#AI efficiency

[techdriveplay]

The Impact of AI on Everyday Life: A New Normal

The impact of AI on everyday life has become a focal point for discussions among tech enthusiasts, policymakers, and the general public alike. This transformative force is reshaping the way we live, work, and interact with the world around us, making its influence felt across various domains of our daily existence. Revolutionizing Workplaces One of the most significant arenas where the impact…

[librarycards]

people interested in defending ai seem to focus on the fact that its dangers are not unique / some bogeyman related solely to this idea of 'advanced technology'....but like, the fact that this is the latest iteration of capitalist interests valuing efficiency over intentionality, and production over creativity, is honestly more than reason enough not to like this idea of "ai art/writing" imo

[atelier-dayz]

I was watching a couple episodes of a crime documentary called Secrets of the Morgue, and they said there was only fifteen (15!) forensic pathologists also boarded in neuropathology??

I'm going to have to do some digging of data once I have the brain power to check, but--

Look, I've mentioned before there's a BIG shortage on pathologists in the US (and worldwide). COVID really exacerbated the shortage because a lot of older pathologists, who'd usually have worked more (because we pathologists actually really love our jobs!) instead retired. Even before COVID, there was a ~50% shortage in forensic pathologists in the US, and now NAME (National Medical Examiner's Association) says there's less than 800 forensic pathologists when we need at least 1500! With the shortage in pathologists overall and the decreasing numbers of medical students going into pathology, we're not getting that 1500 forensic pathologists any time soon. The shortage is going to get worse, and I have heard some astonishingly delayed autopsy case backlogs at various ME offices through the grapevine.

I'm not quite sure where I was going with this because my brain is fried, but guess I just wanted to put that out there. We really need more people going into pathology, not just as doctors but also lab scientists, techs, and pathologists' assistants! Just something to keep in mind!!

[june-again]

hello i hate ai and people who use it

[blondielaguer1989blog]

[m4rs-ex3]

👏 ai 👏 is 👏 a 👏 tool 👏 not 👏 a 👏 medium 👏

[vodka-and-ocs]

[that-giorgione]

Who wpuld win:

Actually learning anatomy and getting better at drawing through consistent exercise and dedications

Using my digital expertise to generate 3d manequins so i can create the whole scene i want in 3d and them use it as traceover reference

[darkwood-sleddog]

been talking about it a lot on my personal blog, but the whole AI as a replacement for creative work makes me feel sick to my stomach and i just needed to share my feelings with the wider audience i have on this blog. like physically it makes me feel ill.

[jcmarchi]

Scientists use generative AI to answer complex questions in physics

New Post has been published on https://thedigitalinsider.com/scientists-use-generative-ai-to-answer-complex-questions-in-physics/

Scientists use generative AI to answer complex questions in physics

When water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.

To fully understand these systems, scientists must be able to recognize phases and detect the transitions between. But how to quantify phase changes in an unknown system is often unclear, especially when data are scarce.

Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.

Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.

Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.

“If you have a new system with fully unknown properties, how would you choose which observable quantity to study? The hope, at least with data-driven tools, is that you could scan large new systems in an automated way, and it will point you to important changes in the system. This might be a tool in the pipeline of automated scientific discovery of new, exotic properties of phases,” says Frank Schäfer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.

Joining Schäfer on the paper are first author Julian Arnold, a graduate student at the University of Basel; Alan Edelman, applied mathematics professor in the Department of Mathematics and leader of the Julia Lab; and senior author Christoph Bruder, professor in the Department of Physics at the University of Basel. The research is published today in Physical Review Letters.

Detecting phase transitions using AI

While water transitioning to ice might be among the most obvious examples of a phase change, more exotic phase changes, like when a material transitions from being a normal conductor to a superconductor, are of keen interest to scientists.

These transitions can be detected by identifying an “order parameter,” a quantity that is important and expected to change. For instance, water freezes and transitions to a solid phase (ice) when its temperature drops below 0 degrees Celsius. In this case, an appropriate order parameter could be defined in terms of the proportion of water molecules that are part of the crystalline lattice versus those that remain in a disordered state.

In the past, researchers have relied on physics expertise to build phase diagrams manually, drawing on theoretical understanding to know which order parameters are important. Not only is this tedious for complex systems, and perhaps impossible for unknown systems with new behaviors, but it also introduces human bias into the solution.

