#google generative ai services

[brillioitservices]

The Generative AI Revolution: Transforming Industries with Brillio

The realm of artificial intelligence is experiencing a paradigm shift with the emergence of generative AI. Unlike traditional AI models focused on analyzing existing data, generative AI takes a leap forward by creating entirely new content. The generative ai technology unlocks a future brimming with possibilities across diverse industries. Let's read about the transformative power of generative AI in various sectors: 

1. Healthcare Industry: 

AI for Network Optimization: Generative AI can optimize healthcare networks by predicting patient flow, resource allocation, etc. This translates to streamlined operations, improved efficiency, and potentially reduced wait times. 

Generative AI for Life Sciences & Pharma: Imagine accelerating drug discovery by generating new molecule structures with desired properties. Generative AI can analyze vast datasets to identify potential drug candidates, saving valuable time and resources in the pharmaceutical research and development process. 

Patient Experience Redefined: Generative AI can personalize patient communication and education. Imagine chatbots that provide tailored guidance based on a patient's medical history or generate realistic simulations for medical training. 

Future of AI in Healthcare: Generative AI has the potential to revolutionize disease diagnosis and treatment plans by creating synthetic patient data for anonymized medical research and personalized drug development based on individual genetic profiles. 

2. Retail Industry: 

Advanced Analytics with Generative AI: Retailers can leverage generative AI to analyze customer behavior and predict future trends. This allows for targeted marketing campaigns, optimized product placement based on customer preferences, and even the generation of personalized product recommendations. 

AI Retail Merchandising: Imagine creating a virtual storefront that dynamically adjusts based on customer demographics and real-time buying patterns. Generative AI can optimize product assortments, recommend complementary items, and predict optimal pricing strategies. 

Demystifying Customer Experience: Generative AI can analyze customer feedback and social media data to identify emerging trends and potential areas of improvement in the customer journey. This empowers retailers to take proactive steps to enhance customer satisfaction and loyalty. 

3. Finance Industry: 

Generative AI in Banking: Generative AI can streamline loan application processes by automatically generating personalized loan offers and risk assessments. This reduces processing time and improves customer service efficiency. 

4. Technology Industry: 

Generative AI for Software Testing: Imagine automating the creation of large-scale test datasets for various software functionalities. Generative AI can expedite the testing process, identify potential vulnerabilities more effectively, and contribute to faster software releases. 

Generative AI for Hi-Tech: This technology can accelerate innovation in various high-tech fields by creating novel designs for microchips, materials, or even generating code snippets to enhance existing software functionalities. 

Generative AI for Telecom: Generative AI can optimize network performance by predicting potential obstruction and generating data patterns to simulate network traffic scenarios. This allows telecom companies to proactively maintain and improve network efficiency. 

5. Generative AI Beyond Industries: 

GenAI Powered Search Engine: Imagine a search engine that understands context and intent, generating relevant and personalized results tailored to your specific needs. This eliminates the need to sift through mountains of irrelevant information, enhancing the overall search experience. 

Product Engineering with Generative AI: Design teams can leverage generative AI to create new product prototypes, explore innovative design possibilities, and accelerate the product development cycle. 

Machine Learning with Generative AI: Generative AI can be used to create synthetic training data for machine learning models, leading to improved accuracy and enhanced efficiency. 

Global Data Studio with Generative AI: Imagine generating realistic and anonymized datasets for data analysis purposes. This empowers researchers, businesses, and organizations to unlock insights from data while preserving privacy. 

6. Learning & Development with Generative AI: 

L&D Shares with Generative AI: This technology can create realistic simulations and personalized training modules tailored to individual learning styles and skill gaps. Generative AI can personalize the learning experience, fostering deeper engagement and knowledge retention. 

HFS Generative AI: Generative AI can be used to personalize learning experiences for employees in the human resources and financial services sector. This technology can create tailored training programs for onboarding, compliance training, and skill development. 

7. Generative AI for AIOps: 

AIOps (Artificial Intelligence for IT Operations) utilizes AI to automate and optimize IT infrastructure management. Generative AI can further enhance this process by predicting potential IT issues before they occur, generating synthetic data for simulating scenarios, and optimizing remediation strategies. 

