#A* Search Algorithm In Artificial Intelligence

[jcmarchi]

10 Best AI SDR Tools (October 2024)

New Post has been published on https://thedigitalinsider.com/10-best-ai-sdr-tools-october-2024/

10 Best AI SDR Tools (October 2024)

The landscape of sales development is undergoing a transformation, powered by artificial intelligence that’s redefining how businesses connect with potential customers. AI SDRs (Sales Development Representatives) have emerged as sophisticated systems that automate and enhance the traditional role of human SDRs, handling everything from initial prospecting and lead qualification to scheduling appointments and managing follow-ups.

As businesses increasingly recognize the value of these AI-powered tools in scaling personalized outreach and maintaining consistent engagement across multiple channels, the market has responded with innovative solutions that combine advanced machine learning with practical sales automation. In this guide, we’ll explore the best AI SDR tools available, examining how they’re helping organizations streamline their sales processes while maintaining the personal touch that’s crucial for successful customer relationships.

Laxis stands at the forefront of AI-powered sales development, revolutionizing how businesses approach lead generation and qualification. At its core, the platform leverages a vast global database of over 700 million contacts, ensuring that sales teams have access to an unprecedented pool of potential prospects. What sets Laxis apart is its sophisticated approach to automating the entire sales development process, from initial contact identification to personalized outreach execution.

The platform’s standout feature is its hyper-personalization capability, which goes beyond basic automation to craft individually tailored communications for each prospect. By analyzing prospect data and behavior patterns, Laxis creates engaging outreach campaigns that resonate with potential customers. The platform also includes an innovative AI Cold Calling feature that maintains natural, human-like interactions while scaling voice outreach efforts.

Key Features:

Automated lead generation and qualification through AI-powered prospect analysis

Hyper-personalized email creation using advanced natural language processing

AI Cold Calling with human-like interactions for scaled voice outreach

Real-time analytics and optimization for campaign performance

Seamless CRM integration for streamlined workflow management

Visit Laxis →

Alisha by Floworks represents a significant leap forward in AI SDR technology, powered by its proprietary ThorV2 engine. This innovative platform has demonstrated remarkable capabilities, outperforming traditional Large Language Models (LLMs) like GPT-4 and Claude-3 with an impressive 90.1% accuracy rate and 27.5% faster latency, all while maintaining superior cost-effectiveness.

The platform’s efficiency stems from its sophisticated research capabilities, which scan across 180 web sources to create deeply personalized outreach campaigns. This comprehensive approach to data gathering and analysis enables Alisha to craft messages that truly resonate with prospects, leading to higher engagement rates and more successful conversions.

Key Features:

Multichannel outreach capabilities spanning email, LinkedIn, and hybrid approaches

In-depth research across 180 web sources for enhanced personalization

Full end-to-end automation of the sales development process

Dynamic self-training capability for continuous improvement

Intelligent response system for real-time prospect engagement

Visit Alisha bt Floworks →

AiSDR delivers a comprehensive solution for businesses looking to streamline their entire sales outreach process through advanced automation and personalization. This versatile platform stands out for its ability to manage both outbound and inbound marketing strategies effectively, providing a unified approach to sales development that adapts to various business needs.

The platform’s strength lies in its intelligent lead engagement system, which uses insights from previous interactions to address common objections and guide prospects toward booking calls with sales representatives. This adaptive approach, combined with automated lead scoring and qualification, ensures that sales teams can focus their energy on conducting meetings with the most promising prospects.

Key Features:

AI-generated personalized email campaigns with dynamic content adaptation

Multi-channel outreach support for comprehensive prospect engagement

Intelligent lead engagement and scoring for optimal prioritization

Automated content generation based on proven sales frameworks

Advanced booking optimization with integrated scheduling capabilities

Visit AiSDR →

Gong has established itself as a leading Revenue Intelligence platform and AI SDR, leveraging advanced AI technology specifically designed for revenue teams. The platform’s sophisticated approach to sales intelligence is built on over 40 proprietary AI models, trained on billions of high-quality sales interactions. This deep learning foundation enables Gong to provide unparalleled analysis of customer interactions across various channels.

What truly distinguishes Gong is its comprehensive approach to generative AI applications in sales. The platform goes beyond simple conversation analysis, automatically generating critical insights from customer interactions, including pain points, outcomes, and actionable next steps. This allows sales teams to quickly implement strategic changes based on data-driven observations rather than just reviewing call recordings.

Key Features:

40+ specialized AI models built on billions of sales interactions

Generative AI insights for automated analysis and recommendations

Custom Active Learning Models for trend identification

Multilingual capabilities for global team coordination

Enterprise-grade security with robust data protection

Visit Gong →

Ava by Artisan represents the next evolution in AI SDR tools, functioning as a sophisticated digital worker capable of managing complex sales development tasks autonomously. With access to over 300 million B2B contacts, Ava transforms how businesses approach prospecting and lead engagement, offering a level of automation that significantly reduces manual workload while maintaining personalized interactions.

