Significant progress has been made in the case of seven air hostesses including the <strong>PIA</strong> air hostess arrested in Toronto, ARY News reported on Tuesday. The

PIA air hostess case turns mysterious

PPP senator opposes cabinet approving flag carrier’s sell-off

‘Clandestine’ PIA sale irks Rabbani | The Express Tribune

The Jeddah Airport witnessed the distress of 50 stranded passengers—Umrah pilgrims. Here are the details of the incident.

PIA Flight Leaves Behind 50 Umrah Pilgrims At Jeddah Airport Due To No Space On Aircraft

ISLAMABAD: Terming the privatization process of the national flag carrier Pakistan International Airlines confined to the ruling elite, former chairman Senate Raza Rabbani has asked the government...

Rabbani wants MPs to examine PIA privatisation | Pakistan | thenews.com.pk

A Pakistan International Airlines (<strong>PIA</strong>) flight from Jeddah to Islamabad couldn’t take all its passengers because the plane was too small, a <strong>PIA</strong> spokesperson said. The airline arranged accommodation and basic needs for the 50 passengers left behind in a nearby hotel.

PIA flight leaves 50 passengers in Jeddah due to capacity issues - Minute Mirror

A <strong>PIA</strong> flight from Jeddah to Islamabad leaves 50 passengers stranded due to aircraft size limitations, prompting demands for intervention. Passengers provided with accommodation while awaiting transportation.

PIA flight leaves 50 passengers in Jeddah over capacity issues

The flights operation of Pakistan International Airlines (<strong>PIA</strong>) was in disarray on Monday as several of its flights were facing hours-long display and one flight left 50 travellers stranded at Jeddah Airport, reported 24NewsHD TV channel.

PIA flights in disarray as 50 passengers left stranded in Jeddah

Officials hope agency’s review will result in <strong>PIA</strong>-EU flight revival

EASA to assess PIA flight safety in May | The Express Tribune

Pakistan News: LAHORE: An Islamabad-bound <strong>PIA</strong> flight carrying Pakistan Prime Minister Shehbaz Sharif, Punjab chief minister Maryam Nawaz and a high-level delegation .

Islamabad-bound PIA flight from Saudi carrying Pakistan PM Shehbaz Sharif diverted to Lahore, inconveniencing other passengers - Times of India

A flight attendant working for Pakistan International Airlines (<strong>PIA</strong>) left her uniform and a brief thank-you note to her employer in a Toronto hotel room before disappearing, the website India Today reported this week.

Another Pakistan Airlines flight attendant vanishes while in Toronto | National Post

