#Autonomous Technology

[alanleemask]

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[ai-innova7ions]

Neturbiz Enterprises - AI Innov7ions

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence. These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware. The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact.

[dailyanarchistposts]

Leninism: Why Not

Red Fascism has its roots in Leninist thought, an analysis dating back to critiques in 1939 with The Struggle Against Fascism Begins with the Struggle Against Bolshevism by Otto Rühle[28] and 1921 The Russian Revolution and the Communist Party by “Four Moscow Anarchists”.[29] The latter states:

[State Communism] is not and can never become the threshold of a free, voluntary, non-authoritarian Communist society, because the very essence and nature of governmental, compulsory Communism excludes such an evolution. Its consistent economic and political centralization, its governmentalization and bureaucratization of every sphere of human activity and effort, its inevitable militarization and degradation of the human spirit mechanically destroy every germ of new life and extinguish the stimuli of creative, constructive work.

As Gabriel Kuhn declares in his review of Malm’s recent publications:

As long as it is not clear how future Leninism of any stripe – anti-Stalinist, ecological, whatever – will be able to avoid these pitfalls, I really don’t find it terribly reassuring to suggest that, well, somehow it’ll turn out alright this time.

In a similar fashion, Malm does not add new elements to the discussions on escalation of tactics in the environmental movement, contrary to his book’s promise. It might be this hollow radicality that entertains bourgeois circles and will grant him a broad audience separate from the core of radical change.

Furthermore, his ability to brag about his own past flirtations with direct action, from the comfort of middle-class existence in a social democracy, shows that he really has no understanding of ecological struggle. People who actually risk themselves struggling for their land, their survival, our planet, face death or decades in prison. They do not get to put their actions on their resumé to sell books after just a few years. To put it plainly, Malm does not know the meaning of struggle. His expertise is in writing academic papers, securing a comfortable, privileged existence for himself, and climbing the class ladder.

Malm tries to ridicule James C. Scott for his not very popular nor influential book Two Cheers for Anarchism (2012), where he makes silly comments on traffic lights. If you’re familiar with Scott’s work, it becomes apparent that Malm’s attack might be caused by Scotts critique of Lenin in Seeing like a State (1998), exposing Lenin as controlling and elitist. Scott’s work will be mentioned further in the next sections.

[ytmarketing001]

Ever wondered what the future holds for robots? As we dive into some of today's most extraordinary robotic innovations, prepare to be amazed. From lifelike dolls that mimic real people to futuristic maintenance bots and even a robot bartender, these creations are quickly becoming part of our reality. Imagine interacting with a robot so realistically that it feels like talking to someone or seeing a giant robot maintaining railways on its own. Join us as we explore groundbreaking advancements like Iron Tech Dolls Real Lady series, DS Doll's autonomous humanoids, Hansen Robotics' Whitney Cummings robot, and Japan's railway-maintenance transformer robot. Discover how these technologies are shaping our future. If you're enjoying this glimpse into the future of robotics, make sure to hit that subscribe button and give us a thumbs up. Stay tuned for more amazing updates right here on AI Evolves! Please subscribe 🙏    / @aievolves  

[sngl-led-auto-lights]

Why do cars now not get better gas mileage?

That's a perceptive observation—while engine technology has advanced significantly, the average fuel economy of new vehicles in the U.S. has stagnated since 2014 after decades of improvement. Here’s why, distilled into key factors:

⚖️ 1. Vehicle Size & Weight Have Skyrocketed Shift to Trucks/SUVs: Over 80% of new vehicles sold in the U.S. are trucks, SUVs, or crossovers—up from 50% in 2013. These are inherently heavier (+800–1,500 lbs vs. sedans) and less aerodynamic.

Safety Regulations: Airbags, reinforced frames, crash structures, and advanced driver aids add 200–500 lbs per vehicle vs. 2000s-era cars.

Feature Bloat: Panoramic roofs, power seats, advanced infotainment, and sound-deadening materials add mass.