Conclusion: 

The potential of generative AI is vast, with its applications continuously expanding across industries. As research and development progress, we can expect even more groundbreaking advancements that will reshape the way we live, work, and interact with technology. 

Reference- https://articlescad.com/the-generative-ai-revolution-transforming-industries-with-brillio-231268.html 

[gingerbredman1989]

At your service, sir.

Google ImageFX

[goldpilot22]

this is the first I've heard about NaNoWriMo being sponsored by an AI writing service, and I'd just like to say, what???

see, I work with AI for one of my jobs (rating, reviewing, and fact-checking AI responses) and the thing is. you know how every writer has a distinct "voice" and a particular writing style?

well guess what... so do these AI language models. and guess what... it's not a good one. the AI writing style is becoming synonymous with content farm slop. I've seen enough AI writing while working that I can just about instantly recognize when an article I'm trying to get information from (sometimes for work, lmao) is AI-written, and it causes me to instantly lose trust in any information the article has. because guess what, AI language models are not good at facts. they're predictive text machines, not web search machines. and the text they predict is boring, generic, uncreative, error-prone, and structured in the same few generic ass ways.

please don't use AI to write your novels... every writer has their own unique style and AI does not have your style nor your creativity.

watching @nanowrimo within a single hour:

make an awful, ill-conceived, sponsored post about "responsible"/"ethical" uses of ai in writing

immediately get ratio'd in a way i've never seen on tumblr with a small swarm of chastising-to-negative replies and no reblogs

start deleting replies

reply to their own post being like 'agree to disagree!!!' while saying that ai can TOTALLY be ethical because spellcheck exists!! (???) while in NO WAY responding to the criticisms of ai for its environmental impact OR the building of databases on material without author consent, ie, stolen material, OR the money laundering rampant in the industry

when called out on deleting replies, literally messaged me people who called them out to say "We don't have a problem with folks disagreeing with AI. It's the tone of the discourse." So. overtly stated tone policing.

get even MORE replies saying this is a Bad Look, and some reblogs now that people's replies are being deleted

DISABLE REBLOGS when people aren't saying what nano would prefer they say

im juust in literal awe of this fucking mess.

[atcuality1]

Augmented Reality: Bridging the Gap in Online Retail

The online retail landscape has always faced the challenge of replicating the tactile experience of physical shopping. Augmented reality (AR) bridges this gap by bringing products to life in virtual spaces. Whether it’s visualizing how a dress fits or seeing how a lamp brightens up a room, AR offers a unique interactive journey for consumers. Businesses investing in augmented reality development services can tap into this potential, transforming their platforms into immersive digital showrooms. These services allow seamless integration of AR features that appeal to tech-savvy customers, offering both utility and entertainment. In the era of personalized shopping, AR has proven to increase purchase confidence and customer retention. It’s time to reimagine e-commerce with this revolutionary technology.

[cheswirls]

i rly have to consider disabling autocorrect on my phone's keyboard...... it's getting to be too much, this whole correcting words spelled right bc the keyboard's ai thinks i mean smth different, then the word it corrects to doesn't even make sense in context of the sentence

[tecdisha]

Google to Highlight AI-Generated Content in Search Results for Enhanced Transparency

Google is taking steps to improve transparency and trust in online content by introducing labels that will clearly indicate when content is created or edited using artificial intelligence (AI). This initiative will help users make more informed decisions about the content they consume, ensuring they know if AI tools were involved in its production. The new labelling system will soon be integrated across various Google platforms, including Search, Images, and Ads.

Read complete details here

[cinema-hallucinations]

Prompt: create a movie concept about a moving company where the employees use their job as covers for high-stake heists.

Title: Movers and Shakers

Logline: A team of seemingly harmless movers double as a high-stakes heist crew, using their cover as a front for their daring robberies. But when a rookie mover discovers their secret, they must decide whether to bring him into the fold or eliminate him as a threat.

Characters:

Alex (30s): The charismatic leader of the moving crew. He's a master of disguise and manipulation, with a knack for planning elaborate heists.

Riley (20s): The new recruit, a naive and enthusiastic mover who stumbles upon the team's secret.

Frankie (40s): The muscle of the crew, a former boxer with a temper and a love for classic rock.