More recently, researchers have begun using machine learning to build discriminative classifiers that can solve this task by learning to classify a measurement statistic as coming from a particular phase of the physical system, the same way such models classify an image as a cat or dog.

The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.

The Julia Programming Language, a popular language for scientific computing that is also used in MIT’s introductory linear algebra classes, offers many tools that make it invaluable for constructing such generative models, Schäfer adds.

Generative models, like those that underlie ChatGPT and Dall-E, typically work by estimating the probability distribution of some data, which they use to generate new data points that fit the distribution (such as new cat images that are similar to existing cat images).

However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free. This distribution describes the measurement statistics of the physical system.

A more knowledgeable model

The MIT team’s insight is that this probability distribution also defines a generative model upon which a classifier can be constructed. They plug the generative model into standard statistical formulas to directly construct a classifier instead of learning it from samples, as was done with discriminative approaches.

“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.

This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.

This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.

At the end of the day, similar to how one might ask ChatGPT to solve a math problem, the researchers can ask the generative classifier questions like “does this sample belong to phase I or phase II?” or “was this sample generated at high temperature or low temperature?”

Scientists could also use this approach to solve different binary classification tasks in physical systems, possibly to detect entanglement in quantum systems (Is the state entangled or not?) or determine whether theory A or B is best suited to solve a particular problem. They could also use this approach to better understand and improve large language models like ChatGPT by identifying how certain parameters should be tuned so the chatbot gives the best outputs.

In the future, the researchers also want to study theoretical guarantees regarding how many measurements they would need to effectively detect phase transitions and estimate the amount of computation that would require.

This work was funded, in part, by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.

[variousqueerthings]

gonna listen to the two-part alan alda and his chatbot journey on his podcast 

we’re starting strong, with alan asking for a basic description of what chatgpt is (he knows, because he’s been using them, but for the sake of basic introduction) and the guy describes it as studied pattern-recognition that is like someone who’s really good at ad-libbing things that they don’t really understand, so it doesn’t really understand what it’s saying, but it’s really good at mimicking --

to which alan goes “this is crushing, I’ve been a chatbot all my life”

[my-thoughts-and-junk]

my new thing is obsessing over a funny little guy for a few months before moving onto another funny little guy

[clevertalelover]

SendBuddy AI Review: Unlock the Ultimate Email Marketing Powerhouse|

Welcome to my blog post SendBuddy AI Review. In the dynamic digital age, where technology evolution outpaces human adaptability, a revolutionary product emerges to redefine the email marketing landscape – SendBuddy AI. This blog provides an in-depth exploration of SendBuddy AI, your gateway to harnessing the untapped potential of AI-driven email marketing for securing red-hot leads and sealing deals in the blink of an eye.

Read the full review here>>>

[sideprojectillustration]

50 home automation icons. Check them out!

If you like them, tell a friend ♡ Side Project

[moonindoon]

Cracking the Code: Manifesting Success with AI-Driven Marketing Strategies

As the domain of marketing technology continues to grow at a rapid pace and is driven by growth in artificial intelligence (AI) and personalization, marketers encounter exciting opportunities as well as daunting challenges. Adapting to these changes requires practical approaches that allow organizations to stay current, manage change effectively, and operate at scale.

In this article, we explore five practical tactics to help modern marketing teams adapt and thrive in this dynamic environment:

Embrace More 'Human' Customer Engagement Technology:

While chatbots have been around for decades, advancements in AI have significantly enhanced their capabilities. Today, AI-powered chatbots can engage with customers in a remarkably human-like manner, providing round-the-clock support and valuable insights.

Leveraging chatbots not only improves customer experience but also generates valuable data for outbound marketing initiatives. By analyzing customer queries and interactions, marketers can easily get valuable data that can enhance their marketing strategies.

Harness Customer Data Responsibly:

Customers willingly share personal information with companies, providing valuable insights into their preferences, behaviours, and sentiments. Marketers must mine this data responsibly and use it to deliver personalized experiences and targeted offers.

By leveraging predictive analytics and machine learning, marketers can analyze data faster and make informed decisions to enhance omnichannel marketing efforts.

Utilize Content Repurposing Tools:

Authentic content remains paramount in marketing, but creating content for various channels and platforms can be challenging. Content repurposing tools like Optimizely and Interaction Studio help marketers adapt long-form content into social media posts, videos, and other formats.