🚀 2. Performance Expectations Have Increased Power Over Efficiency: In 2000, the average vehicle had 172 HP and 0–60 mph in 10.5 seconds. Today: 246 HP and 0–60 in 7.5 seconds—despite added weight.

Turbocharging Trade-Offs: Smaller turbo engines (e.g., 2.0L 4-cyl replacing V6s) boost power but often underperform EPA ratings in real-world driving due to turbo lag and driving habits.

🔧 3. Emissions Controls Reduce Efficiency Gasoline Particulate Filters (GPFs): Mandatory in Europe and some U.S. models post-2020 to trap pollutants—they restrict exhaust flow, increasing backpressure and lowering MPG by 2–5%.

Lean-Burn Engine Limitations: Stricter NOx regulations prevent the use of ultra-lean combustion modes that could boost MPG.

🔌 4. Hybrids/Electric Focus Divert Innovation Automakers invest heavily in EVs and plug-in hybrids to meet regulations. Gains from these vehicles offset stagnant gas-engine MPG in fleet averages, but pure gasoline engines see fewer breakthroughs.

Example: Toyota shifted R&D from next-gen gasoline engines to solid-state batteries.

🌪️ 5. Aerodynamics Hit a Wall Despite advanced modeling, drag reduction gains are marginal:

1980s sedans: Cd ~0.35

2024 sedans: Cd ~0.27 (optimal)

SUVs/trucks: Cd ~0.30–0.40 due to height and frontal area. Further improvements are costly and limited by design trends (tall hoods, aggressive grilles).

📈 6. The CAFE Standards Loophole Corporate Average Fuel Economy (CAFE) gives trucks/SUVs looser targets vs. cars. A "large pickup" only needs 25 MPG by 2026 vs. 52 MPG for a sedan.

Automakers exploit this by shifting production to light trucks—artificially lowering fleet efficiency.

⛽ 7. Real-World vs. Lab Testing EPA test cycles (last updated in 2008) underestimate real-world driving:

Aggressive acceleration

Higher highway speeds (75–80 mph vs. 48 mph test cycle)

Use of AC/heat

Modern engines optimized for tests often underdeliver on roads.

💡 Why It’s Not All Bad: Hidden Efficiency Gains Engine Tech: Direct injection, variable valve timing, and 10-speed transmissions do improve efficiency—but gains are consumed by weight and power demands.

Hybridization: Mild-hybrid systems (e.g., Ford F-150) add 2–4 MPG without plug-in complexity.

High-Efficiency Exceptions: Vehicles prioritizing MPG (e.g., Toyota Prius, Honda Accord Hybrid) achieve 50+ MPG—proving it’s possible without compromises.

🔮 Future Trajectory Stricter CAFE 2026 rules may push fleet averages to 52 MPG (vs. 35.2 MPG today).

Turbo-hybrid systems (e.g., Ford Maverick: 37 MPG pickup) and PHEVs bridge the gap.

Weight reduction via aluminum/steel composites remains critical but expensive.

✅ Key Takeaway

Cars are more efficient per pound and per horsepower than ever—but consumer demand for large, fast, feature-heavy vehicles and regulatory trade-offs have hidden these gains. The push toward electrification will likely accelerate net efficiency, but core gasoline engines face diminishing returns without a revolution in materials and aerodynamics.

[nando161mando]

CAPTCHAs tech companies exploiting free labor to train AI vision for defense contractors military drones and autonomous weapons

[govindhtech]

Google Cloud’s BigQuery Autonomous Data To AI Platform

BigQuery automates data analysis, transformation, and insight generation using AI. AI and natural language interaction simplify difficult operations.

The fast-paced world needs data access and a real-time data activation flywheel. Artificial intelligence that integrates directly into the data environment and works with intelligent agents is emerging. These catalysts open doors and enable self-directed, rapid action, which is vital for success. This flywheel uses Google's Data & AI Cloud to activate data in real time. BigQuery has five times more organisations than the two leading cloud providers that just offer data science and data warehousing solutions due to this emphasis.

Examples of top companies:

With BigQuery, Radisson Hotel Group enhanced campaign productivity by 50% and revenue by over 20% by fine-tuning the Gemini model.