Ivy (30s): The brains of the operation, a tech-savvy hacker who can crack any security code.

The Target: A wealthy individual or corporation with valuable assets that the crew has their sights on.

Plot Summary:

Alex and his team of experienced movers run a seemingly legitimate moving company, but behind the scenes, they're planning their next heist. When Riley, a new recruit, accidentally stumbles upon their secret, they are faced with a dilemma: bring him into the fold or eliminate him as a threat.

Alex, seeing potential in Riley, decides to take a chance and initiate him into the crew. Riley, initially hesitant, is drawn in by the thrill of the heists and the camaraderie of the team. However, as the stakes get higher, Riley's conscience begins to trouble him.

The crew targets a wealthy businessman's mansion, planning a daring heist during a high-profile party. Riley, conflicted by his growing moral dilemma, tries to warn the businessman, but it's too late. The heist goes off without a hitch, but Riley's guilt grows.

The heist attracts the attention of the police, who begin to suspect the moving company. With the heat closing in, Alex must make a decision: continue the heists and risk being caught, or abandon the life of crime and start anew.

Climax:

The crew's next target is a high-security vault filled with priceless artifacts. As they prepare for the heist, Riley reveals his plan to turn himself in and expose the team's crimes. Alex, faced with losing his crew and the life he's built, must decide whether to let Riley go or eliminate him as a threat.

Themes:

The Allure of Easy Money: The film explores the temptation of crime and the allure of quick wealth.

Friendship and Loyalty: Despite their illegal activities, the team demonstrates a strong bond of friendship and loyalty, highlighting the complexities of human relationships.

The Humor of Crime: The film balances the thrill of the heists with humorous moments, such as the team's awkward attempts at disguises and their bumbling attempts to navigate the criminal underworld.

Ending:

In a dramatic confrontation, Alex chooses to let Riley go, realizing that his friend's moral compass is more important than the thrill of the heists. Riley turns himself in to the police, exposing the crew's crimes. Alex and the rest of the team are arrested, but not before they enjoy one last hurrah, a daring escape from the police station that leaves them laughing all the way to their new lives as law-abiding citizens.

[technoadvisertechnologies]

IT solutions ideas that businesses might consider implementing

1. Blockchain Exists for Attaining Supply Chain Visibility:

Blockchain technology could be used to increase supply chain transparency through tracking products from origin to the customer, guaranteeing authenticity and minimizing fraud.

2. Customer Support Chatbots That Have Been Powered by AI:

Develop chatbots that use AI to process customer inquiries, provide support twenty-four hours a day seven days a week, and even book appointments or potentially process orders, which can push customer satisfaction higher while cutting costs in operations.

3. Predictive Analytics for Business Forecasting:

By using predictive analytics algorithms to analyze historical data, businesses can forecast future trends, and make decisions on inventory management, sales forecasting, and resource allocation based on informed decisions.

4. Virtual Reality (VR) for Training and Simulation:

Integrate VR technology with immersive training simulations for employees. It is most useful in areas of manufacturing, healthcare, and aviation where hands-on training is critical and often not possible in a safe environment.

5. Edge Computing for Real-Time Data Processing:

Edge computing infrastructure can be facilitated to process data closer to the source (e.g., IoT devices) in order to reduce latency while enabling real-time analytics and decision-making, particularly useful for industries requiring real-time response like smart cities or autonomous vehicles.

Join our team for more IT solution ideas https://technoadviser.com/

[moonindoon]

Understanding Google SGE

Google Search Generative Experience (SGE) is a new way to search on Google. It's an experiment using artificial intelligence (AI) to give quick summaries of search topics without clicking on individual web pages.

It can help with:

Finding answers

Getting overviews of topics

Summarizing key points

Finding how-to instructions

Let's understand this with an example of how Google SGE could enhance a search query:

Imagine you're planning a weekend getaway to a new city and you want to find the best places to visit. In the past, when you searched on Google, you would have to click on various travel websites to gather information about attractions, restaurants, and activities. However, with Google SGE, when you search for "best things to do in Dehradun," instead of scrolling through multiple web pages, Google presents you with a concise list of top-rated attractions, dining spots, and activities directly at the top of the search results. These suggestions are curated from various trusted sources, making it easier for you to plan your trip quickly and efficiently.