, by 2023, 65% of the world’s population will have their personal information covered under modern privacy regulations. In addition to this worldwide legal motion of privacy regulations, consumers themselves are also becoming more privacy-savvy and demanding that businesses protect their personal information. A privacy risk assessment can help companies to answer these two complementing requirements. Let’s dive into the details of what a privacy risk assessment is, why it can benefit businesses, and how to conduct one. According to Gartner What is a Privacy Risk Assessment? A privacy risk assessment is a risk management framework for determining the risk of holding and maintaining PII (Personal Identifiable Information). Organizations can make informed decisions to prevent privacy-related mistakes by conducting privacy risk assessments. This ensures businesses comply with privacy regulations and can accommodate data privacy requests from consumers and authorities. As a result, companies that conduct privacy risk assessments are more likely to avoid legal and business implications of non-compliance and to build long-term, trustworthy relationships with their customers. Privacy risk assessments are often referred to as PIAs (Privacy Impact Assessments) or DPIA (Data Protection Impact Assessments). What does each of these privacy risk assessment types mean? What are PIAs (Privacy Impact Assessments)? PIAs are risk assessments that analyze an organization’s existing privacy controls. By monitoring the organizational processes, systems, applications, and products, a PIA determines to which extent PII is managed and secured and the extent of the business’s privacy risks when collecting, maintaining and circulating PII. This internal audit enables organizations to take action and resolve any blind spots by implementing actionable steps, for example, by adding privacy policies, enabling consumer opt-out, enforcing data encryption, etc. Businesses often run PIAs when they implement a new business initiative. Initiatives might range from a project as small as a new product launch to as large as an M&A. What are DPIAs (Data Protection Impact Assessments)? DPIAs are also risk assessments. However, unlike PIAs, they are connected to the . Article 35 of the GDPR requires businesses to develop and run DPIAs for high-risk data process activities. GDPR This includes the following use cases: Profiling and other types of evaluations of personal identifiable aspects Large-scale processing of personal information and personal identifiable information Data collection and processing that takes place in an automated manner Surveillance of public areas at a large scale DPIAs are expected to include: A methodical description of data processing activities and their purpose. An analysis of the legal reasoning behind data collection activities. An evaluation of the risk these data collection activities have on individuals. A presentation of the measures the business needs to take to mitigate these risks and ensure compliance with the GDPR. The DPIA framework helps organizations comply with the GDPR and prevent the costly legal and business implications of violating this regulation. Why Businesses Need a Privacy Risk Assessment Privacy risk assessments enable businesses to adhere to modern privacy requirements. These could be either compliance requirements by governmental organizations, consumer requirements demanding businesses to protect their data, or requirements of internal business stakeholders who are aware of the importance of privacy. Let’s break down each one of these requirements: Compliance Regulations - New regulations are sprouting around the world. Legally binding requirements like the GDPR, CCPA, , , and others all require varying extents of personal information management, maintenance, and control. By conducting a privacy risk assessment, businesses can identify compliance gaps, evaluate gaps and their risks and treat them - before suffering from the legal and financial implications of non-compliance. PIPEDA CPA Consumer Requirements - Individuals today are much more privacy-savvy than they were in the past. They know the exposure of their personal information could result in discrimination, personal embarrassment, financial losses, and even physical harm. As a result, they expect businesses to safeguard their personal data. A privacy risk assessment can help companies to build long-term relationships with consumers based on trust. Internal Security Requirements - The threat of a privacy data breach looms over businesses, mainly because it’s hard to anticipate the blast radius. A breach could occur due to a “silly” security overlook like a password (see: SolarWinds) or an innocent leak by an employee but still have a devastating publicized impact. With a privacy risk assessment, the business takes active steps to reduce this risk by identifying security and compliance threats, so it can take action to remediate them. A privacy risk assessment is an opportunity for businesses - to do the right thing for both their customers and to protect their business - and reposition themselves as a privacy-first entity. Benefits of a Privacy Risk Assessment A privacy risk assessment might sound like a daunting and complicated process, but conducting an assessment actually has multiple benefits for businesses, including: Being Prepared - for Anything Privacy risk assessments are strategic initiatives that enable the business to plan privacy activities and prepare for compliance audits and consumer requests so they can prevent any unpleasant surprises. By running them, the company can ensure they are always ready, no matter which privacy curve ball comes their way. In other words, a privacy risk assessment can help avoid unnecessary stress. Making Informed Decisions A privacy risk assessment starts with identifying any privacy gaps in how the business collects, manages, and secures private information like credit card numbers, contact details, credentials, addresses, etc. Then, the assessment evaluates the risk of these gaps and information being leaked. This data-driven and methodical approach enable businesses to make informed and cost-effective decisions regarding how to deal with privacy holes and blind spots. The decisions can be backed up and proven to show the benefit for the business. For example, by comparing the potential damage, a breach could have or by showing the time saved compared to the required process if a violated compliance regulation is found by the government. Signaling to Consumers and Employees Businesses today are subject to lots of pressure. Intense competition, geo-political tectonic shifts, the Great Resignation, and technological changes are just some of the changes keeping business owners up at night. Throughout this turmoil, it is important to maintain a stable and reliable relationship with consumers and with employees, and ensuring their privacy is an important way to do so. Businesses that take action to safeguard consumer and employee personal data signal to them that they are their number one priority. While not every company can afford to run widespread campaigns touting their privacy-first actions, as Apple did, even a pop-up update on the website, an email or text message, or a social media update can do the trick. This will have a beneficial, long-lasting effect. Preparing for Compliance Audits Compliance requirements are strict and mandatory and cannot be treated lightly. A privacy risk assessment ensures no privacy requirements will fall through the cracks. It provides a comprehensive overview of all processes that need to be fixed and gives the business the chance to treat them before they become a legal liability. In addition, a privacy risk assessment provides the company with evidence that it took the required steps on the way to compliance to showcase to the authorities. How to Conduct a Privacy Risk Assessment Despite the importance and value of privacy risk assessments, there is no checklist or single step-by-step process to take when conducting one. There are, however, multiple tools in use by the industry. The main ones are: Data Mapping Data mapping is the process of reviewing, matching, and correlating data fields to create a unified, transparent, and comprehensive overview of where and how data is stored and used in the organization and how it flows within the organization's systems. A data map can include information about the number of data sources, their type, the types of data they contain, data accuracy, data quality, data structure, who has access to the data source, and more. With data mapping, businesses can ensure their data is consistent and of high quality across all sources. This information enables them to identify potential privacy and compliance risks. Once gaps are identified, the remediation process can be streamlined. This helps organizations minimize privacy risks, fulfill regulatory requirements and take control of their data. You can also use a data mapping tool that evaluates your third-party vendors to add an extra layer to your assessment easily. Generating an Automated RoPA A Record of Processing Activities (RoPA) is a record of the privacy procedures and maintenance of these procedures within the organization. A RoPA reveals up to 100% of data sources and presents them in an easy-to-understand manner. An automated RoPA tool will use data mapping to map data flows related to collecting, storing, processing, retaining, and deleting PII. Then, the tool will automatically generate the RoPA report and ensure it is kept updated. With the RoPA, businesses can gain insights into their privacy activities, identify gaps and mitigate them, and be ready for any audit. Manual Assessments A more traditional way of conducting a privacy risk assessment is through the manual collection of data. This includes handing out questionnaires, combing over spreadsheets, and conducting analysis of the results. This process is slow, time-consuming, and inaccurate, and therefore it is not recommended. FAIR Privacy and NIST PRAM Assessments More advanced, albeit still manual, ways of conducting a manual assessment is by using the FAIR Privacy or the NIST PRAM frameworks. Fair Privacy - Based on the FAIR (Factors Analysis in Information Risk) method, Fair Privacy provides a guiding PowerPoint and a spreadsheet for calculating risk based on the Monte Carlo simulation. NIST PRAM - PRAM (Privacy Risk Assessment Methodology) is a series of worksheets designed by NIST to help businesses “analyze, assess, and prioritize privacy risks to determine how to respond and select appropriate solutions.” More details on both frameworks are here. External Assessments An external assessment conducted by a consulting company can take away the heavy lifting of the privacy risk assessment from the business. The vendor will come in, assess all privacy mechanisms and provide recommendations for the next steps for the business to take. When conducting an external assessment, it’s important to evaluate the ROI. This includes the costs of the process, as well as the long-term impact that comes from exposing sensitive data to an external vendor. In addition, it’s recommended to understand the data mapping process the vendor will conduct. Will they run a manual, error-prone process or introduce automated tools and techniques? What happens to your data after the vendor leaves the premises? How often is the assessment updated? And more. Next Steps for Businesses Privacy risk assessments provide early warning for businesses about privacy gaps and their impact, so they can make informed decisions and prevent costly and embarrassing mistakes. To get started we recommend choosing an effective solution that removes the heavy lifting from you and provides an efficient and accurate result that you can quickly and easily act on. provide businesses with an effective and privacy-aware solution that provides insights into their data collection and management. These insights can then be easily translated into actionable steps that bridge any privacy gaps, enabling businesses to comply with regulations, respond to consumer requests and meet internal security requirements. In addition, an automated solution provides businesses with control over the assessment process and their data. Automated data mapping and RoPA report generation Also published . here