The platform’s true strength lies in its ability to operate in full autopilot mode while maintaining high-quality personalization. Ava conducts individual research on each prospect before crafting targeted outreach messages, ensuring that every interaction is relevant and engaging. This combination of automation and personalization allows sales teams to scale their efforts without sacrificing the quality of their outreach.

Key Features:

Automated prospecting with enriched demographic data

Hyper-personalized outreach based on individual prospect research

Multi-channel approach across email and LinkedIn

Built-in email warmup for optimal deliverability

Automated follow-ups with intelligent timing optimization

Visit Ava by Artisan →

Humantic offers an innovative approach to AI-powered sales development by focusing on the psychological aspects of buyer-seller relationships. The platform stands out for its innovative Personality AI Assistant, which combines advanced disciplines including psycholinguistics, computational psychometrics, I/O psychology, and neuroscience with cutting-edge machine learning to provide unprecedented insights into prospect behavior and preferences.

What truly sets Humantic apart is its ability to analyze a prospect’s digital footprint and generate detailed DISC personality profiles, enabling SDRs to tailor their approach before the first interaction. The platform’s “1-click personalization” feature transforms this psychological insight into actionable communication strategies, allowing sales teams to craft highly personalized outreach that resonates with each prospect’s unique personality type and decision-making style.

Key Features:

Advanced Personality AI Assistant powered by multiple scientific disciplines

DISC profile generation from digital footprint analysis

1-click personalization for tailored communications

Chrome extension for real-time prospect insights

Seamless integration with major CRM and sales platforms

Visit Humantic →

Cognism distinguishes itself in the AI SDR landscape through its unwavering focus on data quality and compliance across global markets. The platform’s Diamond Verified Phone Data® technology sets a new standard for contact accuracy, while its commitment to GDPR and CCPA compliance ensures that sales teams can operate confidently in various regulatory environments.

The platform’s innovative approach to AI-powered search, utilizing ChatGPT-style textual and voice prompts, simplifies the prospecting process while maintaining precision. Cognism enables sales teams to identify and prioritize warm leads who are actively searching for solutions, significantly improving conversion rates.

Key Features:

Diamond Verified Phone Data® for enhanced contact accuracy

AI-powered search with intuitive ChatGPT-style prompts

Intent data powered by Bombora for lead prioritization

Automated data enrichment for comprehensive prospect profiles

Sales trigger event tracking for timely outreach

Visit Cognism →

Outreach has established itself as a powerhouse in the sales engagement space by combining sophisticated AI capabilities with comprehensive workflow automation. The platform’s approach to sales execution goes beyond basic automation, leveraging artificial intelligence to analyze buyer sentiment and intent in email replies, automatically classifying responses and guiding sales teams toward the most effective next steps.

What sets Outreach apart is its multichannel sequencing capability, allowing sales teams to create and execute coordinated campaigns across email, phone, SMS, and social media. This integrated approach, powered by AI-driven analytics and insights, enables organizations to maintain consistent messaging while adapting their outreach strategy based on real-time engagement data and prospect behavior.

Key Features:

Multichannel sequences with AI-optimized coordination

AI-powered sentiment analysis for response classification

Advanced email capabilities with stakeholder management

Integrated call and meeting scheduling system

Automated data sync with major CRM platforms

Visit Outreach →

11x.ai is pioneering the future of sales development with Alice, an autonomous AI-powered SDR. This platform represents a paradigm shift in how businesses approach outbound sales, leveraging massive datasets and advanced AI to create a digital worker capable of replacing traditional, disjointed sales tools with streamlined, automated workflows. Having processed trillions of bytes of data and analyzed over 500 million leads, Alice brings scale and precision to sales development operations.

Key Features:

Autonomous lead sourcing and qualification from vast data sources

Multi-channel prospect engagement with personalized messaging

AI-powered meeting scheduling and follow-up automation

Comprehensive workflow automation and integration capabilities

Visit 11x.ai →

LeadSend stands out in the AI SDR landscape as a purpose-built solution focused on automating the most time-consuming aspects of lead generation and qualification. The platform’s distinctive approach leverages advanced AI algorithms to automate up to 90% of manual lead generation tasks, representing a significant leap forward in sales development efficiency. This high level of automation enables sales teams to redirect their energy from routine prospecting to high-value interactions that directly impact revenue.

The platform’s sophisticated AI engine excels at identifying and qualifying leads that precisely match a company’s ideal customer profile, ensuring that outreach efforts are consistently targeted at the most promising prospects. LeadSend’s intelligent system goes beyond basic automation by optimizing every aspect of the outreach process, from crafting personalized messages to determining the optimal timing for email sends, while continuously learning and adapting based on response patterns and engagement data.

Key Features:

AI-powered lead research and qualification system

Automated personalized email outreach optimization

Intelligent send-time optimization

Response analysis and automatic lead qualification

Continuous learning and strategy refinement capabilities

Visit LeadSend →

Why Use an AI SDR?