How to Turn on Google SGE?

To use Google SGE, you'll need:

Chrome browser

Google account

To be 18 or over

To be in one of the 120+ countries where Google SGE is available

You have to opt in to see it. Here's how:

Open Chrome and sign in to your Google Account.

Go to Google on a new tab.

If available in your country, click the "Labs" icon at the top right.

On the "SGE, generative AI in Search" card, click the toggle icon.

After that, you should see AI-generated responses for some of your searches.

How Does Google SGE Work?

Google SGE uses generative AI, which means it can create content like text based on its training on lots of data. Google's model is called Pathways Language Model 2 (PaLM 2). It uses technologies like natural language processing and machine learning to understand your search and respond appropriately.

For example, you're interested in learning about the benefits of meditation for mental health. Usually, when you search for "benefits of meditation," you'd have to browse through multiple articles and research papers to gather information. However, with Google SGE, instead of sifting through various sources, Google provides you with a summarized list of the key benefits of meditation, such as stress reduction, improved focus, and enhanced emotional well-being. Additionally, Google may suggest follow-up questions like "How to start a meditation practice" or "Scientific studies on meditation benefits," allowing you to explore the topic further with just a few clicks..

Benefits of Google’s Generative AI Search

Easy-to-understand summaries of complex topics

More interactive experience with conversational results

Quick and direct information without navigating multiple websites

Potential Downsides of Google’s Generative AI Search

Limited availability in certain regions

Possibility of inaccurate information, especially for important topics like health

Impact on traditional search advertising revenue

Comparison with Similar AI-Powered Tools

Google SGE is different from ChatGPT and Bing's AI search. ChatGPT is more conversational, while Bing's AI search is already available worldwide.

Impact on SEO

Generative AI might reduce website traffic but could also bring higher-quality leads. Optimizing for SGE involves using long-tail keywords, creating quality content aligned with search intent, and implementing structured data.

Prepare for the Future

Google SGE is changing how people find information online. Keep optimizing your site and focus on creating helpful content in natural language that meets search intent.

[srkshaju]

Google Unveils Gemini-Powered Conversational Tool for Effortless Search Ad Campaigns

In a significant update, Google has integrated its Gemini family of multimodal large language models to enhance the conversational experience within the Google Ads platform.

This latest feature aims to simplify the process for advertisers to swiftly create and expand their Search ad campaigns.

The conversational experience is a chat-based tool designed to facilitate the construction of Search campaigns.

Leveraging your website URL, the tool generates relevant ad content, encompassing assets and keywords.

It goes further by suggesting campaign-specific images through generative AI, drawing from both generative AI and your website's existing images.

Google emphasizes that images created with generative AI will be clearly identified.

Before campaigns go live, advertisers have the opportunity to review and approve the suggested images and text.

The beta access to this conversational experience in Google Ads is now accessible to English language advertisers in the U.S. and U.K.

Global access for English language advertisers will gradually roll out over the next few weeks, with plans to extend access to additional languages in the coming months.

Shashi Thakur, Google’s VP and GM of Google Ads, mentioned in a blog post,

"Over the last few months, we’ve been testing the conversational experience with a small group of advertisers. We observed that it helps them build higher quality Search campaigns with less effort."

This innovative tool joins Google's suite of AI-powered tools for advertisers, following the introduction of "Product Studio" a few months ago.

Product Studio enables merchants and advertisers to leverage text-to-image AI capabilities, creating new product imagery and enhancing existing images for free by inputting prompts.

This announcement aligns with Google's broader effort to infuse AI across its products.

Recently, the company unveiled three new AI-powered features for Chrome, introducing functionalities like tab organization, theme customization, and assistance with online activities like writing reviews or forum posts.

As the tech giant continues to integrate AI innovations, advertisers can leverage these tools to streamline campaign creation and enhance the overall advertising experience.

Read More

[streetsofdublin]

ACCORDING TO GOOGLE BARD BIRD AVENUE HOME TO 100 BIRDHOUSES

I cannot find any information relating to the bird houses but it could be true that nesting boxes were installed and I really hope that it is true.