A Guide to Conducting Privacy Risk Assessments

“It would be pretty damn maddening if it turns out programmers are easier to automate than lawyers.” -Professor Alejandro Piad Morffis. The increase in adoption of Large Language Generative AI models such as ChatGPT, Microsoft Bing, Google Bard, Stable Diffusion, etc., while the advantages of these models cannot be refuted, it has led to an exaggerated and harrowing, but not baseless, fear by members of the public on the possibility of these AI models jeopardizing job security for millions of workers worldwide. As described earlier, the threat of AI to human jobs, while being exaggerated and harrowing, isn't baseless. The ability of AI to perform repetitive tasks, process large amounts of information, and mimic human-like decision-making makes it a very good tool to enhance creativity, productivity, and efficiency. To answer the question,  “Will AI take our jobs?” I have enlisted the help of an expert by the name of , a Professor of AI at the University of Havana, Cuba. The Professor is a mentor, teacher, friend, and, most importantly, inspiration to me. Professor Alejandro Piad Morffis How I Hope to Approach This The questions will be pre-fixed with the "Q" letter while the answers will be pre-fixed with the "A" letter. With regards to the questions, I hope to cover technical and philosophical questions as Professor Morffis also has an affinity for the Philosophical. It is also important to note that I will provide links to certain concepts that are complex to grasp, for the sake of understanding. Let us Begin! Q: Firstly, Could you tell us a bit about yourself, your professional qualifications, and such? My name is Alejandro Piad, I majored in Computer Science at the School of Math and Computer Science at the University of Havana, Cuba. I did a Master's in Computer Science also at the same college in 2016 and earned a double Ph.D. in Computer Science at the University of Alicante and a Ph.D. in Math at the University of Havana in 2021. A: My Ph.D. was in knowledge discovery from natural language, specifically focused on entity and relation extraction from medical text. Since grad school, I've been teaching at the University of Havana, I've been the main lecturer in Programming, Compilers, and Algorithm Design, and also an occasional lecturer on Machine Learning and other subjects. Since 2022, I'm a full-time professor there, I was also one of the founders of the new Data Science career, the first of its kind in Cuba, and I wrote the entire Programming and Computing Systems curriculum for that career, I keep doing research in NLP, right now focusing on neuro symbolic approaches to knowledge discovery, mixing LLMs with symbolic systems. Q: How long have you been working with AI systems? I played with AI for games as an undergrad student, and did a couple of student projects with computer vision and metaheuristics. After graduating, I started my master’s in Computer Graphics, but as a side project, I did some research in NLP, specifically on sentiment analysis on Twitter. A: After finishing the master I started thinking about doing a Ph.D. and got all in with machine learning. So you could say around 10 years since I started taking AI seriously. My oldest paper related to this stuff is around 2012. Q: That is intensely impressive! You worked with AI way before it became cool. What do you believe is the singular most significant technical advancement in AI which has contributed to its current mainstream adoption and the consequent job displacement threats? Well it was always cool, just not outside academia. I'd say, the intersection of two developments: the discovery of artificial neural network architectures such as the Transformer, which solved many of the scalability problems of previous architectures, and the invention of hardware where you can run those specific architectures at scale super efficiently. A: orthogonal Q: Fascinating! In your professional opinion as an educator and an AI researcher, what industries stand the risk of being replaced by AI? I don't know if any industry will be replaced entirely, but I'm sure there will be massive changes. In the long term, of course, no one can say anything. But in the short and mid-term (5-10 years), with what we're seeing with language models, my bet is that anyone whose job is predicated on the shallow filtering and processing of natural language will have some reckoning to do. A: This includes all sorts of managerial roles, including anyone whose job is to read emails, summarize, and build reports. Any kind of secretary who doesn't go beyond note-taking and task scheduling. Copywriters who work with templated content. Basically, any content creation task below the level of actual human creativity will be cheaper to automate than paying a human parrot. So those will go away. One single copywriter using the ChatGPT of the near future will be able to craft, in hypothesis, 3x to 10x more content with the same quality. stochastic Not because the model will give them the final quality they aim for, but because the model will give them 90% of the quality, and then the real human creativity comes as a cherry on top and adds the final 10%. Education has to change considerably, too. We can talk more about that if you want. Q: This is a really nice angle. You're an educationist and from your piece titled " ," you obviously know how change-averse the educational institution is. Do you think formal education can be depended upon for survival in the post-AI world? Rethinking college education Yeah, academia will adapt. It is the longest-living institution in Western civilization. It predates all our mainstream religions, and it has survived all major civilization changes. It will change substantially, as it has changed across the ages. A: Q: How important is it for society to consider the ethical implications of AI and job displacement? All technology has potential issues, and the more advanced the tech the more pressing it is to consider them. AI is a very powerful technology with the potential to disrupt all our economic relationships. A: It is something at the level of an industrial revolution, so it will have massive implications, and so the concern must be at the same level. One thing that is different from previous disruptive tech is that mostly, new tech automates the jobs that require the least cognitive skills, it happened with agriculture, manufacturing, mining, etc. However, this time, we are on the brink of replacing a large number of white-collar jobs while leaving lots of blue-collar jobs undisrupted. so we will have lots of people who are used to working in offices finding that an AI can do their job as well (or maybe slightly better) and much cheaper, so they will either have to upgrade their skills significantly or they will have to turn to less skilled jobs. There are other ethical considerations, there is a lot of potential for misuse of AI technologies for misinformation, fake news, social disruption, etc. I don't think we are prepared for a massive number of human-like chatbots taking over Twitter; it is already starting to happen. There are also bias issues, as these systems become more and more pervasive, the harms can be very focused on the minorities, so everyone will not reap the benefits of AI to the same degree, some minorities will get the downsides more strongly than those not from minorities. Q: So in other words, we should pay attention because, unlike past forms of automation, AI has the potential to disrupt cognitively tasking jobs as well. Yeah, especially those jobs. It will automate more white-collar jobs than blue-collar jobs, at least in the near term. That's something new, and society isn't used to having to deal with that kind of job disruption. A: These are folks that went to college and more or less got convinced their jobs were safe, or at least safer than taxi drivers, pizza boys, gardeners, you mention it. Q: This makes sense, let's hit a little bit close to home. Do you believe that an increase in AI capability will ultimately lead to a decrease in overall employment for software engineers/developers? In the very long term, all jobs will evolve in unpredictable ways, including software engineering and development. AI and technological advancements will transform these professions to the point where they may seem to have disappeared. A: However, in the short to midterm, a decrease in software engineers is unlikely due to the increasing demand for software across various industries. This growing need for skilled professionals far surpasses the current number of trained individuals capable of building software. The AI revolution will follow a similar pattern as previous technological