#ML algorithms

[tudip123]

Choose AI/ML Algorithms Very Efficiently

The world is getting smarter every day, to keep up to date and satisfy consumer expectation tech companies adapting machine learning algorithms to make things easy but choosing a machine learning algorithm is always a tedious job for techies, there are lots of algorithms present for different kind of problems and we can use this for tackling things in different ways.

The machine learning algorithm’s main goal is to inspect the data and find similar patterns between them, and with that, make detailed predictions. As the name implies, ML algorithms are basically calculations prepared in different ways.

We are creating data every day; we are just surrounded by data in different formats. It comes from a variety of sources: business data, personal social media activity, sensors in the IoT, etc. Machine learning algorithms are used to extract data and turn it into something useful that can serve to automate processes, personalize experiences, and make difficult forecasts that human brains cannot do on their own.

Choosing algorithms solely depends on your project requirements. Given the type of tasks that ML algorithms answer, each type trains in absolute tasks, taking into consideration the limitations of the knowledge that you have and the necessities of your project.

Types of AI/ML Algorithms

Different types of machine learning algorithms are:

Supervised learning

Unsupervised learning

Semi-Supervised learning

Reinforcement learning

Supervised ML algorithm:

This is the most popular ML algorithm because of its flexibility and comprehensiveness, and it is mostly used to do the most common ML tasks. It requires labeled data.

Supervised knowledge depends on supervision; we train the machines utilizing the branded dataset and establish the training; bureaucracy thinks about the output. It allows you to collect data from previous experiences. Helps you improve performance tests using occurrence.

Unsupervised ML algorithm:

Unsupervised learning is typically achieved by using unsupervised machine learning techniques. Using unsupervised algorithms, you can handle problems differently than with supervised algorithms and operate in more complicated ways. Unsupervised learning, however, could be more irregular than the subsequent deep learning and support learning patterns based on natural input.

There are three main tasks in Unsupervised learning, such as:

Clustering: It is a data mining technique used for grouping unlabeled data based on similarities between them.

Association: It uses different rules to find relationships between variables in a given dataset. These plans are frequently secondhand for advertising basket studies and recommendation transformers.

Dimensionality Reduction: It is used when the number of features in a dataset is too high. It reduces the number of data inputs to a controllable size while more maintaining the dossier honor. Often, this technique is secondhand in the preprocessing dossier stage, in the way that when autoencoders erase noise from being able to be seen with eyes dossier to boost picture quality.

Semi-Supervised ML algorithm:

When you are using a training dataset with both labeled and unlabeled data or you can’t decide on whether to use supervised or unsupervised algorithms, Semi-Supervised is the best choice in that case.

Reinforcement ML algorithm:

Reinforcement knowledge algorithms are mostly based on dynamic compute methods. The idea behind this type of ML treasure is to compare investigation and exploitation. Other machine learning algorithms used mapping middle from two points of recommendation and productivity, Unlike directed supervised placement, where the feedback supported by the power is correct set of conduct for performing a task, support education uses rewards and penalties as signals for helpful and negative behavior.

Conclusion

Choosing an ML algorithm is apparently a complex task, particularly if you don’t have a far-reaching background in this field. However, knowledge about the types of algorithms and the tasks that they were created to resolve and solving a set of questions might help you resolve this complication. Learning more about machine learning algorithms, their types, and answering these questions might lead you to an algorithm that’ll be a perfect match for your goal.

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[aretovetechnologies01]

If you happen to have a conversation about technology trends with a business executive, founder, or software engineer, you definitely hear them talk about Machine Intelligence (Artificial Intelligence or AI), Machine Learning (ML), and automation. And they will also most probably tell you about how these technologies are revolutionizing the traditional business scenarios. It is gaining such prominence, that the total funding assigned to ML, globally during the first quarter of 2019 was close to $28.5 billion. With these statistics in mind, organizations have no choice but to dive deeper into AI and ML and learn how these technologies can help them stay relevant.