The transformation of the sales landscape through AI-powered SDR tools represents a pivotal shift in how sales teams approach their outreach efforts and lead generation strategies. With 81% of sales teams either experimenting with or having fully implemented AI, it’s clear that artificial intelligence has moved from a competitive advantage to a necessary foundation for modern sales development. These sophisticated platforms are revolutionizing the sales process by automating repetitive tasks while maintaining the personal touch crucial for building customer relationships. By leveraging artificial intelligence, sales development representatives can now focus on high-value activities that directly impact revenue, while AI handles everything from data entry and lead qualification to personalized outreach and automated follow-ups.

The impact of AI SDR tools on sales performance is significant and measurable. Sales teams implementing these solutions report generating more qualified leads, shortened sales cycles, and improved closing rates. Through automated sales workflows and intelligent lead scoring, sales professionals can make data-driven decisions based on real-time insights and accurate sales forecasts. This enhanced efficiency allows sales reps to prioritize leads effectively, customize their outreach strategy across multiple channels, and focus on building meaningful customer relationships rather than getting bogged down in manual tasks.

[fancydreamlandtraveler]

7 Smart Ways AI Can Enhance Your Google Search Experience

 AI can make Google searches faster, more accurate, and personalized. By using tools like voice search, AI-powered assistants, filters, and visual search, you can get smarter and more efficient search results. Personalization and AI-based algorithms streamline your search, ensuring relevant information is delivered quickly.

To read this complete blog, Click here! 😊

[mitsde123]

5 AI Trends That Will Shape Digital Marketing in 2024

The landscape of digital marketing is evolving at a breakneck speed, and one of the driving forces behind this transformation is Artificial Intelligence (AI).

As we step into 2024, AI continues to revolutionize how brands connect with their audiences, optimize campaigns, and predict consumer behaviour.

Here are five AI trends that will shape digital marketing this year and how you can stay ahead of the curve with the right skills and certifications.

[troylambert]

Using AI to Do Keyword Research for Authors

Introduction SEO for authors isn’t just a fancy buzzword; it’s the secret sauce to getting your books noticed online. Imagine your book as a needle in a haystack. SEO—or Search Engine Optimization—helps readers find that needle with ease. It’s all about making sure your content appears at the top of search engine results. Keyword research is the cornerstone of effective SEO. By understanding what…

[isubhamdas]

Generative Engine Optimization-Visibility in AI Search

Learn how to boost visibility in AI search engines with Generative Engine Optimization (GEO). Continue reading to discover the strategies and benefits. Search Generative Engine Optimization: A New Paradigm for AI SearchWhy GEO MattersKey Strategies for GEOExpert TipExampleStatisticsFurther Reading Search Generative Engine Optimization: A New Paradigm for AI Search Search Generative Engine…

[livipup]

Artificial Intelligence at Humber College - Final Presentation

[seohabibi]

In the realm of SEO, the battle between AI and human expertise is intensifying. Explore the dynamics, advantages, and limitations of both, shedding light on the ongoing competition for superior SEO. Dive into the nuances of automated algorithms versus human intuition, and discover how businesses can leverage both for optimal search engine success.

[realityfragments]

Firing Up Recommendations: Pyrorank.

Being a bit busy with other things, I didn’t get to write a little but about a new recommendation algorithm which involves artificial intelligence: Pyrorank. It has some lofty claims, largely hidden by academia and academic verbiage. I initially read about it in Researchers devise algorithm to break through ‘search bubbles’ on July 10th and put it into the stack of things I consider interesting.…

[mostlysignssomeportents]

The surprising truth about data-driven dictatorships

Here’s the “dictator’s dilemma”: they want to block their country’s frustrated elites from mobilizing against them, so they censor public communications; but they also want to know what their people truly believe, so they can head off simmering resentments before they boil over into regime-toppling revolutions.

These two strategies are in tension: the more you censor, the less you know about the true feelings of your citizens and the easier it will be to miss serious problems until they spill over into the streets (think: the fall of the Berlin Wall or Tunisia before the Arab Spring). Dictators try to square this circle with things like private opinion polling or petition systems, but these capture a small slice of the potentially destabiziling moods circulating in the body politic.

Enter AI: back in 2018, Yuval Harari proposed that AI would supercharge dictatorships by mining and summarizing the public mood — as captured on social media — allowing dictators to tack into serious discontent and diffuse it before it erupted into unequenchable wildfire:

https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/

Harari wrote that “the desire to concentrate all information and power in one place may become [dictators] decisive advantage in the 21st century.” But other political scientists sharply disagreed. Last year, Henry Farrell, Jeremy Wallace and Abraham Newman published a thoroughgoing rebuttal to Harari in Foreign Affairs:

https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making

They argued that — like everyone who gets excited about AI, only to have their hopes dashed — dictators seeking to use AI to understand the public mood would run into serious training data bias problems. After all, people living under dictatorships know that spouting off about their discontent and desire for change is a risky business, so they will self-censor on social media. That’s true even if a person isn’t afraid of retaliation: if you know that using certain words or phrases in a post will get it autoblocked by a censorbot, what’s the point of trying to use those words?