By connecting over 170 data sources with BigQuery, Gordon Food Service established a scalable, modern, AI-ready data architecture. This improved real-time response to critical business demands, enabled complete analytics, boosted client usage of their ordering systems, and offered staff rapid insights while cutting costs and boosting market share.

J.B. Hunt is revolutionising logistics for shippers and carriers by integrating Databricks into BigQuery.

General Mills saves over $100 million using BigQuery and Vertex AI to give workers secure access to LLMs for structured and unstructured data searches.

Google Cloud is unveiling many new features with its autonomous data to AI platform powered by BigQuery and Looker, a unified, trustworthy, and conversational BI platform:

New assistive and agentic experiences based on your trusted data and available through BigQuery and Looker will make data scientists, data engineers, analysts, and business users' jobs simpler and faster.

Advanced analytics and data science acceleration: Along with seamless integration with real-time and open-source technologies, BigQuery AI-assisted notebooks improve data science workflows and BigQuery AI Query Engine provides fresh insights.

Autonomous data foundation: BigQuery can collect, manage, and orchestrate any data with its new autonomous features, which include native support for unstructured data processing and open data formats like Iceberg.

Look at each change in detail.

User-specific agents

It believes everyone should have AI. BigQuery and Looker made AI-powered helpful experiences generally available, but Google Cloud now offers specialised agents for all data chores, such as:

Data engineering agents integrated with BigQuery pipelines help create data pipelines, convert and enhance data, discover anomalies, and automate metadata development. These agents provide trustworthy data and replace time-consuming and repetitive tasks, enhancing data team productivity. Data engineers traditionally spend hours cleaning, processing, and confirming data.

The data science agent in Google's Colab notebook enables model development at every step. Scalable training, intelligent model selection, automated feature engineering, and faster iteration are possible. This agent lets data science teams focus on complex methods rather than data and infrastructure.

Looker conversational analytics lets everyone utilise natural language with data. Expanded capabilities provided with DeepMind let all users understand the agent's actions and easily resolve misconceptions by undertaking advanced analysis and explaining its logic. Looker's semantic layer boosts accuracy by two-thirds. The agent understands business language like “revenue” and “segments” and can compute metrics in real time, ensuring trustworthy, accurate, and relevant results. An API for conversational analytics is also being introduced to help developers integrate it into processes and apps.

In the BigQuery autonomous data to AI platform, Google Cloud introduced the BigQuery knowledge engine to power assistive and agentic experiences. It models data associations, suggests business vocabulary words, and creates metadata instantaneously using Gemini's table descriptions, query histories, and schema connections. This knowledge engine grounds AI and agents in business context, enabling semantic search across BigQuery and AI-powered data insights.

All customers may access Gemini-powered agentic and assistive experiences in BigQuery and Looker without add-ons in the existing price model tiers!

Accelerating data science and advanced analytics

BigQuery autonomous data to AI platform is revolutionising data science and analytics by enabling new AI-driven data science experiences and engines to manage complex data and provide real-time analytics.

First, AI improves BigQuery notebooks. It adds intelligent SQL cells to your notebook that can merge data sources, comprehend data context, and make code-writing suggestions. It also uses native exploratory analysis and visualisation capabilities for data exploration and peer collaboration. Data scientists can also schedule analyses and update insights. Google Cloud also lets you construct laptop-driven, dynamic, user-friendly, interactive data apps to share insights across the organisation.

This enhanced notebook experience is complemented by the BigQuery AI query engine for AI-driven analytics. This engine lets data scientists easily manage organised and unstructured data and add real-world context—not simply retrieve it. BigQuery AI co-processes SQL and Gemini, adding runtime verbal comprehension, reasoning skills, and real-world knowledge. Their new engine processes unstructured photographs and matches them to your product catalogue. This engine supports several use cases, including model enhancement, sophisticated segmentation, and new insights.