breakthroughs in computer science such as compilers, integrated development environments, cloud computing, containers, code completion, and IntelliSense. These innovations made programming more accessible for those without highly formal backgrounds and expanded opportunities for developers. Over the next 20 years, we can expect an explosion of people entering the field of software development. Although job roles may change somewhat with evolving technology trends, there will likely be continued growth for those interested in learning how to program and write code. Q: This is incredible, although, the release of Generative AI models such as GitHub's Co-Pilot and the GPT family of models has prompted (forgive my pun) rumors about the possibility of software developers losing their jobs to AI. What do you say about this? Look at the numbers. All I'm seeing are more job ads for software developers. The trend is still climbing. A: Q: Jeff Clune, an ex-OpenAI engineer, recently made a prediction at the AI Safety Debate conference, he stated that there was a 30 percent chance that AI will be capable of handling "50% of economically valuable work" by the year 2030, what would this mean for the overall developer labor market? First, I have no idea how you would wrap your head around what a 30% chance of automating a 50% of jobs even looks like. Is it a 15% expectation of losing your job? A: Q: I guess the numbers do make for a confusing scene. But the essential point is; Software developers have lots of reasons to be worried about their job security and many of the tasks they currently spend lots of time on are being automated. The pace at which that occurs will accelerate. Yeah but the thing is, many of the tasks developers spend most of their time on are pretty low value and we would be much better off if they were automated: debugging, writing tests, doing pesky code optimizations. As you automate all of that, we'll have more time for the really important parts of software development, which was never about writing code. A: Q: Could you speak more about those parts? High level and architecture design, user experience, human-computer interaction, and that's just about the software itself. Software engineering is really about the relationship between software and people, both people that make software, and people that use software. So software skills are only half of the story. Understanding your users and colleagues is the other half. A: Q: This is similar to how the core job of accountants is to communicate financial information and not create financial statements, fascinating! It's a fair bet to say AI capabilities will increase in 10-20 years. How prepared are we as a society & species to address the potential job displacement/loss brought on by the potential adoption of AI? How does this affect our sense of purpose as human beings? Very hard to say, of course, we're in the middle of an industrial revolution as big as at least the microprocessor revolution or the internet revolution, no one in 1960 could imagine what 1980 would look like. A: Society is never ready for change, by definition. That's what a system is, something that strives to maintain its status quo. But humans are the most adaptable social species out there, so I think we'll manage. Lots of people will suffer, and that's something we have to work on, definitely, but nothing apocalyptic in my opinion will happen. Q: There have been a lot of talks on the dystopian potential of AI. Why do you say nothing apocalyptic will occur? I still haven't seen any really compelling arguments for the doomsday scenario. Lots of the arguments seem to be predicated on reasoning like "we don't know how this is going to evolve so it will probably kill us all" and that's a classic logical fallacy: you're basically making an inference from lack of knowledge. A: Q: This is true. But does seem plausible. the AI alignment problem I think we will solve it, at least well enough to avoid apocalyptic scenarios. The most severe alignment issues require you to believe in a powerful version of the that I don't believe plausible. A: orthogonality thesis Q: Fascinating! Going back to automation, how can we leverage AI to augment human work rather than replace it and what industries are ripe for this kind of collaboration? I think that's only natural, as we automate more and more of the menial cognitive work (e.g., summarizing documents or finding relevant references) we humans will get to work on the most creative parts of our jobs. Some jobs have very little of that to begin with, and there I see a challenge because maybe those will be completely or almost completely automated away. A: But most knowledge work has a creative side, the part where you actually do something novel. As to which fields are ripe for this, I can't talk about much else but in education at least I think we're bound for a long-needed revolution. We professors no longer need to be gatekeepers of information. Instead of spending most of our time grading the same essays over and over, we can now focus on giving the best possible personal feedback to each student. Q: What are the possible ways AI could revolutionize the educational system? Perhaps more teaching techniques adapted optimally to students. There are a few easy ways and then some not that easy. First is just a matter of increasing access to knowledge. Now almost everything you want to learn, you can find relevant information on the internet at least to begin with, but it is often split around many sources with disparate levels of detail, contradictory stuff, different linguistic styles, etc. A: The first relatively easy application is just here take this bunch of sources on some topic and give me a high-level overview of the main takeaways summarized, with links to dive deeper, etc. We are pretty close to that (baring the hallucinations which are a significant problem). Another way is by simply freeing educators from menial tasks to give them more time to focus on creating learning experiences. But by far the most important thing I believe is the potential for personalized learning. You could have an AI assistant and tell it "I want to learn how to make a rocket" and it could create a very detailed plan, especially for you, based on what it already knows that you know, it would tell you, here, first watch this video, now take this short course, now read this chapter of this book, ... And guide you for 3 months to learn something very specific. Q: This is truly promising! You make a solid case for humans adapting. You spoke about bias, is it fair to say large-scale AI adoption will affect the minority? If yes how can this be combated? Yeah definitely, machine learning is by definition trained on the majority, so it will always hurt the most those whose use case doesn't fit the majority for any reason. A: In particular, whenever you train models to predict human behavior or interact with humans, it tends to work better for the subpopulations that are best represented in the data. What can you do? Start by raising awareness of these issues and make sure to thoroughly test your models for bias. Be very careful about how you collect data, don't go the easy way and crawl the web, and make an effort to find good high quality and high-diversity sources of data. But more than anything include diverse people with diverse points of view in your team. You can't solve a problem you can't see. Q: This makes me think. Is there a possibility that access to said AI tools will be relegated to the financially capable? I'm hoping the open-source community will make the tools available to all. We already have seen what having access to a free operating system, a free office suite, a free game engine, a free code editor, etc., does for the creative kids of the poorer parts of the world. A: I trust we will have open-source AI tools as good as commercial ones, the same way we have open-source dev tools as good as commercial ones. Q: This seems really feasible. What advice would you give to your students to prepare them for the workforce in a post-AI world? If you are already studying computer science, the basic advice is to focus on fundamentals, not just tools. Tools will change but the fundamentals will remain relevant for a long time. If studying something else, learn how AI can improve your productivity, and learn a lot about its limitations. Use it to make your own work better. A: Q: This makes sense, fundamentals will stand the test of time. Thank you very much for your time Professor Morffis. Any closing words? The AI revolution is here. We can all either be a part of it, by learning to use this technology for making good and improving the lives of everyone. A: Also published here