The phrase “Garbage In, Garbage Out” dates back to 1957. That’s how long we’ve known that a computer that operates on bad data will barf up bad conclusions. But this is a very inconvenient truth for AI weirdos: having given up on manually assembling training data based on careful human judgment with multiple review steps, the AI industry “pivoted” to mass ingestion of scraped data from the whole internet.

But adding more unreliable data to an unreliable dataset doesn’t improve its reliability. GIGO is the iron law of computing, and you can’t repeal it by shoveling more garbage into the top of the training funnel:

https://memex.craphound.com/2018/05/29/garbage-in-garbage-out-machine-learning-has-not-repealed-the-iron-law-of-computer-science/

When it comes to “AI” that’s used for decision support — that is, when an algorithm tells humans what to do and they do it��— then you get something worse than Garbage In, Garbage Out — you get Garbage In, Garbage Out, Garbage Back In Again. That’s when the AI spits out something wrong, and then another AI sucks up that wrong conclusion and uses it to generate more conclusions.

To see this in action, consider the deeply flawed predictive policing systems that cities around the world rely on. These systems suck up crime data from the cops, then predict where crime is going to be, and send cops to those “hotspots” to do things like throw Black kids up against a wall and make them turn out their pockets, or pull over drivers and search their cars after pretending to have smelled cannabis.

The problem here is that “crime the police detected” isn’t the same as “crime.” You only find crime where you look for it. For example, there are far more incidents of domestic abuse reported in apartment buildings than in fully detached homes. That’s not because apartment dwellers are more likely to be wife-beaters: it’s because domestic abuse is most often reported by a neighbor who hears it through the walls.

So if your cops practice racially biased policing (I know, this is hard to imagine, but stay with me /s), then the crime they detect will already be a function of bias. If you only ever throw Black kids up against a wall and turn out their pockets, then every knife and dime-bag you find in someone’s pockets will come from some Black kid the cops decided to harass.

That’s life without AI. But now let’s throw in predictive policing: feed your “knives found in pockets” data to an algorithm and ask it to predict where there are more knives in pockets, and it will send you back to that Black neighborhood and tell you do throw even more Black kids up against a wall and search their pockets. The more you do this, the more knives you’ll find, and the more you’ll go back and do it again.

This is what Patrick Ball from the Human Rights Data Analysis Group calls “empiricism washing”: take a biased procedure and feed it to an algorithm, and then you get to go and do more biased procedures, and whenever anyone accuses you of bias, you can insist that you’re just following an empirical conclusion of a neutral algorithm, because “math can’t be racist.”

HRDAG has done excellent work on this, finding a natural experiment that makes the problem of GIGOGBI crystal clear. The National Survey On Drug Use and Health produces the gold standard snapshot of drug use in America. Kristian Lum and William Isaac took Oakland’s drug arrest data from 2010 and asked Predpol, a leading predictive policing product, to predict where Oakland’s 2011 drug use would take place.

[Image ID: (a) Number of drug arrests made by Oakland police department, 2010. (1) West Oakland, (2) International Boulevard. (b) Estimated number of drug users, based on 2011 National Survey on Drug Use and Health]

Then, they compared those predictions to the outcomes of the 2011 survey, which shows where actual drug use took place. The two maps couldn’t be more different:

https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x

Predpol told cops to go and look for drug use in a predominantly Black, working class neighborhood. Meanwhile the NSDUH survey showed the actual drug use took place all over Oakland, with a higher concentration in the Berkeley-neighboring student neighborhood.

What’s even more vivid is what happens when you simulate running Predpol on the new arrest data that would be generated by cops following its recommendations. If the cops went to that Black neighborhood and found more drugs there and told Predpol about it, the recommendation gets stronger and more confident.

In other words, GIGOGBI is a system for concentrating bias. Even trace amounts of bias in the original training data get refined and magnified when they are output though a decision support system that directs humans to go an act on that output. Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste.

There’s a great name for an AI that’s trained on an AI’s output, courtesy of Jathan Sadowski: “Habsburg AI.”

And that brings me back to the Dictator’s Dilemma. If your citizens are self-censoring in order to avoid retaliation or algorithmic shadowbanning, then the AI you train on their posts in order to find out what they’re really thinking will steer you in the opposite direction, so you make bad policies that make people angrier and destabilize things more.

Or at least, that was Farrell(et al)’s theory. And for many years, that’s where the debate over AI and dictatorship has stalled: theory vs theory. But now, there’s some empirical data on this, thanks to the “The Digital Dictator’s Dilemma,” a new paper from UCSD PhD candidate Eddie Yang:

https://www.eddieyang.net/research/DDD.pdf

Yang figured out a way to test these dueling hypotheses. He got 10 million Chinese social media posts from the start of the pandemic, before companies like Weibo were required to censor certain pandemic-related posts as politically sensitive. Yang treats these posts as a robust snapshot of public opinion: because there was no censorship of pandemic-related chatter, Chinese users were free to post anything they wanted without having to self-censor for fear of retaliation or deletion.