Additionally, it provides users with the most cloud-optimized open-source environment. Google Cloud for Apache Kafka enables real-time data pipelines for event sourcing, model scoring, communications, and analytics in BigQuery for serverless Apache Spark execution. Customers have almost doubled their serverless Spark use in the last year, and Google Cloud has upgraded this engine to handle data 2.7 times faster.

BigQuery lets data scientists utilise SQL, Spark, or foundation models on Google's serverless and scalable architecture to innovate faster without the challenges of traditional infrastructure.

An independent data foundation throughout data lifetime

An independent data foundation created for modern data complexity supports its advanced analytics engines and specialised agents. BigQuery is transforming the environment by making unstructured data first-class citizens. New platform features, such as orchestration for a variety of data workloads, autonomous and invisible governance, and open formats for flexibility, ensure that your data is always ready for data science or artificial intelligence issues. It does this while giving the best cost and decreasing operational overhead.

For many companies, unstructured data is their biggest untapped potential. Even while structured data provides analytical avenues, unique ideas in text, audio, video, and photographs are often underutilised and discovered in siloed systems. BigQuery instantly tackles this issue by making unstructured data a first-class citizen using multimodal tables (preview), which integrate structured data with rich, complex data types for unified querying and storage.

Google Cloud's expanded BigQuery governance enables data stewards and professionals a single perspective to manage discovery, classification, curation, quality, usage, and sharing, including automatic cataloguing and metadata production, to efficiently manage this large data estate. BigQuery continuous queries use SQL to analyse and act on streaming data regardless of format, ensuring timely insights from all your data streams.

Customers utilise Google's AI models in BigQuery for multimodal analysis 16 times more than last year, driven by advanced support for structured and unstructured multimodal data. BigQuery with Vertex AI are 8–16 times cheaper than independent data warehouse and AI solutions.

Google Cloud maintains open ecology. BigQuery tables for Apache Iceberg combine BigQuery's performance and integrated capabilities with the flexibility of an open data lakehouse to link Iceberg data to SQL, Spark, AI, and third-party engines in an open and interoperable fashion. This service provides adaptive and autonomous table management, high-performance streaming, auto-AI-generated insights, practically infinite serverless scalability, and improved governance. Cloud storage enables fail-safe features and centralised fine-grained access control management in their managed solution.

Finaly, AI platform autonomous data optimises. Scaling resources, managing workloads, and ensuring cost-effectiveness are its competencies. The new BigQuery spend commit unifies spending throughout BigQuery platform and allows flexibility in shifting spend across streaming, governance, data processing engines, and more, making purchase easier.

Start your data and AI adventure with BigQuery data migration. Google Cloud wants to know how you innovate with data.

[techsikhm]

[danieldavidreitberg]

Fuel your body with precision! 💪 Daniel Reitberg explores how AI optimizes nutrient intake for a healthier lifestyle. 🍎 #NutritionTech #AIHealth #SmartDiet #WellnessInnovation #DanielReitberg

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

The technology industry has seen significant advancements over the past few decades, but nothing quite as transformative as quantum computing promises to be. Why Quantum Computing Will Change the Tech Landscape is not just a matter of speculation; it’s grounded in the science of how we compute and the immense potential of quantum mechanics to revolutionise various sectors. As traditional…

[techtuv]

These autonomous parking robot platforms will park your car for you.

[dailyanarchistposts]

Introduction

Authoritarianism is on the rise as a key talking point when it comes to finding solutions to the ecological crisis. The same attributes that predominate technological society – apathy, fear, cognitive overload and feeling a lack of agency[1] – are more and more reflected in the mainstream environmental movement, leading us to believe in new leaders, figureheads and ideas, such as green growth.[2] More on this later.

Lately, I have come across multiple texts by Andreas Malm, author and associate senior lecturer at Lund University, who is one such authoritarian calling for an “Ecological Leninism”.

In his recent interview with Verso books[3] he was asked:

How do you explain the gap between the relative dynamism of ecological Marxist theories – in Anglo-Saxon countries in particular – and the weakness of the political intervention of Marxists in these movements?