BETA TESTING NEW AI APPLICATION I think that we, in Ireland, refer to them as nest boxes rather than birdhouses. I asked Google BARD AI for an essay describing Bird Avenue and the result is more than a little confused. I am unaware of a new shopping centre.I would have thought that the really large church building that dominates the area should have been mentioned.I cannot find any information…

[mostlysignssomeportents]

What kind of bubble is AI?

My latest column for Locus Magazine is "What Kind of Bubble is AI?" All economic bubbles are hugely destructive, but some of them leave behind wreckage that can be salvaged for useful purposes, while others leave nothing behind but ashes:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Think about some 21st century bubbles. The dotcom bubble was a terrible tragedy, one that drained the coffers of pension funds and other institutional investors and wiped out retail investors who were gulled by Superbowl Ads. But there was a lot left behind after the dotcoms were wiped out: cheap servers, office furniture and space, but far more importantly, a generation of young people who'd been trained as web makers, leaving nontechnical degree programs to learn HTML, perl and python. This created a whole cohort of technologists from non-technical backgrounds, a first in technological history. Many of these people became the vanguard of a more inclusive and humane tech development movement, and they were able to make interesting and useful services and products in an environment where raw materials – compute, bandwidth, space and talent – were available at firesale prices.

Contrast this with the crypto bubble. It, too, destroyed the fortunes of institutional and individual investors through fraud and Superbowl Ads. It, too, lured in nontechnical people to learn esoteric disciplines at investor expense. But apart from a smattering of Rust programmers, the main residue of crypto is bad digital art and worse Austrian economics.

Or think of Worldcom vs Enron. Both bubbles were built on pure fraud, but Enron's fraud left nothing behind but a string of suspicious deaths. By contrast, Worldcom's fraud was a Big Store con that required laying a ton of fiber that is still in the ground to this day, and is being bought and used at pennies on the dollar.

AI is definitely a bubble. As I write in the column, if you fly into SFO and rent a car and drive north to San Francisco or south to Silicon Valley, every single billboard is advertising an "AI" startup, many of which are not even using anything that can be remotely characterized as AI. That's amazing, considering what a meaningless buzzword AI already is.

So which kind of bubble is AI? When it pops, will something useful be left behind, or will it go away altogether? To be sure, there's a legion of technologists who are learning Tensorflow and Pytorch. These nominally open source tools are bound, respectively, to Google and Facebook's AI environments:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

But if those environments go away, those programming skills become a lot less useful. Live, large-scale Big Tech AI projects are shockingly expensive to run. Some of their costs are fixed – collecting, labeling and processing training data – but the running costs for each query are prodigious. There's a massive primary energy bill for the servers, a nearly as large energy bill for the chillers, and a titanic wage bill for the specialized technical staff involved.

Once investor subsidies dry up, will the real-world, non-hyperbolic applications for AI be enough to cover these running costs? AI applications can be plotted on a 2X2 grid whose axes are "value" (how much customers will pay for them) and "risk tolerance" (how perfect the product needs to be).

Charging teenaged D&D players $10 month for an image generator that creates epic illustrations of their characters fighting monsters is low value and very risk tolerant (teenagers aren't overly worried about six-fingered swordspeople with three pupils in each eye). Charging scammy spamfarms $500/month for a text generator that spits out dull, search-algorithm-pleasing narratives to appear over recipes is likewise low-value and highly risk tolerant (your customer doesn't care if the text is nonsense). Charging visually impaired people $100 month for an app that plays a text-to-speech description of anything they point their cameras at is low-value and moderately risk tolerant ("that's your blue shirt" when it's green is not a big deal, while "the street is safe to cross" when it's not is a much bigger one).

Morganstanley doesn't talk about the trillions the AI industry will be worth some day because of these applications. These are just spinoffs from the main event, a collection of extremely high-value applications. Think of self-driving cars or radiology bots that analyze chest x-rays and characterize masses as cancerous or noncancerous.