Next, Yang acquired the censorship model used by a real Chinese social media company to decide which posts should be blocked. Using this, he was able to determine which of the posts in the original set would be censored today in China.

That means that Yang knows that the “real” sentiment in the Chinese social media snapshot is, and what Chinese authorities would believe it to be if Chinese users were self-censoring all the posts that would be flagged by censorware today.

From here, Yang was able to play with the knobs, and determine how “preference-falsification” (when users lie about their feelings) and self-censorship would give a dictatorship a misleading view of public sentiment. What he finds is that the more repressive a regime is — the more people are incentivized to falsify or censor their views — the worse the system gets at uncovering the true public mood.

What’s more, adding additional (bad) data to the system doesn’t fix this “missing data” problem. GIGO remains an iron law of computing in this context, too.

But it gets better (or worse, I guess): Yang models a “crisis” scenario in which users stop self-censoring and start articulating their true views (because they’ve run out of fucks to give). This is the most dangerous moment for a dictator, and depending on the dictatorship handles it, they either get another decade or rule, or they wake up with guillotines on their lawns.

But “crisis” is where AI performs the worst. Trained on the “status quo” data where users are continuously self-censoring and preference-falsifying, AI has no clue how to handle the unvarnished truth. Both its recommendations about what to censor and its summaries of public sentiment are the least accurate when crisis erupts.

But here’s an interesting wrinkle: Yang scraped a bunch of Chinese users’ posts from Twitter — which the Chinese government doesn’t get to censor (yet) or spy on (yet) — and fed them to the model. He hypothesized that when Chinese users post to American social media, they don’t self-censor or preference-falsify, so this data should help the model improve its accuracy.

He was right — the model got significantly better once it ingested data from Twitter than when it was working solely from Weibo posts. And Yang notes that dictatorships all over the world are widely understood to be scraping western/northern social media.

But even though Twitter data improved the model’s accuracy, it was still wildly inaccurate, compared to the same model trained on a full set of un-self-censored, un-falsified data. GIGO is not an option, it’s the law (of computing).

Writing about the study on Crooked Timber, Farrell notes that as the world fills up with “garbage and noise” (he invokes Philip K Dick’s delighted coinage “gubbish”), “approximately correct knowledge becomes the scarce and valuable resource.”

https://crookedtimber.org/2023/07/25/51610/

This “probably approximately correct knowledge” comes from humans, not LLMs or AI, and so “the social applications of machine learning in non-authoritarian societies are just as parasitic on these forms of human knowledge production as authoritarian governments.”

The Clarion Science Fiction and Fantasy Writers’ Workshop summer fundraiser is almost over! I am an alum, instructor and volunteer board member for this nonprofit workshop whose alums include Octavia Butler, Kim Stanley Robinson, Bruce Sterling, Nalo Hopkinson, Kameron Hurley, Nnedi Okorafor, Lucius Shepard, and Ted Chiang! Your donations will help us subsidize tuition for students, making Clarion — and sf/f — more accessible for all kinds of writers.

Libro.fm is the indie-bookstore-friendly, DRM-free audiobook alternative to Audible, the Amazon-owned monopolist that locks every book you buy to Amazon forever. When you buy a book on Libro, they share some of the purchase price with a local indie bookstore of your choosing (Libro is the best partner I have in selling my own DRM-free audiobooks!). As of today, Libro is even better, because it’s available in five new territories and currencies: Canada, the UK, the EU, Australia and New Zealand!

[Image ID: An altered image of the Nuremberg rally, with ranked lines of soldiers facing a towering figure in a many-ribboned soldier's coat. He wears a high-peaked cap with a microchip in place of insignia. His head has been replaced with the menacing red eye of HAL9000 from Stanley Kubrick's '2001: A Space Odyssey.' The sky behind him is filled with a 'code waterfall' from 'The Matrix.']

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

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

 — 

Raimond Spekking (modified) https://commons.wikimedia.org/wiki/File:Acer_Extensa_5220_-_Columbia_MB_06236-1N_-_Intel_Celeron_M_530_-_SLA2G_-_in_Socket_479-5029.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

 — 

Russian Airborne Troops (modified) https://commons.wikimedia.org/wiki/File:Vladislav_Achalov_at_the_Airborne_Troops_Day_in_Moscow_%E2%80%93_August_2,_2008.jpg

“Soldiers of Russia” Cultural Center (modified) https://commons.wikimedia.org/wiki/File:Col._Leonid_Khabarov_in_an_everyday_service_uniform.JPG

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

[jcmarchi]

Search Gets Smarter: How OpenAI’s SearchGPT is Changing the Game

New Post has been published on https://thedigitalinsider.com/search-gets-smarter-how-openais-searchgpt-is-changing-the-game/

Search Gets Smarter: How OpenAI’s SearchGPT is Changing the Game

In our increasingly interconnected world, efficient and accurate Web search has become critical. Whether students gather information for their academic projects or professionals want to stay updated with the latest industry trends, search engines have become an essential part of our daily routines. However, while helpful, traditional search engines often come with their challenges. Users frequently encounter vast amounts of information, struggle with irrelevant search results, and must often refine their queries multiple times to find the exact information they need.

