#efficient ML

[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.

Click the link below to learn more about the blog Choose AI/ML Algorithms Very Efficiently:

[percki]

loveybug is too busy thinking about kissing to fight supervillains... anytime an akuma shows up she hits it with a comically large hammer harley quinn style until it's flat and she can go back to unsubtly flirting with chat noir

[bugoutreviewgirlie]

Marinette traveling to season 1: whoa, that felt like that was...ten years ago!

Me: ha 😐 that's so funny 😐

[bittersweetresilience]

Adrien Agreste and Borderline Personality Disorder

DISCLAIMER: I've been a bit low on spoons this week, so I haven't gathered as much evidence as I probably could have. Also, I am but a humble student in clinical psychology. This is mainly a thought compilation for @moonieratty!

To introduce the topic, without going into it too much, personality is described by features and behavioral patterns, or traits, consistent across situations and across time. Personality disorders are therefore enduring patterns of highly maladaptive traits evaluated in personal and sociocultural context (Dozois, 2019, p. 290).

Like other disorders, personality disorders have diagnostic criteria. The DSM is used primarily for diagnosis in the US and Canada while the ICD is used more widely in Europe and other places. I'm more familiar with the DSM, but I've included a brief section on the ICD to be comprehensive. It ended up a bit longer than anticipated, so let's go below the cut. Warning for discussions of abuse, self harm, and suicide, and a brief mention of substance use.

Overview of Borderline Personality Disorder

BPD is prevalent in a small percentage of the population, about 1-2% by varying estimates, and is characterized by instability across domains of emotions, identity, interpersonal relationships, and behavior. Its onset is usually in late adolescence or early adulthood and symptoms may diminish with age, after one's thirties or forties, especially with therapeutic intervention. Along with ASPD, it has been the focus of a lot of clinical research; it is unfortunately associated with high rates of non-suicidal self-injury and suicide (APA, 2022, pp. 754–755; Dozois, 2019, pp. 308–309).

Etiological factors for BPD include both biological and environmental disturbances. Of note to our discussion is the heightened risk for BPD in cases of child abuse or neglect, as well as growing up with another family member with a serious mental health condition (NHS, 2022). It's been well established that Gabriel is emotionally neglectful if not outright abusive toward Adrien in multiple ways, so this is a clear risk factor. In addition, although I won't argue definitively for Gabriel having a psychological condition, he did keep Emilie's body in the basement, so there is clearly some kind of disturbance going on.

From a cognitive-behavioral perspective, Linehan argues that BPD stems from families who consistently invalidate childhood emotional experiences and "oversimplify the ease with which life's problems can be solved," which may cause children to learn to seek attention and communicate with others through emotional outbursts (Dozois, 2019, p. 297). This rings true for Adrien and Gabriel as well.

I have to apologize for my son, Ladybug, he's like his mother. Way too overly dramatic. (Jackady)

It doesn't seem like this is the first time Gabriel has thought this, and labeling an emotional reaction as being overly dramatic is very much invalidation of it. As for emotional outbursts, Adrien has had quite a few, mostly as Chat Noir. More on this later.

To round out this first section, attachment theory proposes a connection between poor parent-child attachments and BPD relationship difficulties. Specifically, maladaptive behavior in relationships may stem from childhood development of an anxious-ambivalent attachment style, where intense fears of abandonment interfere with a strong desire for intimacy (Dozois, 2019, p. 310). You can clearly see this in Chat Noir's relationship with Ladybug, and I'm sure other people have discussed Adrien's attachment style more in depth, but suffice to say I think this is an apt description.

Argument from DSM-5-TR

In the DSM, personality disorders are primarily diagnosed on a categorical model. There are a few general criteria which I won't be going into, but I will highlight that personality disorders are stable and pervasive, and would be diagnosed only if they were leading to significant distress or dysfunction. Adrien's mental state is not great, so the latter shouldn't be a problem, but this show does not always pay attention to consistency, so I'm going to be speculating some. Everything in this section is cited to the relevant DSM entry unless otherwise stated.

The DSM characterizes BPD with instability in relationships, self image, and affects, as well as marked impulsivity. It has no exclusion criteria, so BPD can be and frequently is comorbid with other disorders like mood disorders, PTSD, and ADHD. Of the below criteria, five need to be met in order for a diagnosis to be made.