These are high value – but only if they are also risk-tolerant. The pitch for self-driving cars is "fire most drivers and replace them with 'humans in the loop' who intervene at critical junctures." That's the risk-tolerant version of self-driving cars, and it's a failure. More than $100b has been incinerated chasing self-driving cars, and cars are nowhere near driving themselves:

https://pluralistic.net/2022/10/09/herbies-revenge/#100-billion-here-100-billion-there-pretty-soon-youre-talking-real-money

Quite the reverse, in fact. Cruise was just forced to quit the field after one of their cars maimed a woman – a pedestrian who had not opted into being part of a high-risk AI experiment – and dragged her body 20 feet through the streets of San Francisco. Afterwards, it emerged that Cruise had replaced the single low-waged driver who would normally be paid to operate a taxi with 1.5 high-waged skilled technicians who remotely oversaw each of its vehicles:

https://www.nytimes.com/2023/11/03/technology/cruise-general-motors-self-driving-cars.html

The self-driving pitch isn't that your car will correct your own human errors (like an alarm that sounds when you activate your turn signal while someone is in your blind-spot). Self-driving isn't about using automation to augment human skill – it's about replacing humans. There's no business case for spending hundreds of billions on better safety systems for cars (there's a human case for it, though!). The only way the price-tag justifies itself is if paid drivers can be fired and replaced with software that costs less than their wages.

What about radiologists? Radiologists certainly make mistakes from time to time, and if there's a computer vision system that makes different mistakes than the sort that humans make, they could be a cheap way of generating second opinions that trigger re-examination by a human radiologist. But no AI investor thinks their return will come from selling hospitals that reduce the number of X-rays each radiologist processes every day, as a second-opinion-generating system would. Rather, the value of AI radiologists comes from firing most of your human radiologists and replacing them with software whose judgments are cursorily double-checked by a human whose "automation blindness" will turn them into an OK-button-mashing automaton:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

The profit-generating pitch for high-value AI applications lies in creating "reverse centaurs": humans who serve as appendages for automation that operates at a speed and scale that is unrelated to the capacity or needs of the worker:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

But unless these high-value applications are intrinsically risk-tolerant, they are poor candidates for automation. Cruise was able to nonconsensually enlist the population of San Francisco in an experimental murderbot development program thanks to the vast sums of money sloshing around the industry. Some of this money funds the inevitabilist narrative that self-driving cars are coming, it's only a matter of when, not if, and so SF had better get in the autonomous vehicle or get run over by the forces of history.

Once the bubble pops (all bubbles pop), AI applications will have to rise or fall on their actual merits, not their promise. The odds are stacked against the long-term survival of high-value, risk-intolerant AI applications.

The problem for AI is that while there are a lot of risk-tolerant applications, they're almost all low-value; while nearly all the high-value applications are risk-intolerant. Once AI has to be profitable – once investors withdraw their subsidies from money-losing ventures – the risk-tolerant applications need to be sufficient to run those tremendously expensive servers in those brutally expensive data-centers tended by exceptionally expensive technical workers.

If they aren't, then the business case for running those servers goes away, and so do the servers – and so do all those risk-tolerant, low-value applications. It doesn't matter if helping blind people make sense of their surroundings is socially beneficial. It doesn't matter if teenaged gamers love their epic character art. It doesn't even matter how horny scammers are for generating AI nonsense SEO websites:

https://twitter.com/jakezward/status/1728032634037567509

These applications are all riding on the coattails of the big AI models that are being built and operated at a loss in order to be profitable. If they remain unprofitable long enough, the private sector will no longer pay to operate them.

Now, there are smaller models, models that stand alone and run on commodity hardware. These would persist even after the AI bubble bursts, because most of their costs are setup costs that have already been borne by the well-funded companies who created them. These models are limited, of course, though the communities that have formed around them have pushed those limits in surprising ways, far beyond their original manufacturers' beliefs about their capacity. These communities will continue to push those limits for as long as they find the models useful.

These standalone, "toy" models are derived from the big models, though. When the AI bubble bursts and the private sector no longer subsidizes mass-scale model creation, it will cease to spin out more sophisticated models that run on commodity hardware (it's possible that Federated learning and other techniques for spreading out the work of making large-scale models will fill the gap).

So what kind of bubble is the AI bubble? What will we salvage from its wreckage? Perhaps the communities who've invested in becoming experts in Pytorch and Tensorflow will wrestle them away from their corporate masters and make them generally useful. Certainly, a lot of people will have gained skills in applying statistical techniques.