This leads to frustration in the users and has consequently led to a growing demand for a more advanced, intuitive, and conversational search experience that can grasp context, engage in meaningful dialogue, and provide precise answers swiftly. This is where SearchGPT comes into play. Developed by OpenAI, SearchGPT is an innovative AI-powered search prototype transforming the search experience. By addressing the shortcomings of traditional search engines, SearchGPT offers a more intelligent, faster, and more personalized way to navigate the Web.

The SearchGPT Prototype

SearchGPT is not simply another search engine; it represents a significant shift in how we interact with information on the Web. It is designed to explore integrating advanced AI models with real-time Web data, aiming to deliver a more refined and human-like search experience. Its primary goal is to offer users accurate, relevant answers supported by clear and trustworthy sources.

Unlike traditional search engines that rely on complex algorithms to rank and display a list of links, SearchGPT operates on a different principle. It engages users in a conversation, directly responding to their queries with detailed and comprehensive answers. For example, if a user plans a vacation and asks, “What are some family-friendly activities in Houston?” SearchGPT would provide a list of websites and generate a contextually relevant and detailed response, including recommendations for parks, museums, theaters, and other attractions suitable for families, and linking to sources where one can book tickets or find additional information.

This conversational capability enables SearchGPT to handle follow-up questions, maintain context, and provide more in-depth responses that evolve as the conversation progresses. It is designed to function less like a traditional tool and more like a knowledgeable assistant that understands and anticipates your needs.

How SearchGPT Works?

At the core of SearchGPT is OpenAI’s Generative Pre-trained Transformer (GPT) technology, a deep learning model trained on vast amounts of text data from a wide range of sources. This extensive training enables SearchGPT to understand and process natural language in a manner that closely mimics human communication.

When one submits a query to SearchGPT, the AI does not simply match keywords to Web pages. Instead, it interprets the intent behind the words in the input string, understands the context, and generates a response that is specifically relevant to the asked question. This capability is compelling for handling complex or ambiguous queries, where traditional search engines may struggle.

For instance, if one is working on learning cloud computing and asks SearchGPT, “What are the main benefits of cloud computing?” Instead of presenting the user with a list of articles, SearchGPT would provide a comprehensive answer. This answer might cover scalability, cost efficiency, and flexibility, all supported by citations from reliable sources. Next, in follow-up, if asked, “How does scalability impact cloud computing?” SearchGPT would effectively continue the conversation, offering detailed information that builds upon the previous response.

This ability to maintain a shared context throughout the interaction represents a significant shift from traditional search engines, which treat each query as an isolated event. SearchGPT’s contextual understanding allows it to deliver more accurate and relevant answers, making the search process faster, more efficient, and less cumbersome.

Example Use Cases

SearchGPT’s versatility makes it a valuable tool across various scenarios, each demonstrating its unique capabilities.

In academic research, students and researchers can use SearchGPT to gather detailed, source-cited information on complex topics quickly. Likewise, in travel planning, SearchGPT simplifies the process by providing cohesive responses to related queries, such as weather conditions, visa requirements, local attractions, and accommodation options. This helps travelers efficiently plan their trips with all the necessary information at their fingertips.

SearchGPT offers accurate, up-to-date information linked to reputable medical sources regarding health inquiries. Similarly, content creators, including writers, journalists, and marketers, can also benefit from SearchGPT. It is a powerful research tool, helping them quickly gather facts, generate ideas, and even draft initial content. For example, a writer working on an article about emerging tech trends can use SearchGPT to gain insights into new technologies, assess potential industry impacts, and gather expert opinions, providing a solid foundation for their work.

SearchGPT’s Collaborative Approach with Publishers to Enhance Digital Integrity

One of SearchGPT’s significant features is its collaborative approach with publishers. In a time when digital content is often shared and repurposed without proper attribution, SearchGPT prioritizes connecting users with the original sources of information. By citing and linking directly to publishers, SearchGPT ensures that content creators receive the recognition and traffic they deserve.

This collaboration goes beyond simple citation. SearchGPT also provides publishers with controls over how their content is accessed and displayed within the AI’s responses. This respect for intellectual property promotes a positive relationship between AI-driven search technologies and the publishing industry, setting a new standard for ethical AI development.

Moreover, by driving traffic to original content creators, SearchGPT helps sustain the journalism and publishing industries, which is vital to the flow of accurate and well-researched information online. In an age where misinformation can spread rapidly, the ability to direct users to credible sources is more important than ever.

Integration with ChatGPT

While SearchGPT is currently a standalone prototype, OpenAI has plans to integrate its most successful features into ChatGPT. This integration will enhance ChatGPT’s capabilities, enabling it to function as a conversational partner and a powerful and intuitive search tool.