Expanding your content footprint not only enhances brand visibility but also allows for faster learning and adaptation to changing market dynamics.

Invest in Upskilling Your Team:

While AI-based tools offer significant automation potential, managing and mastering these technologies require skilled professionals. Marketers must invest in continuous learning and cross-functional collaboration to stay ahead.

Effective leadership and teamwork are essential for navigating the complexities of modern marketing. Encouraging knowledge sharing and collaboration across teams fosters a culture of innovation and growth.

Embrace Transformational Opportunities:

As AI continues to reshape the marketing landscape, traditional metrics of success are being redefined. Marketers must embrace the transformative potential of AI and other emerging technologies to serve their customers better.

When evaluating new ideas and technologies, marketers should prioritize customer value and align them with their brand and company values. By focusing on solutions that genuinely benefit customers, marketers can drive meaningful impact and success.

In conclusion, navigating the ever-evolving domain of AI-driven marketing requires a blend of innovative strategies and steadfast principles. By embracing more human-centric engagement technologies, responsibly harnessing customer data, utilizing content repurposing tools, investing in team upskilling, and embracing transformational opportunities, modern marketing teams can position themselves for success. The key lies in adapting to change while remaining true to customer-centric values, fostering collaboration, and prioritizing solutions that genuinely benefit the audience. With these practical tactics in hand, marketers can not only thrive but also lead the way in shaping the future of marketing.

[seoconsultantaustraliaa]

85% of Australian e-commerce content found to be plagiarised

Optidan Published a Report Recently

OptiDan, an Australia-based specialist in AI-driven SEO strategies & Solutions, has recently published a report offering fresh insights into the Australian e-commerce sector. It reveals a striking statistic about content across more than 780 online retailers: 85% of it is plagiarised. This raises severe questions about authenticity and quality in the e-commerce world, with possibly grave implications for both consumers and retailers.

 Coming from the founders of OptiDan, this report illuminates an issue that has largely fallen under the radar: content duplication. The report indicates that suppliers often supply identical product descriptions to several retailers, resulting in a sea of online stores harbouring the same content. This lack of uniqueness unfortunately leads to many sites being pushed down in search engine rankings, due to algorithms detecting the duplication. This results in retailers having to spend more on visibility through paid advertising to compensate.

Key Findings in Analysis

Key findings from OptiDan's research include a worrying lack of originality, with 86% of product pages not even meeting basic word count standards. Moreover, even among those that do feature sufficient word counts, Plagiarism is distressingly widespread. Notably, OptiDan's study presented clear evidence of the detrimental impacts of poor product content on consumer trust and return rates.

 Founder and former retailer JP Tucker notes, "Online retailers anticipate high product ranking by Google and expect sales without investing in necessary, quality content — an essential for both criteria." Research from 2016 by Shotfarm corroborates these findings, suggesting that 40% of customers return online purchases due to poor product content.

 Tucker's industry report reveals that Google usually accepts up to 10% of plagiarism to allow for the use of common terms. Nonetheless, OptiDan's study discovered that over 85% of audited product pages were above this limit. Further, over half of the product pages evidenced plagiarism levels of over 75%.

 "Whilst I knew the problem was there, the high levels produced in the Industry report surprised me," said Tucker, expressing the depth of the issue. He's also noted the manufactured absence of the product title in the product description, a crucial aspect of SEO, in 85% of their audited pages. "Just because it reads well, doesn't mean it indexes well."

 OptiDan has committed itself to transforming content performance for the online retail sector, aiming to make each brand's content work for them, instead of against them. Tucker guarantees the effectiveness of OptiDan's revolutionary approach: "We specialise in transforming E-commerce SEO content within the first month, paving the way for ongoing optimisation and reindexing performance."

 OptiDan has even put a money-back guarantee on its Full Content Optimisation Service for Shopify & Shopify Plus partners. This offer is expected to extend to non-Shopify customers soon. For now, all retailers can utilise a free website audit of their content through OptiDan.

Optidan – Top AI SEO Agency

Optidan is a Trusted AI SEO services Provider Company from Sydney, Australia. Our Services like - Bulk Content Creation SEO, Plagiarism Detection SEO, AI-based SEO, Machine Learning AI, Robotic SEO Automation, and Semantic SEO

We’re not just a service provider; we’re a partner, a collaborator, and a fellow traveller on this exciting digital journey. Together, let’s explore the limitless possibilities and redefine digital success.

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