Frantic efforts to avoid real or imagined abandonment

Hey, where're you going? . . . So you're allowed to know her true identity, but I'm not? (Syren)

You're not really replacing me with a turtle, are you? (Anansi)

A lot of people have delved into Adrien's abandonment issues, which feature most prominently in his relationship with Ladybug. His fears of being replaced result in him seeking reassurance from Ladybug, and he can lash out if he does not receive the response he hopes for. Ladybug is his world, and he wants to be hers, so anything infringing on that feels to him like a threat of being abandoned, and he does not like it very much.

Impulsive behaviors like giving up his ring can be interpreted under this lens: he can avoid abandonment by doing the abandoning first. Then it will be him leaving, and not the other way around.

I also interpret Adrien's nightmare (Le Marchand de Sable) as being a fear of being alone as much as it is a fear of being trapped.

Unstable and intense interpersonal relationships alternating between extremes of idealization and devaluation

We'll be united, more powerful and free. We'll defeat Hawk Moth, then we can both run away to an island! Far away from everything. We will live off nothing but fruits, and we will have a little pet hamster and we will name it— (Le Patineur)

I just can't do this anymore. I give up… on everything. Even you . . . If I become Chat Noir again, Ladybug will just end up rejecting me, over and over. (Kuro Neko)

Even though Adrien mostly keeps his head on straight regarding what he's owed and not owed by other people, his relationship with Ladybug is full of highs and lows. He thinks Ladybug is the most amazing girl, but this can recoil quickly into feeling as though Ladybug doesn't care about him enough or isn't meeting his needs.

Furthermore, he developed this idealization of Ladybug as a potential lover or caregiver at one of their first meetings (Cœur de Pierre), and continually sought to spend time together and share the intimate details of their secret identities early on. These are all common to individuals with BPD, as is the switch from idealization to feeling as if the other person "does not care enough, does not give enough, or is not 'there' enough." Ladybug is the only person Adrien consistently projects this instability and intensity on, which might be something interesting to explore.

Identity disturbance: unstable self image or sense of self

When I was a kid, I always wanted to be what my parents wanted me to be! (Exauceur)

But, does that mean Chat Noir is who I really am? Deep down inside? (Kuro Neko)

With all the secret identities, it isn't surprising that Adrien fits this criterion, but canon even explicitly draws a link between Adrien's unstable sense of self and his childhood experiences. He doesn't know who he is, and he distracts from this by being Chat Noir, only to struggle even more when he feels useless and underlooked as his hero self. His behavior shifts dramatically between trying to prove himself with grand gestures and refusing to participate or lashing out. There is definitely something to discuss on this front.

Impulsivity in at least two potentially self-damaging areas

Giving you some extra time . . . I trust you to bring me back, m'lady. (Gamer 2.0)

There are only two liars left in Paris and one of them knows the ultimate way to catch her attention . . . Crazy about you, m'lady. (Mensonge)

This is walking a thin line between impulsivity and suicidal behavior, which would be excluded from this criterion, but I'll list self sacrifice here because I can see an argument for Chat Noir's impulsive behaviors being out of love or the desire to be useful. There may still be some self injurious intent or euphoria, but Chat Noir does have faith in Ladybug to bring him back eventually. Nevertheless, this is impulsive and unhealthy. Chat Noir jumps too quickly to this option to have thought it through.

I can't think of another area, because Adrien isn't old enough for reckless driving, spending, substance use, or sex. This is also a kids' show. Just presenting these options for fanfiction writers out there.

Recurrent suicidal or self mutilating behavior, gestures, or threats

I... I don't know what to do! (Chat Blanc)

This is all our fault . . . Cataclysm. (Culpabysse)

By itself, what happened in Chat Blanc would not be solid evidence, as that was an unprecedentedly traumatic situation. Combined with Culpabysse, though, there is a strong case to be made for at least passive suicidality for this to be able to come up as an option.

You could also interpret the self sacrifice in this category. Suicidal behavior in individuals with BPD is often preempted by fears of rejection or abandonment, so an interesting analysis could be made on this front.

Affective instability due to marked reactivity of mood

He's still only thinking of himself! I just want this terrible day to be over and done with! I hate Christmas! (Pire Noël)

Sorry! Sorry! I didn't mean to make you so mad. I get it. You're sick of me . . . No one can help me, Kagami. (Glaciator 2)

Adrien's prolonged episodes of anger and despair reflect a high reactivity to emotional stressors, especially interpersonal ones. In general, he just doesn't feel very well unless something is actively bringing him joy. Most of his outbursts are brief, though, and I will discuss them as part of a later criterion.