The implications of this integration are far-reaching. With ChatGPT integrated with SearchGPT, users could combine advice requests with factual information queries. This integration would allow for comprehensive responses that blend conversational insights with accurate data, all delivered in real-time. As a result, ChatGPT would become a truly multifaceted assistant capable of efficiently supporting a wide range of tasks across various domains.

As AI-powered search becomes more integrated into our digital experiences, the distinction between searching for information and conversing with an AI assistant will continue to blur. This evolution will lead to a more intuitive and engaging way of interacting with information online.

The Bottom Line

SearchGPT marks a new era in how we navigate the Web, offering an intelligent, efficient, and personalized search experience. By blending AI with real-time insights, not only enhances the way we find information but also ensures that content creators are rightfully credited.

The future integration with ChatGPT promises to elevate this even further, turning ChatGPT into a versatile assistant capable of easily handling a wide range of tasks. As SearchGPT continues to evolve, it is ready to redefine our digital interactions, making them more intuitive and impactful.

[the-laws-of-physics-were-harmed]

Oh btw. Artificial "intelligence" is not inherently bad (I put quotation marks around "intelligence" because some people take that to mean that it is sentient. It is not.)

AI art, writing, etc. is generally bad as the data is almost always taken without consent. An AI art program made with data from consenting artists would not be inherently bad (though bad things could still be done with it.) Also, sometimes people ask the AI serious questions that end up with answers that are just plain wrong, because the AI does not actually understand what it is saying, because it is not actually "intelligent"; it is an algorithm that has taken in a lot of words and is working out the most likely order to smash them together in.

There is AI (I'm not sure if it's currently in existence or still being developed) that searches for cancer cells. This is clearly not bad; it helps save lives.

The idea that AI is inherently bad is misleading, but that does not mean that it is inherently good. I think the idea of being "anti AI" misses a lot of nuance, though I do understand it.

[mariacallous]

A few years ago I wrote about how, when planning my wedding, I’d signaled to the Pinterest app that I was interested in hairstyles and tablescapes, and I was suddenly flooded with suggestions for more of the same. Which was all well and fine until—whoops—I canceled the wedding and it seemed Pinterest pins would haunt me until the end of days. Pinterest wasn’t the only offender. All of social media wanted to recommend stuff that was no longer relevant, and the stench of this stale buffet of content lingered long after the non-event had ended.

So in this new era of artificial intelligence—when machines can perceive and understand the world, when a chatbot presents itself as uncannily human, when trillion-dollar tech companies use powerful AI systems to boost their ad revenue—surely those recommendation engines are getting smarter, too. Right?

Maybe not.

Recommendation engines are some of the earliest algorithms on the consumer web, and they use a variety of filtering techniques to try to surface the stuff you’ll most likely want to interact with—and in many cases, buy—online. When done well, they’re helpful. In the earliest days of photo sharing, like with Flickr, a simple algorithm made sure you saw the latest photos your friend had shared the next time you logged in. Now, advanced versions of those algorithms are aggressively deployed to keep you engaged and make their owners money.

More than three years after reporting on what Pinterest internally called its “miscarriage” problem, I’m sorry to say my Pinterest suggestions are still dismal. In a strange leap, Pinterest now has me pegged as a 60- to 70-year-old, silver fox of a woman who is seeking a stylish haircut. That and a sage green kitchen. Every day, like clockwork, I receive marketing emails from the social media company filled with photos suggesting I might enjoy cosplaying as a coastal grandmother.

I was seeking paint #inspo online at one point. But I’m long past the paint phase, which only underscores that some recommendation engines may be smart, but not temporal. They still don’t always know when the event has passed. Similarly, the suggestion that I might like to see “hairstyles for women over 60” is premature. (I’m a millennial.)

Pinterest has an explanation for these emails, which I’ll get to. But it’s important to note—so I’m not just singling out Pinterest, which over the past two years has instituted new leadership and put more resources into fine-tuning the product so people actually want to shop on it—that this happens on other platforms, too.

Take Threads, which is owned by Meta and collects much of the same user data that Facebook and Instagram do. Threads is by design a very different social app than Pinterest. It’s a scroll of mostly text updates, with an algorithmic “For You” tab and a “Following” tab. I actively open Threads every day; I don’t stumble into it, the way I do from Google Image Search to images on Pinterest. In my Following tab, Threads shows me updates from the journalists and techies I follow. In my For You tab, Threads thinks I’m in menopause.

Wait, what? Laboratorially, I’m not. But over the past several months Threads has led me to believe I might be. Just now, opening the mobile app, I’m seeing posts about perimenopause; women in their forties struggling to shrink their midsections, regulate their nervous systems, or medicate for late-onset ADHD; husbands hiring escorts; and Ali Wong’s latest standup bit about divorce. It’s a Real Housewives-meets-elder-millennial-ennui bizarro world, not entirely reflective of the accounts I choose to follow or my expressed interests.