But there will also be a lot of unsalvageable wreckage. As big AI models get integrated into the processes of the productive economy, AI becomes a source of systemic risk. The only thing worse than having an automated process that is rendered dangerous or erratic based on AI integration is to have that process fail entirely because the AI suddenly disappeared, a collapse that is too precipitous for former AI customers to engineer a soft landing for their systems.

This is a blind spot in our policymakers debates about AI. The smart policymakers are asking questions about fairness, algorithmic bias, and fraud. The foolish policymakers are ensnared in fantasies about "AI safety," AKA "Will the chatbot become a superintelligence that turns the whole human race into paperclips?"

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

But no one is asking, "What will we do if" – when – "the AI bubble pops and most of this stuff disappears overnight?"

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/12/19/bubblenomics/#pop

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

--

tom_bullock (modified) https://www.flickr.com/photos/tombullock/25173469495/

CC BY 2.0 https://creativecommons.org/licenses/by/2.0/

[tecdisha]

Top AI Tools & Artificial Intelligence Tools I Can't Live Without

You might ask, "Why would anyone need so many AI tools?" The truth is, each of these "AI tools" plays a unique role, and once you explore them, you'll likely want to include them in your daily life too.

Over the years, I’ve experimented with various "artificial intelligence tools" and have found these to be the "best AI tools" that help me stay productive and creative every day. Here’s a detailed look at the "Top 5 AI tools" I rely on:

[mark-matos]

AI Revolution: How Cutting-Edge Tech is Transforming the Medical World and Banishing Paperwork Nightmares

Welcome to the future of medicine, where Artificial Intelligence (AI) is not only revolutionizing patient care but also liberating healthcare professionals from the ever-present burden of paperwork. Gone are the days when doctors and nurses had to spend countless hours on administrative tasks, as AI is ushering in a new era of efficiency and precision. Let's take a deep dive into the transformative power of AI and how it's changing the game for the medical community.

The AI Buzz at HIMSS Conference

At the HIMSS Global Health Conference in Chicago, the medical community was abuzz with the potential of AI in healthcare. With giants like Microsoft, Google, and Amazon showcasing their latest AI-driven health applications, it was evident that the industry is on the cusp of a massive transformation. The focus was primarily on generative AI, a technology that can create text or images from complex prompts provided by users.

The AI-Driven Administrative Revolution

One of the most promising applications of AI in healthcare is streamlining administrative tasks, which can be both time-consuming and mentally exhausting for clinicians. By automating routine paperwork, AI can significantly reduce the workload of healthcare professionals, allowing them to devote more time and energy to patient care.

Empowering Doctors with Epic Systems and Microsoft

Microsoft has expanded its partnership with Epic Systems, a healthcare software company that provides electronic health records solutions. Epic's AI technology generates draft responses to patient messages, saving doctors time and effort. This groundbreaking collaboration signifies the potential of AI to serve as an impactful hypothesis generation tool for physicians.

Amazon Web Services and Philips: A Dynamic Duo

Amazon Web Services (AWS) announced an expanded partnership with Philips, a Netherlands-based health technology company. AWS will support Philips' cloud-based and AI initiatives, such as helping radiologists analyze scans and medical images more quickly. By harnessing generative AI technology, Philips aims to simplify clinical workflows and enhance its imaging capabilities.

Google Cloud: Streamlining Health Insurance Claims

Google Cloud's Claims Acceleration Suite uses AI to streamline health insurance claims processing and prior authorization. By converting unstructured data into a structured format, Google's AI helps alleviate the administrative burden for providers, making the claims process more efficient.

The Challenge: Ensuring Responsible AI Deployment

While generative AI holds tremendous potential for improving administrative efficiency in healthcare, it is essential to deploy the technology responsibly. AI systems are vulnerable to bias and discrimination if trained on unrepresentative data, which can lead to inadequate decision-making or treatment plans. The medical community must work together to ensure that AI is equitable and does not cause harm to patients.

The AI revolution is well underway in the medical community, promising to transform healthcare by reducing paperwork and streamlining administrative tasks. As the industry continues to embrace this groundbreaking technology, the possibilities for improved patient care and healthcare professional satisfaction are truly limitless.