[aitalksblog]

Machine Learning in Finance: Opportunities and Challenges

(Images made by author with MS Bing Image Creator ) Machine learning (ML), a branch of artificial intelligence (AI), is reshaping the finance industry, empowering investment professionals to unlock hidden insights, improve trading processes, and optimize portfolios. While ML holds great promise for revolutionizing decision-making, it presents challenges as well. This post explores current…

[cimcondigital]

Transforming Predictive Maintenance with CIMCON Digital’s IoT Edge Platform: Unlocking Proactive Asset Management

Introduction

In today’s fast-paced and technologically advanced world, the need for efficient and proactive asset management is paramount for businesses to stay competitive. CIMCON Digital’s IoT Edge Platform emerges as a game-changer in the realm of Predictive Maintenance, empowering organizations to detect anomalies in advance using ML algorithms. This capability not only enables customers to plan schedules well in advance and avoid costly downtime but also provides real-time visibility into the remaining useful life of assets. In this article, we delve into how CIMCON Digital’s IoT Edge Platform revolutionizes Predictive Maintenance with practical examples of proactive asset management.

1. The Challenge of Reactive Maintenance

Traditionally, companies have been plagued by reactive maintenance practices, where assets are repaired or replaced only after failures occur. This reactive approach leads to unexpected downtime, reduced productivity, and increased maintenance costs. Predicting asset failures and planning maintenance schedules in advance is critical to ensure smooth operations, optimize resource allocation, and minimize overall downtime.

2. Empowering Proactive Maintenance with ML Algorithms

CIMCON Digital’s IoT Edge Platform is equipped with advanced Machine Learning algorithms that analyze real-time data from connected assets and machines. By continuously monitoring sensor data and historical performance trends, the platform can accurately detect anomalies and deviations from normal operating patterns. This proactive approach allows businesses to predict potential asset failures well in advance, providing ample time to schedule maintenance activities before any critical failures occur.

3. Planning Ahead to Avoid Downtime

Imagine a scenario in a manufacturing facility where a critical piece of equipment experiences an unexpected failure. The consequences could be disastrous, leading to costly downtime and missed production targets. With CIMCON Digital’s IoT Edge Platform in place, the same equipment would be continuously monitored in real-time. As soon as the platform detects any unusual behavior or signs of potential failure, it triggers an alert to the maintenance team.

Armed with this early warning, the maintenance team can plan the necessary repairs or replacements well in advance, avoiding unplanned downtime and minimizing disruption to production schedules. This capability not only ensures smooth operations but also optimizes maintenance resources and lowers the overall maintenance costs.

4. Real-Time Visibility into Asset Health

The IoT Edge Platform goes beyond detecting anomalies; it also provides real-time insights into the remaining useful life of assets. By analyzing historical performance data and asset health indicators, the platform estimates the remaining operational life of an asset with high accuracy.

Consider a scenario in a utility company managing a fleet of aging turbines. The maintenance team needs to know the remaining useful life of each turbine to plan proactive maintenance and avoid sudden breakdowns. With CIMCON Digital’s IoT Edge Platform, the team can access real-time information on the health of each turbine, enabling them to make data-driven decisions about maintenance schedules, parts replacement, and resource allocation.

5. Benefits of CIMCON Digital's IoT Edge Platform

CIMCON Digital’s IoT Edge Platform offers a host of benefits to businesses seeking to enhance their Predictive Maintenance capabilities:

a) Proactive Decision-making:��By detecting anomalies in advance, the platform enables proactive decision-making, reducing reactive responses and enhancing overall operational efficiency.

b) Minimized Downtime: With the ability to schedule maintenance activities in advance, businesses can avoid costly downtime, leading to increased productivity and higher customer satisfaction.

c) Optimal Resource Allocation: The platform’s real-time visibility into asset health allows for better resource allocation, ensuring that maintenance efforts are targeted where they are most needed.

d) Cost Savings: By avoiding unexpected failures and optimizing maintenance schedules, businesses can significantly reduce maintenance costs and improve their bottom line.

Conclusion:

CIMCON Digital’s IoT Edge Platform empowers businesses to transcend traditional reactive maintenance practices and embrace a proactive approach to asset management. With the platform’s advanced ML algorithms, businesses can detect anomalies in advance, plan maintenance schedules proactively, and gain real-time visibility into asset health. This transformative capability results in minimized downtime, optimized resource allocation, and substantial cost savings. As CIMCON Digital’s IoT Edge Platform continues to revolutionize Predictive Maintenance, businesses can embark on a journey towards greater efficiency, productivity, and long-term sustainability.

[botreetech]

How to Paraphrase Text Using ML Algorithms in Python?