Meta gave a boilerplate response when I asked how Threads weights its algorithm and determines what people want to see. Spokesperson Seine Kim said what I’m seeing is personalized to me based on a number of signals, “such as accounts and posts you have interacted with in the past on both Threads and Instagram. We also consider factors like how recently a post was made and how many interactions it has received.” (A better explanation might be that Threads has a rage-bait problem, as this intrepid reporter learned.)

What scares me most about this is not that Meta has a shitbucket of data on me (old news) or that the health hacks I’m being shown might be completely illegitimate. It’s that I might be lingering on these posts more than I realize, unconsciously shoveling more signals in and anxiously spiraling around my own identity in the process. For those of us who came of age on the internet some 20 to 30 years ago, the way these recommendation systems work now represents a fundamental shift to how we long thought of our lives online. We used to log on to tell people who we were, or who we wanted to be; now the machines tell us who we are, and sometimes, we might even believe them.

As for Pinterest, I granted the company access to my account so they could investigate why the app recommends ageist, AARP-grade content to me in its emails. It turns out I hadn’t actively logged in to the app in over a year, which means the data it has one me is, ironically, old. Back then I was researching paint, so the app thinks I’m still into that.

Then there’s the grandma hair: Not only had I searched on Pinterest for skincare products and hairstyles in the long-ago past, but Pinterest gives a lot of weight to data from other users who have searched for similar items. So perhaps those other, non-identifiable users are into these hairstyles. The company claims its perceived relevance for recommendations has improved over the past year.

Pinterest’s suggested solution for me? Use Pinterest more. Un-pin stuff I don’t like. Threads also suggested I can fine-tune my own feed by swiping left to hide a post or tapping a three-dot menu to indicate I’m not interested. It’s on me, young buck. In both cases, I’m supposed to tell the algorithms who I am.

I’m supposed to do the work. I’m supposed to swipe more. I’ll be so much better off if I do. And so will they.

[solarpunkbusiness]

Steel production contributes over 7% of carbon dioxide (CO2) emissions worldwide. Green steel plants can achieve almost zero emissions, enabling sustainable steelmaking processes and constructing a cleaner, brighter future for the planet.

The search for green steel has led to significant breakthroughs in technology. Advances in carbon capture technologies, electric arc furnaces, and hydrogen-based direct reduction are changing the face of steel production and setting the stage for a more sustainable future.

Digitalization in the steel sector reveals opportunities for systemic optimization, yield and product enhancement, reduced CO2 and greenhouse gas emissions, improved safety, and effective order processing.

Adopting predictive maintenance techniques is one intriguing potential for the steel sector. Steel manufacturing plants can optimize production processes by utilizing advanced technologies like artificial intelligence (AI), deep learning algorithms, and Internet of Things (IoT) sensors. Some of how these technological advancements make green steel plants effective are:

Predictive maintenance facilitates the early detection of equipment faults, reducing unexpected downtime and enhancing overall efficiency.

Artificial intelligence (AI)-powered predictive analytics can examine real-time data from devices and operations to find areas where energy can be saved, lowering carbon emissions and energy use.

Predictive maintenance extends equipment life and minimizes the need for expensive new parts manufacturing by monitoring the condition of essential components and enabling prompt repairs and replacements.

Automated maintenance detects and corrects manufacturing process inefficiencies and can decrease material waste and related environmental effects.

[vidoeslot]

as a tech lover what do u think of ai. love ur art <3

Oh man. This is a hell of a question!!

I think right off the bat I want to say that “AI” as a term is so so deeply misused it may be beyond repair at this point. The broadness of AI cannot be understated. Even the most basic search and sorting algorithms are AI. Chessbots are AI. Speech recognition is AI. Machine translation, camera autofocus, playlist shuffle, spam filtering, antivirus, inverse kinematics, it all uses AI and has used it for years. Every single piece of software you interact with has AI technology in it somewhere.

All of this is mostly unrelated to what most people think of as AI nowadays (generative AI, like chatGPT or midjourney), both of which are entirely unrelated to the science fiction concept of an artificial intelligence.

That said, I'm assuming you're talking about generative AI since that's the hot-button issue. I think it's a very neat technology and one I wish I could be enthusiastic about seeing improve. I also think it is a deeply dangerous technology and we are entirely unprepared for the consequences of unfettered access to and complete trust in AI generation. It's what should be a beneficial technology built on foundations of harm – programmed bias from inextricable structural prejudice in the computer science world, manipulation of sources without creator/user/random person who happened to be caught on a camera once/etc consent – being used for harm – deliberate disinformation, nonsense generated content being taken as fact, violation of personal privacy and consent (as seen with deepfake porn), the list goes on. There's even more I could say about non-generative neural networks (that very reductive reference to "bread scanning AIs they taught to recognize cancer cells" so highly lauded by tumblr) but it just boils down to the same thing; the potential risk of using these technologies irresponsibly far and away outweighs any benefit they might have since there's no actual way to guarantee they can be used in a "good" or "safe" way.

All of it leaves a rotten taste in my mouth and I can't engage with the thought of any generative AI technology because of it. There's just too much at stake and I don't know if it even can be corralled to be used beneficially at this point. The genie's out of the bottle.