Malm answers:

Ecological Marxism has a tendency to cripple itself by staying inside academia. It needs to engage with and reach out to the actual movements in the field. Anarchist ideas should be combated; they will take us nowhere. I think it’s time to start experimenting with things like ecological Leninism or Luxemburgism or Blanquism. But the weakness of Marxism in ecological politics is of course inextricable from its nearly universal weakness at this moment in time (i.e., one symptom of the crisis of humanity, alongside acidification of the oceans and everything else).

Malm represents a Nordic example of eco-modernist [R.F. – see ‘The Decoupling Thesis’] authoritarian thought. Establishing a false dichotomy (e.g. centralized vs decentralized) between anarchistic approaches to change making, Malm meanwhile fails to reflect on the impacts of authoritarian systems in any honest way. This combines with a detached and warped perception of the environmental movement’s recent history.

In How to Blow Up a Pipeline, Malm advocates, but also shits on direct action. Clearly detached from ecological struggles, referring to anarchists attacks as not big enough, he draws on the work of Micheal Loadenthal who documented “27,100 actions between 1973 and 2010,” in an attempt to discredit decentralized action.[4]

“All those thousands of monkeywrenching actions achieved little if anything,” explains Malm, “and had no lasting gains to show for them. They were not performed in a dynamic relation to a mass movement, but largely in a void.”

Ignoring the actions of the remaining Leftist governments (Ecuador, Bolivia, Venezuela, Nicaragua, etc.), it is clear Malm has no idea what these actions advocate, let alone the continuation and intensification of eco-anarchist attacks in Europe and the rest of the world between 2010–2016 (see Return Fire magazine, 325, Act for Freedom Now, Avalanche etc.). More still, many of these actions, especially Earth Liberation Front (ELF) actions, were supported by local struggles.[5]

He conveniently forgets all the direct actions and sabotage in direct connection to popular movements that helped save wetlands and stop motorways across the UK [R.F. – see Return Fire vol.4 pg89], or the vital role decentralized direct action and sabotage play in the highly effective struggle of the Mapuche people to recover their territory [R.F. – see Return Fire vol.3 pg59], to name just two examples – and there are countless.

And because environmental justice and social justice go hand in hand, we shouldn’t forget the vital role that arson attacks and other major decentralized sabotage actions had in the divestment campaign against the apartheid government of South Africa in the 1980s, or the change in public attitudes towards the racist police in the United States accomplished by direct and decentralized attacks across that country [R.F. – see The Siege of the Third Precinct in Minneapolis].

Popular rejection of the police is now so strong, many cities face a shortage of recruits for their police forces, even as local governments fight to expand funding. This example shows the relative merits of the decentralized, grassroots action that Malm derides, versus the government action pushed by leftwing parties. It is also worth noting that Malm is decidedly uninterested in and uninformed regarding antiracist struggles, while also using racist tropes and promoting the technocratic, institutional framework of colonialism in his writings.

Malm’s limited view is not just a defect of his own thinking. The tendency of technocrats to reduce the interrelated problems of widespread ecological devastation, borders and migration, global hunger and lack of food sovereignty caused by the so-called Green Revolution, is a huge problem.

It opens the door to eco-fascism, and gives the fascists and other racists a seat at the table. If we only think about climate, as though it were distinct from all the other entangled social and ecological problems, then we are forced to focus narrowly on bringing down Co2 within the existing institutional framework of states, NGOs, and corporations. This means that ultimately, each state (as the chief administrative unit) is responsible for bringing down its own emissions.

This leads to an entire accounting game of pushing off emissions responsibility onto poorer countries, closing borders, blaming immigrants, promoting socially and ecologically destructive technologies (e.g. ‘smart’ cities [R.F. – see Return Fire vol.3 pg31], low-carbon infrastructures, idiotic conservation schemes). From Austria to the UK, Green Parties and mainstream environmental movements have already been making alliances of convenience with far right parties and organizations. Now, Malm is trying to put Leninism back on the table, mirroring the resurgence of classical fascist groups and authoritarian governments.