Chronic feelings of emptiness

I'm not Adrien, so I wouldn't know if this is the case, but I can say he has experienced at least one depressive episode (Kuro Neko) and emptiness would not be unfamiliar. You can look at him and decide.

Inappropriate, intense anger or difficulty controlling anger

How was your amazing evening with your "friends"? . . . What do you think? (Glaciator)

Why not? No one'll know if I quit. No one'll care! (Syren)

A two-person plan?! There's only one two-person plan, and that's Ladybug and me! (Sentibulleur)

Give me a break, Miss "I can't even come up with a superhero name"! (Hack-San)

Anger in individuals with BPD can manifest as bitterness, sarcasm, or recurrent verbal outbursts, which Chat Noir absolutely exemplifies. These outbursts are often followed by feelings of shame or guilt and contribute to a feeling of being bad. Chat Noir apologizes after being harsh in Glaciator, and I wouldn't be surprised if he felt badly about the other times. Again, these outbursts are often precipitated by interpersonal fears and perceived threats of abandonment. Unlike other symptoms, this specific one tends to be unique to BPD.

Transient, stress-related paranoid ideation or severe dissociation

I cannot recall any evidence for this but headcanon away, my friends.

All in all, I think Adrien has a strong case for BPD presentation according to the DSM. Canon is not always consistent, but I think it presents an interesting and mostly coherent narrative for this lens. From this perspective, Adrien's behavior reflects a deep intolerance of being alone and a dependence on other people to define the self.

As a corollary, BPD behavioral patterns are frequently linked to self sabotage and self undermining right when a goal is about to be realized, which could manifest as dropping out of school right before graduating or ending a relationship when it seems to be going well. Food for thought. Individuals with BPD may also feel more secure with transitional objects like pets than with interpersonal relationships, which could reflect in Adrien's relationship with Plagg.

To add some subjective understanding to this clinical mumbo jumbo, I've added a source of genuine BPD experiences below (Mind, 2022).

Argument from ICD-11

With the release of the ICD-11, a dimensional model for diagnosis of personality disorders became the new standard. What this means is that individuals are no longer labeled as having 'histrionic' or 'dependent' or, indeed, 'borderline' personality disorders, but are rather assessed as having any form of personality disorder on a sliding scale of severity and with trait domain specifiers (Swales, 2022). To put it more simply, people are diagnosed only with a general personality disorder or personality difficulty which can be optionally specified as deviating on one of the personality traits in the Big Five model, which is well established in personality psychology.

This move has several benefits. Stigmatization related to particular personality disorders can be reduced, and differential diagnosis and comorbidity between personality disorders becomes irrelevant. However, people retain access to treatment and support. Evidence-based treatments like dialectical behavior therapy are particularly well established and crucial for BPD; for this pragmatic purpose, the ICD contains one additional specifier for borderline pattern personality disorder (Bach et al., 2022; Swales, 2022).

The DSM and ICD are designed to be compatible with each other in many ways, and in this case, the borderline specifier in the ICD is directly lifted from the criterion of the DSM (WHO, 2023, 6D11.5). Based on our previous discussion, Adrien would be equally qualified for a diagnosis in France. I would likely describe his personality disorder with moderate severity, where multiple areas of functioning are affected and self harming behaviors may be present, but not all areas or relationships may be equally impacted and traits are not as rigid and inflexible (WHO, 2023, 6D10.0–6D10.2).

Interestingly, the ICD includes three additional manifestations of borderline pattern which are optional and may vary across time (WHO, 2023, 6D11.5).

A view of the self as inadequate, bad, guilty, disgusting, and contemptible

An experience of the self as profoundly different and isolated from other people; a painful sense of alienation and pervasive loneliness

Proneness to rejection hypersensitivity; problems in establishing and maintaining appropriate levels of trust in relationships; frequent misinterpretation of social signals

I'm fascinated by the number of adjectives in here. I simplified very slightly, as I did with the DSM criteria, but I had to keep all these adjectives. Anyway, I won't elaborate for too many more paragraphs, so let's say sentimonster moment and leave it at that. I will spare you my mirrored Félix essay. For now.

Qualifications and Limitations

First of all, Adrien is a teenager. The distinguishing factor between a personality disorder and regular adolescent difficulties would be longevity and identity concerns beyond his developmental phase (APA, 2022, p. 758). Second of all, Adrien has a uniquely terrible home life and magical problems. Some of his behaviors could be normal considering his experiences and sociocultural context, and this matters when it comes to psychological evaluation. Take everything with a grain of salt!