How Real Is AI's Threat to Job Security? An Interview With AI Professor Alejandro Piad Morffis

চাকরির নিরাপত্তার জন্য AI এর হুমকি কতটা বাস্তব? এআই প্রফেসর আলেজান্দ্রো পিয়াড মরফিসের সাথে একটি সাক্ষাৎকার

Wie real ist die Bedrohung der Arbeitsplatzsicherheit durch KI? Ein Interview mit KI-Professor Alejandro Piad Morffis

¿Qué tan real es la amenaza de la IA para la seguridad laboral? Una entrevista con el profesor de inteligencia artificial Alejandro Piad Morffis

Quelle est la menace réelle de l'IA pour la sécurité de l'emploi ? Entretien avec le professeur d'IA Alejandro Piad Morffis

नौकरी की सुरक्षा के लिए एआई का खतरा कितना वास्तविक है? एआई प्रोफेसर एलेजांद्रो पियाड मॉर्फिस के साथ एक साक्षात्कार

雇用の安定に対する AI の脅威はどれほど現実的ですか? AI 教授アレハンドロ ピアド モルフィスへのインタビュー

AI가 직업 안정에 얼마나 위협적인가? AI 교수 알레한드로 피아드 모르피스(Alejandro Piad Morffis)와의 인터뷰

Quão real é a ameaça da IA à segurança do trabalho? Uma entrevista com o professor de IA Alejandro Piad Morffis

Yapay Zekanın İş Güvenliğine Yönelik Tehdidi Ne Kadar Gerçek? Yapay Zeka Profesörü Alejandro Piad Morffis ile Röportaj

Mối đe dọa của AI đối với an ninh việc làm thực sự như thế nào? Một cuộc phỏng vấn với Giáo sư AI Alejandro Piad Morffis

