Google says its new PailiGemma 2 family of models can "analyze images, enabling the AI to generate captions and answer questions about people it 'sees' in photos". Specifically, the model can go beyond simple identification and "describe actions, emotions, and the overall narrative of the scene”. Automatically detecting emotions has been tried for a long time, and claimed to have been achieved as well, but so far "the science stands on shaky empirical ground". Experts are concerned about potential issues with models like this that "can lead to a dystopian future, where your emotions determine if you get the job, a loan, and if you’re admitted to uni”. On a more uplifting and funny side, The Big Bang Theory season 10 episode 14 "The Emotion Detection Automation" has such a machine at its heart. 29 TechCrunch https://lnkd.in/e2R26gEv 𝗚𝗼𝗼𝗴𝗹𝗲 𝘀𝗮𝘆𝘀 𝗶𝘁𝘀 𝗻𝗲𝘄 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗰𝗮𝗻 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗲𝗺𝗼𝘁𝗶𝗼𝗻𝘀 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗵𝗮𝘀 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 𝘄𝗼𝗿𝗿𝗶𝗲𝗱
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The Wall Street Journal has put together a list of their top tech tips of the year! This list includes how to use iMessage to track flights in real-time, how to stop social platforms from training AI on your posts, and how to secure your passwords using Apple Passwords. #TechTips #AI #DigitalLife #LifeHacks
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What happens when you ask AI to solve its own power crisis? After feeding 10,000 pages of electrical engineering data into Google's Gemini, including real-world metrics from data center deployments, we uncovered fascinating insights about power quality and what goes on in the AI's mind. The analysis reveals some surprising conclusions. https://lnkd.in/et5ewRDq
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Another great post by Riley Brown The AI platforms are heating up. Google systems stepping ahead of OpenAi’s system is amazing.
Google Just Released Real Time AI Video Chat And wow its fun to use, and smarter than you think On the 4th question in this video I DREW I A DIAGRAM and it responded with the correct answer after 1 second Pretty insane... What else should I try?
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Transforming Engineering Teams with Effective Leadership | Coaching and Mentoring | Writing about Leadership & Business
"Decisions made by software should be explainable. When a decision impacts my life, I need to understand what led to it. Perhaps the decision was based on incorrect information. There may be a logical flaw in the decision-making process that I need to question and escalate. Understanding the decision process can help me adjust my actions to achieve better outcomes in the future." Martin's post inspired me to consider where we, as users, would benefit from software's ability to explain algorithmic decisions. For example: Why does my LinkedIn feed show me one post over another? To whom are my posts shown? Why is my booking price different from my friend’s? Why did the flight ticket price suddenly increase? Recently, I had to help an elderly neighbor with a fraudulent banking transaction. We went to the bank, where a young clerk with little experience in transaction fraud took quite a long time to understand the situation and the system's messages and decisions about the transaction. In this era of AI, we should consider ways to make software system decisions easy to explain to everyone. This mindset is more beneficial to people than overregulating AI to protect us from black boxes. What do you think? How important is it for software to explain its decisions?
NEW POST I've seen various calls for restrictions on the use of AI and algorithmic decision making in general. I think we would be better off ensuring software decisions are explainable https://lnkd.in/eRA2p5ma
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Hands on Engineering Leader . Cloud Native Architecture . Identity . Operational Excellence . Digital Transformation . AWS . Platform Architecture . Design . SDLC
This resonates with me at so many levels. Starting from rubber duck debugging where I had explained to myself on problems in order to find a solution to giving the context and background of the issue before explaining the decision/recommendation to senior stakeholders. If you are able to explain the "method to the madness", then it is easy for your audience to relate and buy in to your proposal or solution. It has become so much important in the context of AI, but it has always been the case in tech industry. As a side note, I have always appreciated good code comments on why it was done like that (again explainability) versus how it was done, which is self explanatory reading the code. Agree?
NEW POST I've seen various calls for restrictions on the use of AI and algorithmic decision making in general. I think we would be better off ensuring software decisions are explainable https://lnkd.in/eRA2p5ma
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B-Tech @MNIT'25 | Machine Learning Engineer | Deep Learning Engineer | Mechanical Engineer
𝐀𝐈 𝐌𝐲𝐭𝐡𝐬 𝐁𝐮𝐬𝐭𝐞𝐝: 𝐈𝐭’𝐬 𝐍𝐨𝐭 𝐌𝐚𝐠𝐢𝐜, 𝐈𝐭’𝐬 𝐋𝐨𝐠𝐢𝐜! Hello people! Let me begin busting all the bubble of myths in your head about artificial intelligence (AI). Since it’s time AI is rushing into our lives at a high pace, hey stop what… It isn’t, it’s been lurking since the 1950s like that quiet kid at a party who suddenly starts a dance-off when no one’s looking. Back then, we didn’t have Big Data or supercomputers, just a bunch of hopeful scientists dreaming of talking to their calculators. Now, we’ve got more data than gossip at a family reunion and computers faster than your uncle racing for the last piece of pie. So, buckle up! Artificial intelligence isn’t just a trend; it’s the upgrade we’ve been waiting for since we realized sending carrier pigeons with messages wasn’t sustainable. Read More 👇 https://lnkd.in/gWa53zjd
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NEW POST I've seen various calls for restrictions on the use of AI and algorithmic decision making in general. I think we would be better off ensuring software decisions are explainable https://lnkd.in/eRA2p5ma
