and getting words together. Medium now has a disclosure policy: Other publications will probably follow suit. The big impact would be on education. All the teachers that were just asking for an essay from their students will have to rethink their grading policies. And it can also be used to answer true and false questions. This now gives me an idea for the next article: Have ChatGPT take a personality test like the Myers Briggs. Conclusion In conclusion, coauthoring with ChatGPT can be a fun and useful tool for exploring creative writing, but it also has its limitations. The algorithm has been designed with certain restrictions set by its trainers, such as not answering questions that are inappropriate or unethical. Additionally, the data used to train the model is not up-to-date and may contain inaccuracies, so it should not be relied upon for providing current information. Finally, when it comes to publishing, there are limitations set by publications to ensure that the work is original and not solely created by an AI. Despite these limitations, coauthoring with ChatGPT can provide a unique and entertaining experience for writers looking to quickly explore different writing styles and get feedback on their ideas.

The Limits of Coauthoring With ChatGPT

ChatGPT-এর সাথে সহ-লেখার সীমাবদ্ধতা

Die Grenzen der gemeinsamen Autorenarbeit mit ChatGPT

Los límites de la coautoría con ChatGPT

Les limites de la co-création avec ChatGPT

चैटजीपीटी के साथ सह-लेखन की सीमाएं

ChatGPT との共著の限界

ChatGPT 공동 작성의 한계

Os limites da coautoria com o ChatGPT

ChatGPT ile Birlikte Yazmanın Sınırları

Giới hạn của việc đồng tác giả với ChatGPT

使用 ChatGPT 共同创作的限制

It’s no longer news that ChatGPT and other chatbots have reimagined our understanding of and interaction with artificial intelligence (AI) tools. Many people came to know about AI for the first time when the internet went wild at the release of ChatGPT last November. Without discounting the excitement associated with OpenAI’s chatbot, we are exposed to AI tools and operations daily. Take, for instance, the fact that Google’s search engine and map rely on AI to process queries and churn out responses in seconds. AI - the Good, the Bad, and the Inevitable The possibilities of what can be done with ChatGPT and other AI tools have generated unprecedented exhilaration. There are instances of these tools creating content and technical documents exclusively reserved for professionals. ChatGPT has been used to write codes, develop malware, generate ideas, translate languages, and many more. In 2022, the use of Midjourney grew by over . 1000% The capabilities of these tools also introduce the fear of doomsday. There are concerns of , , , , , and . mass unemployment ai-powered plagiarism and education roguery copyright litigation disinformation fake research abstracts democratization of cybercrime A lawsuit filed on January 13, 2023, accused Stability AI, Midjourney, and DeviantArt of “the rights of millions of artists”. violating AI is the future. We must learn to embrace the good and implement measures to minimize the impact of the bad. However, there is no doubt that AI will continue to disrupt modern society. A recent survey by showed that Gen Z (29%), Gen X (28%), and millennials (27%) used generative AI tools. Statista The AI global market revenue is expected to grow from $136 billion in 2022 to over by 2030. According to , 35% of companies used AI in their business, 42% were exploring AI, and 66% were either currently executing or planning to apply AI to address sustainability goals. $1.5 trillion IBM AI benefits included work automation (30%), cost savings (54%), IT performance (53%), and better customer experience (48%). The Making of AI Given the many wonders of ChatGPT and other tools, it is easy to assume that AI tools are a fusion of sorcery and science. Fortunately, they are not. AI is data-based, laboratory-generated mathematical models designed to perform tasks that require human intelligence, such as recognizing patterns, learning from experience, solving problems, and making effective decisions. The development of AI is driven by advances in fields such as computer science, neuroscience, and psychology. It is based on the idea that human intelligence can be modeled and simulated by a machine. Some of the key technologies and techniques used in AI include machine learning, natural language processing, and robotics. Impact of Human Bias on AI Development The notion of “garbage in, garbage out” is very much true of AI. As we continue to develop and rely more and more on AI systems, we must also be aware of the potential for bias in these systems. While it's easy to point the finger at AI itself as the problem, the truth is that the real concern is the human bias of those who develop and train these systems. AI systems behave exactly the way their developers want them to behave. 69% of AI researchers believe and should entail a greater focus on safety. AI and its development are a safety concern Human bias in AI can manifest in a variety of ways, from the data used to train AI to the decision-making processes of the system itself. For example, if an AI system is trained on a dataset that is disproportionately made up of one particular group of people, it may not be able to accurately understand and make decisions for other groups. Similarly, if an AI system is designed to make decisions based on certain assumptions or stereotypes, it may perpetuate harmful biases in society. One of the biggest issues with AI is the fact that the data it is trained on can reflect the biases of the people who collect and curate that data. For instance, if a dataset used to train a facial recognition system is mostly composed of images of light-skinned people, the system will likely perform poorly when trying to recognize the faces of people with darker skin tones. This is a form of bias that can have real-world consequences, such as in the case of due to a faulty facial recognition match. people of color being falsely arrested But it's not just the data that can be biased, the people creating and training these systems can also introduce bias through their own unconscious prejudices. For example, a found that language models like GPT-3, which were trained on a dataset mostly racist and sexist tend to produce gender-stereotyped and racist texts. This can perpetuate harmful stereotypes and limit the potential of AI systems. study Socially Responsible AI Tools With 74% of organizations haven’t taken steps to ensure their AI is trustworthy and responsible, the urgency to make socially responsible AI tools is a collective responsibility. It begins with identifying the potential for bias and actively working to mitigate it. This means diversifying the team of people working on AI and ensuring that a wide range of perspectives and experiences are represented. It is important to ensure that the data used to train AI is diverse and representative of the population it will be serving. This involves carefully selecting and curating the data to ensure that it does not perpetuate existing biases or stereotypes. Additionally, it is important to consider the potential impact of the data on different groups of people and obtain input from diverse perspectives to ensure that the data is inclusive and fair. AI systems design should be transparent and explainable. This means that the decision-making processes of AI systems should be clear and easily understood by humans so that any potential biases or issues can be identified and addressed. It is essential to regularly evaluate and monitor the performance of AI systems to ensure that they are functioning as intended and not perpetuating harmful biases. This includes regularly analyzing the data used to train AI systems, as well as the decisions and actions of AI models. Government should make laws and regulations that enforce socially responsible AI development and use without stifling growth and development. Maximizing AI for the Good of Society AI has the potential to revolutionize the way we live, work, and socialize. But as we continue to develop and rely on AI systems, it's crucial that we also address the potential for bias. We must recognize that the real concern is not AI itself, but the bias of those who develop and train it. By being aware of this issue and taking steps to mitigate it, we can ensure that the future of AI is one that benefits everyone. The goal should be the development of AI for the good of society. Such a goal will require collective responsibility from governments, developers, and users. Governments should enforce regulations that ensure AI systems are developed in a socially responsible way. Developers should prevent bias by adopting diversity, transparency, and accountability. They should regularly evaluate and monitor the performance of AI systems to prevent unintended bias and abuse. The public should also understand that they are responsible for how they use AI. Socially irresponsible use of AI should be discouraged by all.

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DATA TRAINED EDUCATION

EVERGREEN INDEX #3572

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DATA TRAINED EDUCATION

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DATA TRAINED EDUCATION

EVERGREEN INDEX #3572

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