人工智能对工作安全的威胁有多真实？人工智能教授 Alejandro Piad Morffis 访谈

I’ve spent the past few months trying to teach myself the piano, pretty much from scratch. It’s been tough, and sometimes disheartening, but also hugely rewarding. You might be wondering by now what an article about learning to play piano is doing on a tech blog. The answer is simple: I might not still be trying if it weren’t for technology. Though I occasionally feel like I’m in way over my head starting out as a 27-year old, and traditional methods are still the core part of this musical journey, I’m lucky to live in a time where a wealth of resources are available to me at the click of a button. This is intended to be the first part of a long-term series on learning the piano. It’s primarily a personal account, but I hope it might just help someone else starting out too. Starting out This personal goal has been a long time coming. Although I have no formal background in music, I have a fair bit of background. Nearly half of the music I listen to is classical, and I go to live performances several times a year. I took a couple of music history courses in college, even though I couldn’t read music better than a 3rd grader, let alone play it. Everything I knew about the technical side of music until now came from occasional my Wikipedia-fueled curiousity. informal To be totally honest, this isn’t my first go at learning the piano; I spent many an hour banging away at cheap keyboards when I was about 11. My parents bought me a second-hand beginner Casio, the kind with built-in games that teach you how to play. technically Thanks to those piano games, I could play a mean Twinkle, Twinkle Little Star, and I learned about a rudimentary quarter of Fur Elise. I was very proud the latter fact because I share a birthday with Beethoven. It wasn’t much, but I credit those cheesy MIDI tunes for sparking my initial interest in classical music. But then I stopped playing. My parents couldn’t afford a teacher, and I moved around too much for lessons to be viable anyway. In an era before smartphones, tutorials, ebooks, and modern access to information, trying to teach myself piano felt like a Herculean task I wasn’t quite up to. YouTube 16 years or so later, I decided I wanted to be more than a simple listener; I wanted actually the music I like so much. Perhaps even make my own. understand So, I googled. The Piano First, I needed something to actually play on. I looked to internet communities for advice on picking a piano and starting off as a beginner; became a particularly valuable resource. /r/piano on Reddit After an inordinate amount of research, I settled on Roland’s GO:Piano, a 61-Key instrument aimed at beginners that sells for around $329. It’s light and compact, has Bluetooth, can work with AA batteries, and the keys are the same width as a real piano. I liked Roland’s piano sounds better than anything else for its price, and the extra tones were neat too. I’m particularly fond of the Jazz Scat: My only real annoyance is that the headphone port is on the back, which seems like an odd choice for a keyboard designed to help beginners practice. It’s worth mentioning my choice somewhat went against the advice of many a piano forum member, who are quick to point you towards an 88-key unit with fully ‘weighted’ keys. Most digital keyboards — including the Go:Piano — use springs to push keys up. Weighted keys try to replicalicate the complicated mechanism behind a grand piano’s keyboard action, for more responsive playing that translates better to an acoustic piano. Reddit has a great list of recommendations, but pretty much no new digital pianos below $400 have that feature. Still, I chose the Roland for its portability and versatility, figuring I’d upgrade when necessary (more on this later). A small apartment means I needed something compact and easy to manuever. I usually stored the Go:Piano under my desk, and often played the keyboard on my lap if my desk had too many contraptions on it. My first priority was getting something practical so I’d actually play consistently, and on that front. I think I made the right choice. Learning After some research on a good book, I settled on Faber’s Adult Piano Adventures. That’s partly because because the Go:Piano came with a discount for it, but mostly because Faber was an established name that also happened to have a nifty app (an version is reportedly in the works) and lesson videos. It was the right combination of traditional and new-fangled I was looking for. iOS Android To be clear, the bulk of my time was still spent with the book, which combines theory, technique, and lessons, but the app was a useful resource for the moments I got stuck — and all-around making the process more fun. I connected the app to an Mini via Bluetooth, although a wired connection works too. There are a few notable features: iPad You can control the tempo/metronome, loop difficult sections, resize the sheet music. The optional keyboard view lets you see exactly which notes to hit, which helped me learn to read music more quickly — I use it less now You can isolate each hand’s part or only listen to the accompaniment. You can also change the volume for each part. You can make the music wait for you to play the right note. A dynamic tempo mode changes the speed of the music to match your own playing pace. This was my preferred was of learning to play with accompanyments, until I get the music up to the default tempo There are hundreds of practice songs at different levels When I was have trouble with a particular piece or passage in the book, I was able to turn to the app to help me figure out a tune and reinforce it section by section. To supplement my piano playing, I bought an introductory book on Music Theory. There’s no particular reason I chose this book other than the fact it was one of very few I could find with an audiobook version on Audible. I do most of my reading through audiobooks nowadays, and it’s a huge time-saver to be to read something educational while doing menial activities. Those things might seem like a small difference over sticking to a book, audio files, and a metronome, but it’s the of Bluetooth and the app that helped make sure I was practicing nearly every day, and being able to switch things up kept it interesting. It’s because I can listen to an audiobook on my way to the gym that I’m learning even when I can’t pratice. I’ve been learning with an almost obsessive focus, and I’d almost certainly be a few units behind if it weren’t for the added convenience technology affords me. convenience Drawbacks and resources But technology can be overwhelming too. In my first month or so, I tried to use _every_resource out there. I was watching tutorials on YouTube, reading a variety of music education sites, researching advice on forums, using learning apps and playing a few different piano “games”. There’s an app called Synthesia, for instance, which is popular for helping you play piano in a Guitar Hero style. I realize a lot of people use it with results for learning individual songs, but I simply found it counterproductive for my more theory-oriented goals. Which is why, as in all things, moderation is important. I eventually realized I was and am making consistent progress simply working through my two books, the Faber app, and a sightreading app; there’s no need to add superflous distractions if I’m already making progress. If you’re interested in starting out yourself, I’d recommend you stick to a couple of established resources to not be overwhelmed by the wealth of content out there. That said, here are a few useful resources I found during my own practice beyond the ones I mention above: Reddit’s piano FAQ and buying guide MusicTheory.net: A Great free resource on music theory Ear Training and Rhythm Practice: (Android) or (iOS) Perfect Ear Tenuto : Hundreds of useful videos covering all sorts of topics related to pianos. Living Pianos Videos Going to live performances. Seriously, there’s nothing quite like hearing an accomplished pianist play something live. What’s next Working in the tech media sometimes feels like technology is always out to make us depressed, ill, or dead. It’s good to remember that put to the right use, it can be fulfilling, too. There are a few things I intend to do as I move forward. I’m moving on to a full-size, weighted keyboard to get my muscle memory working with something closer to a real piano. I’ll be getting an honest-to-goodness piano teacher within the next couple of months to make sure I’m developing good habits. One of the great laments of being a post-college adult is not feeling like I’m simply something new. It’s great to have that feeling back. See you in a few months. learning This story was written by , and was originally published on . Napier Lopez The Next Web To read the weekly trending TNW story on Hacker Noon, visit . Hackernoon.com/tnw