More generally, the categorical model of the DSM has several issues, not to mention diagnostic issues related to culture, gender, and stigma. Some but not all of these issues are addressed by the dimensional model it includes in a later section, which shares theoretical foundations with the model of personality disorders in the ICD. Even so, issues remain. Diagnosis, access to treatment, and political statements are intrinsically linked in complex ways. I won't get into all of the nuances, but be safe, remember this is a fictional character, and keep an open mind.

REFERENCES:

American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). https://doi.org/10.1176/appi.books.9780890425787

Bach, B., Kramer, U., Doering, S., di Giacomo, E., Hutsebaut, J., Kaera, A., De Panfilis, C., Schmahl, C., Swales, M., Taubner, S., & Renneberg, B. (2022). The ICD-11 classification of personality disorders: A European perspective on challenges and opportunities. Borderline Personality Disorder and Emotion Dysregulation, 9(1). https://doi.org/10.1186/s40479-022-00182-0

Dozois, D. J. A. (2019). Abnormal psychology: Perspectives (6th ed.). Pearson.

Mind. (2022, September). What does BPD feel like? https://www.mind.org.uk/information-support/types-of-mental-health-problems/borderline-personality-disorder-bpd/experiences-of-bpd/

National Health Service. (2022, November 4). Causes - Borderline personality disorder. https://www.nhs.uk/mental-health/conditions/borderline-personality-disorder/causes/

Swales, M. A. (2022). Personality disorder diagnoses in ICD-11: Transforming conceptualisations and practice. Clinical Psychology in Europe, 4(Special Issue). https://doi.org/10.32872/cpe.9635

World Health Organization. (2023). International statistical classification of diseases and related health problems (11th ed.). https://icd.who.int/

[gravitasmalfunction]

Forgot to add “plot merman” to the xianxia checklist

[snickerdoodlles]

people need to stop assigning genAI more credit than it deserves 😭 it's spicy autofill. it cannot do lateral thinking for shit nor give you more than what you put in. it is not anymore dangerous than students using cheat strategies that already exist, like chegg or copying a friend's homework. which aren't great either! you don't want anyone cheating, especially not in those fields! but chatgpt and other genAI is not helping students cheat more effectively than they already did, it very literally cannot hold or understand these concepts nor mimic it well enough to pass exams.

also, while I can't speak to which measures other professions use, part of engineering's safety check systems involve multiple sign-offs from licensed engineers, which is a certification program that requires minimum a bachelor's degree, four years of working under a professional engineer (an already licensed engineer, who's also required to continually maintain said license), two rigorous exams, and earn a license from a state licensure board. people know fuck ups in engineering can lead to a lot of destruction, injury, and death, and they've reacted accordingly to implement new safety measures to try to prevent new disasters from occuring, which also include measures to keep people who take shortcuts or cheat egregiously out of sign-off positions. genAI can't help people skirt around those.

I agree that cheating is bad and we absolutely want to discourage it for all students, but LLMs (and genAI overall) are just not that good at doing anything other than chatting or acting as a scribe; it's not going to suddenly compromise the safety systems we have in place right now.

ur future nurse is using chapgpt to glide thru school u better take care of urself

[widedevsolution1]

The AIoT Revolution: How AI and IoT Convergence is Rewriting the Rules of Industry & Life

Imagine a world where factory machines predict their own breakdowns before they happen. Where city streets dynamically adjust traffic flow in real-time, slashing commute times. Where your morning coffee brews automatically as your smartwatch detects you waking. This isn’t science fiction—it’s the explosive reality of Artificial Intelligence of Things (AIoT), the merger of AI algorithms and IoT ecosystems. At widedevsolution.com, we engineer these intelligent futures daily.

Why AIoT Isn’t Just Buzzword Bingo: The Core Convergence

Artificial Intelligence of Things fuses the sensory nervous system of IoT devices (sensors, actuators, smart gadgets) with the cognitive brainpower of machine learning models and deep neural networks. Unlike traditional IoT—which drowns in raw data—AIoT delivers actionable intelligence.

As Sundar Pichai, CEO of Google, asserts:

“We are moving from a mobile-first to an AI-first world. The ability to apply AI and machine learning to massive datasets from connected devices is unlocking unprecedented solutions.”