"Delve into the world of text paraphrasing using ML algorithms with this engaging Medium article. Learn how to effectively rephrase text using Python, exploring the innovative techniques and algorithms that make it possible. Enhance your understanding of natural language processing and elevate your paraphrasing skills through this informative read on Nerd For Tech."

[edvancers]

https://edvancer.in/10-must-know-machine-learning-algorithms-for-novice/

[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

[nixthelapin]

Anyone else deeply disturbed that at middle school teacher in a kids show was named after a type of underwear? A type of underwear known to enhance the bust I might add

(Mme Caline Bustier)

A bustier:

…yikes

[robbyykeene]

Hey, quick non-CK question - what do you mean you got in trouble for hoarding URLs?? I didn't even know that was a THING?

Yeah haha. Not sure how long you’ve been on tumblr, but back in the day (like 10-12+ years ago) hoarding URLs was a big thing, especially in fandom spaces. Basically the idea was that you create a bunch of inactive sideblogs to save a URL that you might want to use in the future. At this point in time, switching back and forth between URLs was also a lot more common. I think some people even used to sell their URLs, or trade other URLs for them which is crazy. There was an entire URL hoarding real estate. It was actually a huge problem lol, I remember wanting to enter certain fandom spaces but being unable to get a URL related to it. And back then I feel like fandoms were a little harder to breach into, especially if you didn’t have a blog that looked like it should belong there—or maybe I was just younger and didn’t know how to interact with people lol, but either way it was still frustrating.

However, Tumblr has since (not sure when exactly) banned URL hoarding. Idk what exactly they quantify as hoarding, but I did have at least a few dozen URLs saved from over a decade ago that I honestly didn’t even remember I had. Most of them were related to emo bands lmao. I think what happened in my case is that this sideblog got caught in the tumblr spam filter somehow, which is why for months I wasn’t able to reply to people and was bot having posts appear in the tags. A lot of people have been having problems with being incorrectly flagged as spam in the last couple years, due to the algorithms they use to detect potential spam accounts. The real problem is that once your account has been flagged tumblr doesn’t notify you or communicate the problem to you, so if you don’t know what to look for (which I didn’t) you don’t know how to properly report the problem. I contacted support a few times but because I reported it as a bug, I suspect it didn’t go to the right place. Eventually this led to my account being terminated without warning or notification. I reached out to tumblr support on multiple platforms, and I think when they looked into the issue they saw that I had a bunch of URLs hoarded and suspended my main account (but restored access to this one). I deleted all the saved URLs, emailed support back, and they released my main account back. All in all I’m just happy with how quickly they resolved the issue tbh, I know a lot of people have spent months trying to get a terminated/suspended account back so I feel pretty grateful. And honestly I think it’s good that tumblr enforces the ban on URL hoarding, bc I remember how upsetting it was being like 12 years old and unable to get a URL related to the thing I really cared about.

[mlearningai]

How do you do Machine Learning research from scratch?

[haruspexism]

relating things youve learned in other classes to current classes is so fun and gives you an ego trip like no other i absolutely recommend it

[raffaellopalandri]

Statistical Tools

Daily writing promptWhat was the last thing you searched for online? Why were you looking for it?View all responses Checking which has been my most recent search on Google, I found that I asked for papers, published in the last 5 years, that used a Montecarlo method to check the reliability of a mathematical method to calculate a team’s efficacy. Photo by Andrea Piacquadio on Pexels.com I was…

[krakenartificer]

[Image Description: A screenshot of a series of tweets from C.W. Howell (@cwhowell123) Tweet 1: So I followed @GaryMarcus' suggestion and had my undergrad class use chatGPT for a critical assignment. I had them all generate an essay using a prompt I gave them, and then their job was to "grade" it -- look for hallucinated info and critique its analysis. *All 63* essays had Tweet 2: hallucinated information. Fake quotes, fake sources, or real sources misunderstood and mischaracterized. Every single assignment. I was stunned -- I figured the rate would be high, but not that high.

Tweet 3: The biggest takeaway from this was that the students all learned that it isn't fully reliable. Before doing it, many of them were under the impression that it was always right. Their feedback largely focused on how shocked they were that it could mislead them. Probably 50% of them

Tweet 4: were unaware that it could do this. All of them expressed fears and concerns about mental atrophy and the possibility of misinformation/fake news. One student was worried that their neural pathways formed from critical thinking would start to degrade or weaken. One other student

Tweet 5: opined that AI both knew more than us but is dumber than we are since it cannot think critically. She wrote, "I'm not worried about AI getting to where we are now. I'm much more worried about the possibility of of us reverting to where AI is."]

