Paul Viola’s Post

Ok .... I deal with a lot of wack jobs in my job.... But , Flat earth ...or Flat Earth(ers). Pretext: After work, in my spare time, I watch a lot of documentaries on nature, the world, and technology to learn. Yeah, I love watching a lot of movies to learn: Nature Channels, BBC docs, Netflix, HBO/Max, and YouTube.I have always loved watching that kinda stuff, There are many places that I will never get to go to, so learn about them. So I was on Amazon Prime and a documentary about "Flat Earth" came up .... Well, I got an hour to hear about this, and their views and viewpoints. Holy shit !!! I have seen some bat-shit-crazy-ass-nut job-loonie-toon crap.....before, but not like this.... One - here are the rules ..... Make-believe is not real ... without scientific imperial - evidence ... plain and simple. YOU have to have facts/evidence/use the Scientific Method and Data. Facts overrule feelings.... plain and simple. Two - Facts are pure and proven, and Feelings are your own (personal points of view). You might feel a certain way ....but, feelings don't come into play. Example: Fire is hot, water is wet, and stupidity kills... You want to set yourself on fire, and say it feels great and say "I feel wonderful and this is be best I have ever felt, in my life "...you go right ahead....Stupidity kills.... The same goes for what we what we do in Technology, I am a technologist. Went to school, did the deal, got certifications, and paid my dues in the industry that I work in ... that is technology. My role is to convey to my shareholders, my bosses, my colleagues, and friends and family. 1- The Truth - 2- The Facts - I see a lot of people out there who are not doing this - not realizing the truth and the facts ..... maybe I am wrong....but if so , here is some gas and a lighter and tell me how you "feel"...

Great posting here from a Life After Layoff. The culling of tech jobs appears to not be stopping, much less slowing. It appears that if you are in a tech field/company, AI is very likely not going to be your friend. Get leveled up for skills in the area, such as prompt engineering, machine learning, etc... The future is today. Time to be ready for it was yesterday. Don't wait for tomorrow. Tomorrow is already here. https://lnkd.in/gFHdZZmW

A lot of you have been asking what I'm doing while taking a break from work. Well, I'm running it like a project, which means a vision and goals. Vision: Success in a job that provides purpose, growth, and achievement while supporting my lifestyle in Boise including family, friends, and civic engagement.  Get the second half of my career off to a start that will take me to retirement: - Strong relationships with friends and family - Affluent - Respected as a citizen and professional Sabbatical Mission: Be ready to succeed at the job I want.  Figure out what job I want instead of settling for what I can find quickly.  Get emotional separation from my last role, reskill to close gaps in new tech, and organize my personal life (including PhD) consistent with committing to a new challenge. Target role: - 40-200 person company (preferred) or a similar organization within a larger one - Report to CEO or CTO - Head of engineering, data science, AI/ML, or new product delivery - Products are heavily automated and technology-driven, probably B2B - Existing product revenue flow that generates significant differentiated data - Resources, interest, and commitment to launching new products using that data - I'm focused on launching these products while improving execution and delivery against current revenue streams.

Completed Data Analysis with Python Career Track! I’m happy to share that I’ve recently completed the Data Analyst with Python career track at DataCamp! Over the July, I dedicated 36 hours to dive deep into 9 courses and tackle 5 hands-on projects. From investigating Netflix movies and analyzing crime data in Los Angeles to visualizing the history of Nobel Prize winners, the journey has been both enlightening and enjoyable. One standout moment was visualizing the gender trends among Nobel laureates. Seeing the increasing recognition of women over time was a powerful reminder of the impact data visualization can have in highlighting real-world trends. It’s incredible to turn data into actionable insights and stories. As I transition from software engineering to data analysis, I’m eager to expand my network and explore new opportunities. Whether you're involved in data analysis, hiring for data roles, or know of any exciting projects or internships, I’d love to connect. Feel free to reach out if you have advice, opportunities, or know of any events I should be aware of. Let’s connect and explore how we can collaborate or share insights in the data world.

Writing a simple wrapper around GPT-4 may help you quickly build a demo, but a high-quality large model app requires way more than that. Here is a talk I gave at FM+SE Summit, on the top 10 challenges and why community efforts are needed to address them. https://lnkd.in/g8b4wFCt If you find the challenges resonate with your experience and would like to join us in pushing the field forward, please consider joining us at AIware'24 by participating in our Late Breaking Arxiv Track! The track is our latest effort to break the limitations of the traditional conference and journal publishing process. You can submit an Arxiv link to any of your current or prior research (peer-reviewed or not) that’s relevant to the conference topic through a simple online form. As the paper will not be included in proceedings, there will be no copyright constraints and you can proceed to submit the paper to other venues for publication later if you wish. Find out more at https://lnkd.in/gxvkEM7V We truly believe in the value of bringing top researchers across disciplines together, and hope we can together shape the future of high-quality, trustworthy AI software systems with you! Hope to see you in July! #ai #llm #foundationmodel #softwareengineering #genai #aiware #trustworthyai #arxiv

🚀Today, we are announcing the availability of Anthropic’s Claude 3 Opus on Amazon Bedrock, the most intelligent Claude 3 model, with best-in-market performance on highly complex tasks. It can navigate open-ended prompts and sight-unseen scenarios with remarkable fluency and human-like understanding, leading the frontier of general intelligence. 🚀 https://lnkd.in/dRVEGpgi

The past couple of weeks I've been noticing a more pronounced trend and want to gut check it with you all. So many of my mid-late career friends are discussing how the purpose of their business isn't aligning with the practice of their business, and not in a philosophical way but, rather, a very concrete way. I have spoken to a friend in academia who feels that they aren't able to teach, a scientist who feels like they aren't able to conduct science, a Dr. who feels like they aren't able to practice medicine, and a therapist who feels that they aren't able to help others. Not due to a deficiency in skill, ability or desire - but because the very structure in which they operate isn't built to do so. The objective of a Dr. is a healthy patient; but that's not the objective of the employer. The objective of the scientist is discovery; but that is not the objective of the institution. Etc. I know this has always existed to some degree, but it feels like it has accelerated at an alarming rate and I truly fear that new technological advances (like AI) will be directed not at solving underlying problems but at optimizing already misaligned systems. Talk me off the ledge.

If you’re looking for a machine learning engineer role, you’ll likely face the **cold start problem**—a common issue not only in machine learning but in any role when you’re just starting out. What do I mean by that? You don’t have any experience, but you need to show your experience. Knowledge won’t magically download into your brain; you have to get your hands dirty. Start solving some Kaggle problems and experiment with toy datasets. While this won’t fully reflect real-world machine learning problems, it’s a great start!

Dropping hint for my next article.