The AIoT Trinity: Trends Reshaping Reality

1. Predictive Maintenance: The Death of Downtime Gone are days of scheduled check-ups. AI-driven predictive maintenance analyzes sensor data intelligence—vibrations, temperature, sound patterns—to forecast failures weeks in advance.

Real-world impact: Siemens reduced turbine failures by 30% using AI anomaly detection on industrial IoT applications.

Financial upside: McKinsey estimates predictive maintenance cuts costs by 20% and downtime by 50%.

2. Smart Cities: Urban Landscapes with a Brain Smart city solutions leverage edge computing and real-time analytics to optimize resources. Barcelona’s AIoT-powered streetlights cut energy use by 30%. Singapore uses AI traffic prediction to reduce congestion by 15%.

Core Tech Stack:

Distributed sensor networks monitoring air/water quality

Computer vision systems for public safety

AI-powered energy grids balancing supply/demand

3. Hyper-Personalized Experiences: The End of One-Size-Fits-All Personalized user experiences now anticipate needs. Think:

Retail: Nike’s IoT-enabled stores suggest shoes based on past purchases and gait analysis.

Healthcare: Remote patient monitoring with wearable IoT detects arrhythmias before symptoms appear.

Sectoral Shockwaves: Where AIoT is Moving the Needle

🏥 Healthcare: From Treatment to Prevention Healthcare IoT enables continuous monitoring. AI-driven diagnostics analyze data from pacemakers, glucose monitors, and smart inhalers. Results?

45% fewer hospital readmissions (Mayo Clinic study)

Early detection of sepsis 6+ hours faster (Johns Hopkins AIoT model)

🌾 Agriculture: Precision Farming at Scale Precision agriculture uses soil moisture sensors, drone imagery, and ML yield prediction to boost output sustainably.

Case Study: John Deere’s AIoT tractors reduced water usage by 40% while increasing crop yields by 15% via real-time field analytics.

🏭 Manufacturing: The Zero-Waste Factory Manufacturing efficiency soars with AI-powered quality control and autonomous supply chains.

Data Point: Bosch’s AIoT factories achieve 99.9985% quality compliance and 25% faster production cycles through automated defect detection.

Navigating the Minefield: Challenges in Scaling AIoT

Even pioneers face hurdles:ChallengeSolutionData security in IoTEnd-to-end encryption + zero-trust architectureSystem interoperabilityAPI-first integration frameworksAI model driftContinuous MLOps monitoringEnergy constraintsTinyML algorithms for low-power devices

As Microsoft CEO Satya Nadella warns:

“Trust is the currency of the AIoT era. Without robust security and ethical governance, even the most brilliant systems will fail.”

How widedevsolution.com Engineers Tomorrow’s AIoT

At widedevsolution.com, we build scalable IoT systems that turn data deluge into profit. Our recent projects include:

A predictive maintenance platform for wind farms, cutting turbine repair costs by $2M/year.

An AI retail personalization engine boosting client sales conversions by 34%.

Smart city infrastructure reducing municipal energy waste by 28%.

We specialize in overcoming edge computing bottlenecks and designing cyber-physical systems with military-grade data security in IoT.

The Road Ahead: Your AIoT Action Plan

The AIoT market will hit $1.2T by 2030 (Statista). To lead, not follow:

Start small: Pilot sensor-driven process optimization in one workflow.

Prioritize security: Implement hardware-level encryption from day one.

Democratize data: Use low-code AI platforms to empower non-technical teams.

The Final Byte We stand at an inflection point. Artificial Intelligence of Things isn’t merely connecting devices—it’s weaving an intelligent fabric across our physical reality. From farms that whisper their needs to algorithms, to factories that self-heal, to cities that breathe efficiently, AIoT transforms data into wisdom.

The question isn’t if this revolution will impact your organization—it’s when. Companies leveraging AIoT integration today aren’t just future-proofing; they’re rewriting industry rulebooks. At widedevsolution.com, we turn convergence into competitive advantage. The machines are learning. The sensors are watching. The future is responding.

“The greatest achievement of AIoT won’t be smarter gadgets—it’ll be fundamentally reimagining how humanity solves its hardest problems.” — widedevsolution.com AI Lab

[intelisync]

Overcoming the 60% Struggle with ML Adoption: Key Insights

In the race to stay competitive, companies are turning to machine learning (ML) to unlock new levels of efficiency and innovation. But what does it take to successfully adopt ML?