*****

OK, I'm going to try to ask this in the nicest possible way, because clearly I am having an XKCD 2501 moment (https://m.xkcd.com/2501/) and I have massively over-estimated general understanding of what chatGPT does. So I need to correct my biased viewpoint, and for that I need people to explain to me. So.

People who were under the impression that chatGPT is always right, that it's fully reliable ... or who were under the impression that out of 63 essays, you'd expect to get unreliable information in much fewer than 63 cases ... or who were thinking that this unreliability can be easily circumvented by asking chatGPT if its output is accurate ... basically, anyone who is surprised by this thread

this is a genuine and not-condescending question: Why? What experiences or sources or reasoning led you to think that? What is it about chatGPT, or about the way people are talking about chatGPT, that makes you trust it so much more than you would trust your phone's autocorrect function?

Because my industry is clearly not doing it's damn job, and I need to understand where the disconnect is. What are we forgetting to explain, or are explaining poorly, or are using terrible terminology, or whatever it is we're screwing up, that left you with the impression you have/had about this technology?

[jcmarchi]

Andy Nightingale, VP of Product Marketing at Arteris – Interview Series

New Post has been published on https://thedigitalinsider.com/andy-nightingale-vp-of-product-marketing-at-arteris-interview-series/

Andy Nightingale, VP of Product Marketing at Arteris – Interview Series

Andy Nightingale, VP of Product Marketing at Arteris is a seasoned global business leader with a diverse background in engineering and product marketing. He’s a Chartered Member of the British Computer Society and the Chartered Institute of Marketing, and has over 35 years of experience in the high-tech industry.

Throughout his career, Andy has held a range of roles, including engineering and product management positions at Arm, where he spent 23 years. In his current role as VP of product marketing at Arteris, Andy oversees the Magillem system-on-chip deployment tooling and FlexNoC and Ncore network-on-chip products.

Arteris is a catalyst for system-on-chip (SoC) innovation as the leading provider of semiconductor system IP for the acceleration of SoC development. Arteris Network-on-Chip (NoC) interconnect intellectual property (IP) and SoC integration technology enable higher product performance with lower power consumption and faster time to market, delivering proven flexibility and better economics for system and semiconductor companies, so innovative brands are free to dream up what comes next.

With your extensive experience at Arm and now leading product management at Arteris, how has your perspective on the evolution of semiconductor IP and interconnect technologies changed over the years? What key trends excite you the most today?

It’s been an extraordinary journey—from my early days writing test benches for ASICs at Arm to helping shape product strategy at Arteris, where we’re at the forefront of interconnect IP innovation. Back in 1999, system complexity rapidly accelerated, but the focus was still primarily on processor performance and essential SoC integration. Verification methodologies were evolving, but interconnect was often seen as a fixed infrastructure—necessary but not strategic.

Fast-forward to today and interconnect IP has become a critical enabler of SoC (System-on-Chip) scalability, power efficiency, and AI/ML performance. The rise of chiplets, domain-specific accelerators, and multi-die architectures has placed immense pressure on interconnect technologies to become more adaptive, innovative, physically, and software-aware.

One of the most exciting trends I see is the convergence of AI and interconnect design. At Arteris, we’re exploring how machine learning can optimize NoC (Network-on-Chip) topologies, intelligently route data traffic, and even anticipate congestion to improve real-time performance. This is not just about speed—it’s about making systems more innovative and responsive.

What excites me is how semiconductor IP is becoming more accessible to AI innovators. With high-level SoC configuration IP and abstraction layers, startups in automotive, robotics, and edge AI can now leverage advanced interconnect architectures without needing a deep background in RTL design. That democratization of capability is enormous.

Another key shift is the role of virtual prototyping and system-level modeling. Having worked on ESL (Electronic System Level) tools early in my career, it’s rewarding to see those methodologies now enabling early AI workload evaluation, performance prediction, and architectural trade-offs long before silicon is taped out.

Ultimately, the future of AI depends on how efficiently we move data—not just how fast we process it. That’s why I believe the evolution of interconnect IP is central to the next generation of intelligent systems. Arteris’ FlexGen leverages AI driven automation and machine learning to automate NoC (Network-on-Chip) topology generation. How do you see AI’s role evolving in chip design over the next five years?