How to Learn Piano With Technology

My goal is to make a workable piano. Once I’ve made the piano, I want to be able to build a database of playable songs, to be able to play along to. Nothing fancy, like rock band, but just a simple piano app with songs that you can play along with. If you want to follow along, here’s my workable prototype that we’ll be working toward: https://my-little-piano-app.herokuapp.com/ This is a fun project, and would be a fun little game to make for your kids. So let’s get started. I’m breaking this project up into 3 main components: 1 The HTML 2 The JavaScript 3 The Node.JS Database For the HTML, I originally wanted to make each key clickable, so the note would play on click. Later, I found that clicking each key achieved a much slower play, and an un-realistic sound. So I switched it up and assigned each sound to an individual key press. This allowed for more complex melodies and a more realistic sound. However, there are advantages to doing both, so in this tutorial, I’m going to show how you might go about doing either method, in case you want to follow along. 1a — HTML Keys If you decide to do clickable keys, one problem you might run into very quickly is that keys are complex shapes. They fit around each other like puzzle pieces. In HTML and CSS, it’s easy to make squares and rectangles, but slightly harder to make more complex shapes. Since I wanted to have mouseover effects, I needed to have each key separate. Instead of trying to do simple rectangles and then figure out alignment and z-indexes, I went with a much harder, and unnecessary approach: svg polygons. The “SVG” tag allows you to create shapes that would otherwise be impossible. So I created each key with specific points in the shapes I wanted. So that you don’t have to figure that out yourself, I’m including those elements here: <svg class="piano" height="230" width="1000"><polygon points="200,10 230,10 230,100 245,100 245,220 200,220 200,10" class="white"  id="c" data-key="65"/> <polygon points="245,100 260,100 260,10 275,10 275,100 290,100 290,220 245,220 245,100" class="white" data-key="83" id="d"/> <polygon points="305,10 335,10 335,220 290,220 290,100 305,100 305,10" class="white" data-key="68" id="e"/> <polygon points="335,10 365,10 365,100 380,100 380,220 335,220 335,10" class="white" data-key="70" id="f"/> <polygon points="380,100 395,100 395,10 410,10 410,100 425,100 425,220 380,220 380,100" class="white" data-key="71" id="g"/> <polygon points="425,100 440,100 440,10 455,10 455,100 470,100 470,220 425,220 425,100" class="white" data-key="72" id="a"/> <polygon points="470,100 485,100 485,10 515,10 515,220 470,220 470,100" class="white" data-key="74" id="b"/> <polygon points="515,10 545,10 545,100 560,100 560,220 515,220 515,10" class="white" data-key="82" id="key5"/> <polygon points="560,100 575,100 575,10 590,10 590,100 605,100 605,220 560,220" class="white" data-key="84" id="key5"/> <polygon points="605,100 620,100 620,10 650,10 650,220 605,220 605,100" class="white" data-key="89" id="key5"/> <polygon points="650,10 680,10 680,100 695,100 695,220 650,220 650,10" class="white" data-key="85" id="key5"/> <polygon points="695,100 710,100 710,10 725,10 725,100 740,100 740,220 695,220 695,100" class="white" data-key="73" id="key5"/> <polygon points="740,100 755,100 755,10 770,10 770,100 785,100 785,220 740,220 740,100" class="white" data-key="79" id="key5"/> <polygon points="785,100 800,100 800,10 830,10 830,220 785,220 785,100" class="white" data-key="80" id="key5"/> <polygon points="230,10 260,10 260,100 230,100 230,10" class="black" data-key="49" id="c_sharp"/><polygon points="275,10 305,10 305,100 275,100 275,10" class="black" data-key="50" id="d_sharp"/><polygon points="365,10 395,10 395,100 365,100 365,10" class="black" data-key="51" id="f_sharp"/><polygon points="410,10 440,10 440,100 410,100 410,10" class="black" data-key="52" id="g_sharp"/><polygon points="455,10 485,10 485,100 455,100 455,10" class="black" data-key="53" id="a_sharp"/><polygon points="545,10 575,10 575,100 545,100 545,10" class="black" data-key="54" id="key4"/><polygon points="590,10 620,10 620,100 590,100 590,10" class="black" data-key="55" id="key4"/><polygon points="680,10 710,10 710,100 680,100 680,10" class="black" data-key="56" id="key4"/><polygon points="725,10 755,10 755,100 725,100 725,10" class="black" data-key="57" id="key4"/><polygon points="770,10 800,10 800,100 770,100 770,10" class="black" data-key="48" id="key4"/></svg> Each polygon element is enclosed in the SVG tag and is defined by a series of points. Eacn polygon is also assigned a class, either black or white, corresponding to either black or white keys. Here’s the class styles I made for those: .white {fill:white;stroke:black;stroke-width:1;cursor:pointer;margin:2px;} .white:hover {fill:#9e9e9e;stroke:lightblue;cursor:pointer;stroke-width:1;outline: black solid 1px;} .black {fill:black;stroke-width:1;cursor:pointer;margin:2px;} .black:hover {fill:#515151;stroke:lightblue;stroke-width:1;outline: black solid 1px;} This defines the color and border, as well as the hover effects for each of the keys. Even though the black and white keys are next to each other on a keyboard, for me it was easier to group the black and white keys separately in the HTML document. Each polygon also has an ID. Most of the IDs correspond to the note associated with that polygon, starting at middle “C” all the way up to “B” of the next octave. You will also notice that in addition to an ID, each polygon element also has a data-key, with looks like this: data-key="80" What this does, is it associates each element with key press. Each key on the keyboard has a number. So at this point we could either click the key on the screen, or press a keyboard key to get the program to play a sound for us. However, since the user won’t instinctively know which keyboard key corresponds to each sound, I’ve included a display in the HTML to show that to the user. <div class="keysNotes"><h3>note</h3><h3>c</h3><h3>d</h3><h3>e</h3><h3>f</h3><h3>g</h3><h3>a</h3><h3>b</h3><h3>c</h3><h3>d</h3><h3>e</h3><h3>f</h3><h3>g</h3><h3>a</h3><h3>b</h3></div> <div id="keysshow" class="keysNumbers"><h3>keys</h3><h3>A</h3><h3>S</h3><h3>D</h3><h3>F</h3><h3>G</h3><h3>H</h3><h3>J</h3><h3>R</h3><h3>T</h3><h3>Y</h3><h3>U</h3><h3>I</h3><h3>O</h3><h3>P</h3></div> Then, in the CSS for these classes, I have the following: .keysNumbers {color: lightgray;font-size: 40px;font-family: monospace;font-weight: bold;z-index: 10;width: 740px;display: flex;flex-flow: row nowrap;justify-content: space-between;margin-left: 80px;margin-top: -110px;} .keysNotes {color: gray;font-size: 40px;font-family: monospace;font-weight: bold;z-index: 13;width: 740px;display: flex;flex-flow: row nowrap;justify-content: space-between;margin-left: 80px;margin-top: -150px;} This perfectly lines up the number and key display overlayed ontop of the on screen keys. Now, we need to bring in our audio files. I searched the internet, and found piano notes for each key I needed, except for F sharp, A sharp and both Fs. Since I had the other key sounds, I took those keys into a free program called audacity, and changed the pitch to the higher or lower pitch, and resaved the file to get the sound I needed. Audacity is a great tool for that, especially if you wanted to make something like a guitar player