Machine learning (ML) is a transformative technology offering personalized customer experiences, predictive analytics, operational efficiency, fraud detection, and enhanced decision-making. Despite its potential, many companies struggle with ML adoption due to data quality challenges, a lack of skilled talent, high costs, and resistance to change.

Effective ML implementation requires robust data management practices, investment in training, and a culture that embraces innovation. Intelisync provides comprehensive ML services, including strategy development, model building, deployment, and integration, helping companies overcome these hurdles and leverage ML for success.

Overcoming data quality and availability challenges is crucial for building effective ML models. Implementing robust data management practices, including data cleaning and governance, ensures consistency and accuracy, leading to reliable ML models and better decision-making. Addressing the talent gap through training programs and partnerships with experts like Intelisync can accelerate ML project implementation. Intelisync’s end-to-end ML solutions help businesses navigate the complexities of ML adoption, ensuring seamless integration with existing systems and maximizing efficiency. Fostering a culture of innovation and providing clear communication and leadership support are vital to overcoming resistance and promoting successful ML adoption.

Successful ML adoption involves careful planning, strategic execution, and continuous improvement. Companies must perform detailed cost-benefit analyses, start with manageable pilot projects, and regularly review and optimize their AI processes. Leadership support and clear communication are crucial to fostering a culture that values technological advancement. With Intelisync’s expert guidance, businesses can bridge the talent gap, ensure smooth integration, and unlock the full potential of machine learning for their growth and success. Transform your business with Intelisync’s comprehensive ML services and stay ahead in the competitive Learn more....

[nithinsys9786]

Our AI and ML solutions are crafted to enhance efficiency, reduce costs, and drive growth. With numerous successful projects for industry leaders and a 100% customer satisfaction rate, we’re your trusted partner in innovation.

Ready to take your business to the next level? Contact us today to discover how our advanced AI and ML solutions can revolutionize your operations!

Visit: https://www.synclovis.com/services/ai-ml-services/

[pulsaris]

Apple M4

A Apple anunciou nesta semana passada o lançamento do novo processador Apple M4. O M4 é produzido através de um processo de fabrico de 3 nanómetros (de segunda geração) e possui um nível de eficiência sem precedentes. Aliado a uma capacidade de processamento avançada, o novo processador equipa os novos iPad Pro e oferece novas possibilidades de utilização com as capacidades de Machine Learning, Ray-Tracing, aceleração do codec AV1 por hardware, de entre muitas outras capacidades.

Saiba tudo no comunicado oficial da Apple localizado em: https://www.apple.com/newsroom/2024/05/apple-introduces-m4-chip/

______ Direitos de imagem: © Apple (via https://www.apple.com/newsroom/)

[rubylogan15]

Explore the dynamic fusion of DevOps & Machine Learning, unraveling how ML redefines operational paradigms. Dive into the future of tech efficiency

[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.

[trendingnow3-blog]

A Game-Changer in the Kitchen!

I recently purchased the Rico Japanese Technology Rechargeable Wireless Electric Chopper, and it has completely transformed my cooking experience. This little kitchen gadget has exceeded my expectations in every way, and I couldn’t be happier with my purchase. First and foremost, the wireless design of this chopper is a game-changer. No more dealing with annoying cords or searching for an…

[thanatos-girl]

Not to sound too anarchic for my hot ML mutuals but the state has a vested interest in making their ability to notice and stop crime look incredibly efficient despite the fact that it isn't and that law enforcement in general is wildly incompetent. Every time you believe their propaganda about this you make it harder and harder to break out of the cop-sponsored panopticon you've constructed in your head.

Just remember that they cannot and do not observe everyone simultaneously and that they're relying more on you being too scared to do crime than they are on any sort of law enforcement agency

[heartmaddie]

SAKUSA KIYOOMI doesn't know what he's doing here.

standing outside your door, tightly gripping onto the flimsy plastic of a convenience store bag which was filled with various medicines and groceries for him to cook. to him, this felt a bit too intimate. showing up to your home unannounced because your classmate told him you were out sick and he knew that your parents weren’t home, so who would look after you if it wasn’t him? 

realistically, he could turn around right now, you didn’t know he was here yet. but there was something unfamiliar which pulled him towards your front door. an unsettling sensation bloomed deep in his chest when he’d imagine you sick, all alone on your bed. 

before he knew it, his legs were pulling him towards your door, he sighs as he presses his finger against your doorbell, hoping that you weren’t sleeping. a couple moments later, the door shifts open and he sees you, bundled in his dark sweatshirt and a pair of large trousers, and his expression softens slightly.