AI is fundamentally transforming chip design, and over the next five years, its role will only deepen—from productivity aid to intelligent design partner. At Arteris, we’re already living that future with FlexGen, where AI, formal methods, and machine learning are central to automating Network-on-Chip (NoC) topology optimization and SoC integration workflows.

What sets FlexGen apart is its blend of ML algorithms—all combined to initialize floorplans from images, generate topologies, configure clocks, reduce Clock Domain Crossings, and optimize the connectivity topology and its placement and routing bandwidth, streamlining communication between IP blocks. Moreover, this is all done deterministically, meaning that results can be replicated and incremental adjustments made, enabling predictable best-in-class results for use cases ranging from AI assistance for an expert SoC designer to creating the right NoC for a novice.

Over the next five years, AI’s role in chip design will shift from assisting human designers to co-designing and co-optimizing with them—learning from every iteration, navigating design complexity in real-time, and ultimately accelerating the delivery of AI-ready chips. We see AI not just making chips faster but making faster chips smarter.

The semiconductor industry is witnessing rapid innovation with AI, HPC, and multi-die architectures. What are the biggest challenges that NoC design needs to solve to keep up with these advancements?

As AI, HPC, and multi-die architectures drive unprecedented complexity, the biggest challenge for NoC design is scalability without sacrificing power, performance, or time to market. Today’s chips feature tens to hundreds of IP blocks, each with different bandwidth, latency, and power needs. Managing this diversity—across multiple dies, voltage domains, and clock domains—requires NoC solutions that go far beyond manual methods.

NoC solution technologies such as FlexGen help address key bottlenecks: minimizing wire length, maximizing bandwidth, aligning with physical constraints, and doing everything with speed and repeatability.

The future of NoC must also be automation-first and AI-enabled, with tools that can adapt to evolving floorplans, chipset-based architectures, and late-stage changes without requiring complete rework. This is the only way to keep pace with modern SoCs’ massive design cycles and heterogeneous demands and ensure efficient, scalable connectivity at the heart of next-gen semiconductors.

The AI chipset market is projected to grow significantly. How does Arteris position itself to support the increasing demands of AI workloads, and what unique advantages does FlexGen offer in this space?

Arteris is not only uniquely positioned to support the AI chiplet market but has been doing this already for years by delivering automated, scalable Network-on-Chip (NoC) IP solutions purpose-built for the demands of AI workloads including Generative AI and Large Language Models (LLM) compute —supporting high bandwidth, low latency, and power efficiency across increasingly complex architectures.  FlexGen, as the newest addition to the Arteris NoC IP lineup, will play an even more significant role in rapidly creating optimal topologies best suited for different large-scale, heterogeneous SoCs.

FlexGen offers incremental design, partial completion mode, and advanced pathfinding to dynamically optimize NoC configurations without complete redesigns—critical for AI chips that evolve throughout development.

Our customers are already building Arteris technology into multi-die and chiplet-based systems, efficiently routing traffic while respecting floorplan and clock domain constraints on each chiplet. Non-coherent multi-die connectivity is supported over industry-standard interfaces provided by third- party controllers.

As AI chip complexity grows, so does the need for automation, adaptability, and speed. FlexGen delivers all three, helping teams build smarter interconnects—faster—so they can focus on what matters: advancing AI performance at scale.

With the rise of RISC-V and custom silicon for AI, how does Arteris’ approach to NoC design differ from traditional interconnect architectures?

Traditional interconnect architectures were primarily built for fixed-function designs, but today’s RISC-V and custom AI silicon demand a more configurable, scalable, and automated approach than a modified one-size-fits-all solution. That’s where Arteris stands apart. Our NoC IP, especially with FlexGen, is designed to adapt to the diversity and modularity of modern SoCs, including custom cores, accelerators, and chiplets, as mentioned above.

FlexGen enables designers to generate and optimize topologies that reflect unique workload characteristics, whether low-latency paths for AI inference or high-bandwidth routes for shared memory across RISC-V clusters. Unlike static interconnects, FlexGen’s algorithms tailor each NoC to the chip’s architecture across clock domains, voltage islands, and floorplan constraints.

As a result, Arteris enables teams building custom silicon to move faster, reduce risk, and get the most from their highly differentiated designs—something traditional interconnects weren’t built to handle.