instead of a piano, but had limited recordings available to make your initial sounds. In any case, I don’t want you to have to go through all that trouble, so I’m providing the sounds I gathered here for free: https://www.dropbox.com/sh/buuc6h937s62vw2/AADHdRxmmDCfcatwjxvdwAvNa?dl=0 Next, I’m putting each of these files into the HTML with audio tags, and also including the key press codes we used earlier in the polygon elements. <audio data-key="65" id="c_octave1_audio" src="/middle_c.mp3"></audio><audio data-key="49" id="c_octave1_sharp_audio" src="/mid_c_sharp.mp3"></audio><audio data-key="83" id="d_octave1_audio" src="/middle_d.mp3"></audio><audio data-key="50" id="d_octave1_sharp_audio" src="/mid_d_sharp.mp3"></audio><audio data-key="68" id="e_octave1_audio" src="/middle_e.mp3"></audio><audio data-key="70" id="f_octave1_audio" src="/middle_f.mp3"></audio><audio data-key="51" id="f_octave1_sharp_audio" src="/mid_f_sharp.mp3"></audio><audio data-key="71" id="g_octave1_audio" src="/middle_g.mp3"></audio><audio data-key="52" id="g_octave1_sharp_audio" src="/mid_g_sharp.mp3"></audio><audio data-key="72" id="a_octave1_audio" src="/middle_a.mp3"></audio><audio data-key="53" id="a_octave1_sharp_audio" src="/mid_a_sharp.mp3"></audio><audio data-key="74" id="b_octave1_audio" src="/middle_b.mp3"></audio> <audio data-key="82" id="c\_octave2\_audio" src="/high\_c.mp3"></audio>  
<audio data-key="54" id="c\_octave2\_sharp\_audio" src="/high\_c\_sharp.mp3"></audio>  
<audio data-key="84" id="d\_octave2\_audio" src="/high\_d.mp3"></audio>  
<audio data-key="55" id="d\_octave2\_sharp\_audio" src="/high\_d\_sharp.mp3"></audio>  
<audio data-key="89" id="e\_octave2\_audio" src="/high\_e.mp3"></audio>  
<audio data-key="85" id="f\_octave2\_audio" src="/high\_f.mp3"></audio>  
<audio data-key="56" id="f\_octave2\_sharp\_audio" src="/high\_f\_sharp.mp3"></audio>  
<audio data-key="73" id="g\_octave2\_audio" src="/high\_g.mp3"></audio>  
<audio data-key="57" id="g\_octave2\_sharp\_audio" src="/high\_g\_sharp.mp3"></audio>  
<audio data-key="79" id="a\_octave2\_audio" src="/high\_a.mp3"></audio>  
<audio data-key="48" id="a\_octave2\_sharp\_audio" src="/high\_a\_sharp.mp3"></audio>  
<audio data-key="80" id="b\_octave2\_audio" src="/high\_b.mp3"></audio> You’ll probably want to include the audio files somewhere in your project, so that your HTML actually has access to them. Now that we have our audio, we could either code the keypresses or the onclicks to trigger the sound, or both. If we go the onclick route, our JavaScript could look something like this: function play(){       var audio = document.getElementById("audio");       audio.play();                 } Of course, replacing “audio” with whichever element we want to trigger the function. One side note, if we do nothing but this, you will run into a problem, where, if you click one button, and then a second, the second one won’t play until the first one has finished. So It’s nice to include a little extra in the function to stop any previous sounds, if a button is clicked, and then play only the sound that has been clicked. Which way is better? OnClick or KeyPress? That’s entirely up to you, but here are the arguments for both. OnClick is better if your goal is to help you or someone else learn the piano. And Here’s why: If your program relies on keypress, then the user is more often going to associate the key played with the letter displayed on the key, and not the letter associated with the note. So you could have the “C” key play the “middle C” and so forth, but that only works if you only have one octave. And then “C sharp” etc is also tricky. Eventually you’re going to have the user pressing “G” to play “high F” and “S” to play “A sharp”. You can see how this would get confusing for someone who is trying to learn the piano. OnClick allows the user to only associate the sound with the displayed letter, not the letter on the keyboard. However, clicking for each sound leads to a much clunkier piano sound, that isn’t very fluid or easy to play. In my opinion, neither is really a great tool for learning a musical instrument, which is why I went with the keypress approach, which is just more fun to play. I took some of the JavaScript for that from a drum Kit in one of Wes Bos’s projects, but it’s a great approach for this type of project. Here’s what it looks like: window.addEventListener('keydown', function(e) {const audio = document.querySelector(`audio[data-key="${e.keyCode}"]`);const key = document.querySelector(`.key[data-key="${e.keyCode}"]`);if (!audio) return;audio.currentTime = 0;audio.play();key.classList.add('active') Instead of having to get each element by ID, you simply listen for a keypress, and match it with the corresponding data-key, which we already have in our HTML. It plays the sound associated with that keypress, which we already have in our HTML as well. Finally, we’re saying if there isn’t any audio, play it, and we’re setting the audio.currentTime to 0, which helps with the wait I mentioned earlier, allowing the key pressed to play, and stopping any other sounds currently still playing. At this point you should be able to get your piano working either with keypresses or onclicks, whichever you prefer, and your piano is playable, hopefully. The only other thing I did extra was start a database of “sheet music” , so that you could select a song to play, and it will give you the notes for that song. You can see that in my example earlier. The hard part for this, is that, there isn’t an easy shortcut. If you create a similar database, you would have to manually add the notes for each song in there yourself. However, if anyone has a great way to transpose sheet music into JSON data…I’m all “ears”. Once I had the notes saved in a database, I could just make an api call when the song is selected and retrieve the notes associated with the selected song. You could also take it a step further, and let the song be played first, or any number of additional add-ons. Anyway, I hope you enjoy, and have as much fun with it as I did. If you have any feedback, please let me know!

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OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work, a quick overview of my testing methodology should be helpful. Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario. I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product.
When I buy a new appliance, I do not read the instruction manual very thoroughly.
When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had.
I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer.


Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores.


Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too! OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work , a quick overview of my testing methodology should be helpful. Q1 benchmark work Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario . Testing the Extents of AI Empathy: A Nightmare Scenario I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. untrained and unprompted trained It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had. I am curious about life on other planets. I often forget the precise details of conversations I've had. I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too!

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