“oh, yn” he mumbles, gently rubbing your cheek, “you look so unwell.”

“why are you here kiyoomi?” you ask, obviously congested as you looked up at him confused. you knew about his aversion to disease, and how he’d go out of his way to ensure that he wouldn’t get sick, so you weren’t exactly sure on why he was here, holding you gently like this.

kiyoomi shrugs nonchalantly and pushes past you, closing the door as he slips off his shoes and neatly places them near the door. 

“i just felt like it, why aren’t you in bed?” he questions, unpacking all the medicines and aligning them in a neat row before moving to wash the vegetables and fruits, looking over at you occasionally.

“i had a really horrible headache, and i couldn’t get to sleep.” you explained, moving to lean your head on his back, gently brushing your fingers against his arm. kiyoomi smiles to himself, looking back at you with a soft expression.

“tired?” he asks gently, curling his fingers against your soft hair. you nod in response, letting out a yawn as you rubbed your face against his back.

“so exhausted, i hate being sick like this,” you complained. he thought you were cute, with your nose red and dry as you looked up at him expectantly, “what are you cooking?”

“okayu.” he states simply, washing the rice efficiently, “you should go lie down on the couch, have you had any medicine today?” he asks pointedly, letting out a deep sigh when you shake your head no. “you should be taking better care of yourself, there’s pei ko on the island bench.”

“i know, but i’ve just been so bedridden today.” you slip away from him, and kiyoomi can't help but miss the comfort of your warmth. he watches as you pour 10 ml of the thick medicine into a plastic cup, before downing the medicine, it’s sickeningly sweet on your tongue, and he smiles when he sees you scrunch your face up. 

“go lie down on the couch while i make this for you.” he sighs, gently pulling you in for a soft kiss before walking you to the sofa, he guides you onto lying onto the couch, pushing your hair behind your ears as he adjusts the pillow behind you. 

you look up at him as he works, letting him pamper you as you feel the weight of your sickness settled in your bones. sakusa kiyoomi was infamous for being a perfectionist, but he let that go when it was just you. he’d gently lay blankets over your body, pressing kisses against your cough ridden lips between each layer.

“okay, just try to sleep while i cook for you, and i’ll wake you up when it’s done. okay?” he asks, his voice uncharacteristically soft as he strokes your hair, lulling you to sleep.

he promptly moves back to the kitchen, continuing to swiftly cut shallots and occasionally check on the simmering pot as the sea-like smell of dashi powder wafts through the air of your home. sakusa would have a small smile on his face when he heard you sleeping soundly, this was oddly domestic, and he somehow didn’t mind the predicament. 

a half hour later you feel kiyoomi gently nudging you awake, the tray of okayu and a variety of different side dishes on a tray which is laid on the coffee table.

“is it ready?” you asked tiredly, rubbing your eyes as kiyoomi nodded.

“yeah, here.” he passes you a box of tissues and rubs your shoulder when you blow your nose, “ready to eat?” he asks curiously, helping you sit up.

“yeah, thank you. it looks really nice, baby.” you smile up at him, bringing the metal spoon to your mouth. the porridge was warm on your tongue, and had a satisfying light taste. he was good at this.

you sat in silence, leaning against his shoulder as the only sounds which surrounded you was the metal scraping against the wooden bowl. kiyoomi was glad that you seemed to be enjoying the meal he prepared, he would bury his nose into your hair as he held you close, circling his fingers against your waist.

he watches as you set the cutlery on the finished tray and he hums softly.

“did you enjoy it?” he asks curiously, “made your throat feel a bit better?”

“yeah, it was really soothing, thank you omi.” you reply, curling against his chest.

“okay, let’s get you to bed then.” he’d scoop you up into his arms, walking you towards your warm bedroom and tucking you in tight, “i’ll come join you in a bit, i’m gonna clean up first.” but before he could leave you clutched onto his arm tight.

“stay, just for a bit.” you ask, looking up at him with tired eyes, “just until i fall asleep.” 

who was he to say no.

sakusa nods and slips into the bed next to you, pulling you against his chest and wrapping the blankets around you securely. he lets out a deep sigh of relief, feeling his muscles relax in your comforting presence, even if he can already feel his throat tightening with the same sickness as yours. 

“feeling better?” he asks, twirling your hair against your finger as he feels your head shift up and downwards against his chest.