FlexGen claims a 10x improvement in design iteration speed. Can you walk us through how this automation reduces complexity and accelerates time-to-market for System-on-Chip (SoC) designers?

FlexGen delivers a 10x improvement in design iteration speed by automating some of the most complex and time-consuming tasks in NoC design. Instead of manually configuring topologies, resolving clock domains, or optimizing routes, designers use FlexGen’s physically aware, AI-powered engine to handle these in hours (or less)—tasks that traditionally took weeks.

As mentioned above, partial completion mode can automatically finish even partially completed designs, preserving manual intent while accelerating timing closure.

The result is a faster, more accurate, and easier-to-iterate design flow, enabling SoC teams to explore more architectural options, respond to late-stage changes, and get to market faster—with higher-quality results and less risk of costly rework.

One of FlexGen’s standout features is wire length reduction, which improves power efficiency. How does this impact overall chip performance, particularly in power-sensitive applications like edge AI and mobile computing?

Wire length directly impacts power consumption, latency, and overall chip efficiency—both in cloud AI / HPC applications that use the more advanced nodes and edge AI inference applications where every milliwatt matters. FlexGen’s ability to automatically minimize wire length—often up to 30%—means shorter data paths, reduced capacitance, and less dynamic power draw.

In real-world terms, this translates to lower heat generation, longer battery life, and better performance-per-watt, all of which are critical for AI workloads at the edge or in mobile environments and the cloud by directly impacting the total cost of ownership (TCO). By optimizing the NoC topology with AI-guided placement and routing, FlexGen ensures that performance targets are met without sacrificing power efficiency—making it an ideal fit for today and tomorrow’s energy-sensitive designs.

Arteris has partnered with leading semiconductor companies in AI data centers, automotive, consumer, communications, and industrial electronics. Can you share insights on how FlexGen is being adopted across these industries?

Arteris NoC IP sees strong adoption across all markets, particularly for high-end, more advanced chiplets and SoCs. That is because it addresses each sector’s top challenges: performance, power efficiency, and design complexity while preserving the core functionality and area constraints.

In automotive, for example, companies like Dream Chip use FlexGen to speed up the intersection of AI and Safety for autonomous driving by leveraging Arteris for their ADAS SoC design while meeting strict power and safety constraints. FlexGen’s smart NoC optimization and generation in data centers help manage massive bandwidth demands and scalability, especially for AI training and overall acceleration workloads.

FlexGen provides a fast, repeatable path to optimized NoC architectures for industrial electronics, where design cycles are tight and product longevity is key. Customers value its incremental design flow, AI-based optimization, and ability to adapt quickly to evolving requirements, making FlexGen a cornerstone for next-generation SoC development.

The semiconductor supply chain has faced significant disruptions in recent years. How is Arteris adapting its strategy to ensure Network-on-Chip (NoC) solutions remain accessible and scalable despite these challenges?

Arteris responds to supply chain disruptions by doubling down on what makes our NoC solutions resilient and scalable: automation, flexibility, and ecosystem compatibility.

FlexGen helps customers design faster and remain more agile to adjust to changing silicon availability, node shifts, or packaging strategies. Whether they are doing derivative designs or creating new interconnects from scratch.

We also support customers with different process nodes, IP vendors, and design environments, ensuring customers can deploy Arteris solutions regardless of their foundry, EDA tools, or SoC architecture.

By reducing dependency on any one part of the supply chain and enabling faster, iterative design, we’re helping customers derisk their designs and stay on schedule —even in uncertain times.

Looking ahead, what are the biggest shifts you anticipate in SoC development, and how is Arteris preparing for them?

One of the most significant shifts in SoC development is the move toward heterogeneous architectures, chiplet-based designs, and AI-centric workloads. These trends demand far more flexible, scalable, and intelligent interconnects—something traditional methods can’t keep up with.

Arteris is preparing by investing in AI-driven automation, as seen in FlexGen, and expanding support for multi-die systems, complex clock/power domains, and late-stage floorplan changes. We’re also focused on enabling incremental design, faster iteration, and seamless IP integration—so our customers can keep pace with shrinking development cycles and rising complexity.

Our goal is to ensure SoC (and chiplet) teams stay agile, whether they’re building for edge AI, cloud AI, or anything in between, all while providing the best power, performance, and area (PPA) no matter the complexity of the design, XPU architecture, and foundry node used.

Thank you for the great interview, readers who wish to learn more should visit Arteris.