Malm unapologetically remains politically naïve to the realities of repression and state violence endured by people engaging in non-violent sabotage and vandalism actions. In a review by Gabriel Kuhn, an Austrian political author based in Sweden, he calls Malm’s ignorance of struggles and movements “offensive,” pointing out how he ignores “The Green Scare” [R.F. – see Return Fire vol.4 pg82] and how, despite minimizing decentralized action, the ELF and eco-anarchist actions were labeled by the FBI as the “number one domestic terrorist threat.”[6]

People are fighting, dying [R.F. – see Return Fire vol.5 pg56], and serving extended sentences in prison (9–22 years, see June11.org or any Anarchist Black Cross), which Malm flagrantly disrespects for his pseudo-academic circus and attempted revival of Leninism. More importantly, however, many fighters are getting away with these actions inflicting economic costs and real delays. Right now, supposedly ecologically militant people like Malm, should be working to socially normalize committed non-violent (but not pacifist) struggles and spread it to this new generations of “climate youth” continues who are eager to make a difference. Yet Malm instead vomits political ignorance, authoritarian romantics, flagrant disrespect and concerted hostility to the people engaged in this fight.

Malm does not have to be a self-absorbed academic unaccountable to reality. All of us, instead, can think like outlaws, like feral cats, and organize with our friends to destroy what destroys us. While I am unsure if their actions were “performed in a dynamic relation to a mass movement” (whatever that means), most participants were entrenched in various “activist” or non-activist communities (for better and worse).[7] There is a relatively small, but viral movement – everywhere – already in place risking life and limb to confront mines, pipelines, energy infrastructure and the authoritarian systems that maintain them.

Malm’s analysis widely ignores how environmental struggles have so far required all kinds of actors, from saboteurs to lawyers, journalists and lawmakers: There is no either/or. Rather than making a career out of bashing them and for a perverse authoritarian leftist agenda, Malm should be part of organizing prisoner support for eco-warriors, curating information nights on struggles, securing lawyers, influencing public policy to eliminate terrorism enhancement charges and so on. There is so much people can do in general, but also established academics. Why not support Indigenous land defense, eco-anarchist attack and actually begin organizing against the sources of ecological degradation, instead of promoting some hair brained Leninist scheme? The Trotskyites at Verso should also take a good look into the mirror and reconsider their political values, but more so it seems unwise to publish and give a platform to uneducated and poorly researched work like this. Where is the pushback?

[meelsport]

Perplexity AI Stock and AI Market Growth: Top Insights for Investors

Artificial intelligence (AI) is at the forefront of technological innovation, driving significant changes across industries. Companies like Perplexity AI are leading the charge with groundbreaking AI-driven tools, although Perplexity AI itself isn’t publicly traded at this time. However, the broader AI market growth offers numerous opportunities for investors. This article delves into the market…

[netscapenavigator-official]

Honda has to have one of the worst Lane Keep Assist Systems ever.

Like how tf are you, the computer, gonna push me into the rumble strips and them scream at me on your wittle screen about Lane Departure. Like, I know we're departing the lane. You're the one who took the curve too tight!

And don't even get me started on how awful the line tracking it. Like, oops, lane's getting wider. Better move over really fast and jerk the steering wheel really hard. Wait... what's this "interstate exit" you speak of? Better jerk the steering wheel back the other way and get back centered into the lane we were never supposed to leave.

Oh, the car in front of us is entering the turn lane? Better slam on the brakes and match its speed until it's been fully out of the main lane for a solid fifteen seconds.

And then there are the times it just gives you back control without warning. No audible chime at all. You'll be mid-turn on a curvy highway, and it'll just decide "nope, I'm done" and all off a sudden steering assist is disabled, and you're veering into the next lane, and then you realize the car can't see the lines anymore, so you have to jerk really hard back into your lane, and it's just ugh.

Remember when Honda said all of their cars would come standard with Level 5 autonomy by 2025. Lol.

[algoworks]

The integration of AI in drone technology is pushing the boundaries of what’s possible. From enhanced navigation to autonomous operations, AI is transforming the way drones are used across various industries.

Curious about the future of drones? Let’s dive into how AI is leading this revolution!