“yeah, a lot better since you got here.” you admit, “it was miserable being home all alone today.” making kiyoomi nod in response.

“sounds boring, i’m glad i came since you weren’t eating your proper nutrients, hm?” he scolds, smiling against your scalp, “it’s how you’re supposed to get better, silly girl,” you nod lazily in response.

“i know, but i was just so tired.” you whine,

“then rest, and be quiet now.” he shushes you, pressing a final kiss to your lips before cradling your head in his arms, signalling that he wanted to sleep too.

sakusa kiyoomi would gently pepper kisses against your temple as he felt your slow breaths rise up and down against him, he’d spend hours memorising the pattern, occasionally matching his breaths to yours. he’d shift his hold on you when you moved around in your sleep, sometimes taking the subconscious initiate to curl against him, wrapping your arms tightly as you’d nuzzle your soft head against him.

kiyoomi didn’t think he was built for this, the domesticity of somebody else in his arms. he was scared of course, it was so him to be afraid of contracting your disease, but he didn’t necessarily think about it this time. you were his priority now, so he was okay with the high chance of having the flu than watch you suffer in your lonesome. maybe the way you’d fit perfectly in his arms every time would make him rethink his innate desires, because kiyoomi had a very individualistic mindset, his whole future was planned out since the ripe age of 11, but maybe he’d want to welcome you into that too. 

! extra

a couple weeks later, you look over to your boyfriend and hear a newly familiar sound,

“a-achoo!” he sneezes, cringing as he wipes his nose, “it’s all over for me” he sighs dramatically, leaning against your shoulder as he seeks your warmth, letting you spoon feed him bits of okayu with poached chicken.

©heartmaddie all rights reserved. please do not repost my work.

[canmom]

you read ML research (e.g. arxiv, state of ai, various summaries), you find an overwhelming blizzard of new techniques, clever new applications and combinations of existing techniques, new benchmarks to refine this or that limitation, relentless jumps in capabilities that seem unstoppable (e.g. AI video generation took off way faster than I ever anticipated). at some point you start to see how Károly Zsolnai-Fehér became such a parody of himself!

you read ed zitron & similar writers and you hear about an incomprehensibly unprofitable industry, an obscene last-gasp con from a cancerous, self-cannibalising tech sector that seems poised to take the rest of the system down with it once the investors realise nobody actually cares to pay for AI anything like what it costs to run. and you think, while perhaps he presents the most negative possible read on what the models are capable of, it's hard to disagree with his analysis of the economics.

you read lesswrong & cousins, and everyone's talking about shoggoths wearing masks and the proper interpretation of next-token-prediction as they probe the LLMs for deceptive behaviour with an atmosphere of paranoid but fascinated fervour. or else compile poetic writing with a mystic air as they celebrate a new form of linguistic life. and sooner or later someone will casually say something really offputting about eugenics. they have fiercely latched onto playing with the new AI models, and some users seem to have better models than most of how they do what they do. but their whole deal from day 1 was conjuring wild fantasies about AI gods taking over the world (written in Java of course) and telling you how rational they are for worrying about this. so... y'know.

you talk to an actual LLM and it produces a surprisingly sharp, playful and erudite conversation about philosophy of mind and an equally surprising ability to carry out specific programming tasks and pull up deep cuts, but you have to be constantly on guard against the inherent tendency to bullshit, to keep in mind what the LLM can't do and learn how to elicit the type of response you want and clean up its output. is it worth the trouble? what costs should be borne to see such a brilliant toy, an art piece that mirrors a slice of the human mind?

you think about the news from a few months ago where israel claimed to be using an AI model to select palestinians in gaza to kill with missiles and drones. an obscene form of statswashing, but they'd probably kill about the same number of people, equally at random, regardless. probably more of that to come. the joke of all the 'constitutional AI', 'helpful harmless assistant' stuff is that the same techniques would work equally well to make the model be anything you want. that twat elon musk already made a racist LLM.

one day the present AI summer and corresponding panics will burn out, and all this noise will cohere into a clear picture of what these new ML techniques are actually good for and what they aren't. we'll have a pile of trained models, probably some work on making them smaller and more efficient to run, and our culture will have absorbed their existence and figured out a suitable set of narratives and habits around using them in this or that context. but i'm damned if I know how it will look by then, and what we'll be left with after the bubble.

if i'm gonna spend all this time reading shit on my computer i should get back to umineko lmao