#applying machine learning

[vbeyonddigital23]

Applying machine learning to process automation

Implementing machine learning in process automation can provide various advantages to businesses. Here, we look at its use cases and benefits for enterprises.

The practice of applying Machine Learning (ML) models to real-world situations through automation is known as Automated Machine Learning. It automates the selection, construction, and parameterization of machine learning models in particular. Since then, Artificial Intelligence (AI) and its subsets have become clever additions to the field of automation, attempting to tackle problems that go beyond simply taking over repetitive tasks.

Machine learning is one form of applied AI that is now being used in Business Process Automation (BPA) and Robotic Process Automation (RPA). Machine learning is the use of artificial intelligence to enable systems to learn and make decisions without being specifically programmed to do so. In RPA, ML can move robots beyond repetitive process execution and enable them to perform jobs that previously needed human decision-making. Artificial Intelligence skills may also be used to improve data integrity, give structure to unstructured and semi-structured data sources, increase business insights, and improve automated execution.

In this blog, we’re taking a closer look at the role of machine learning in automation.

Machine Learning in Process Automation

The applications of machine learning are expanding rapidly to help organizations and society solve real-world issues. ML works on the basis of encapsulating a vast quantity of data (or knowledge) into some form of a mathematical model. The model may be used to solve issues by using the knowledge it contains.

Machine learning can be applied to a wide range of problems where large amounts of historical data can be used to predict or make decisions in specific areas. It should be highlighted that, unlike constructing an algorithm, it is based on the creation of a knowledge base. However, the ML technique is particularly efficient in dealing with such problems.

Process automation has evolved to provide solutions to assist, simplify, and expedite processes. It all started with robotic process automation based on automating step-by-step workflows in a single platform or application. RPA has transformed the way people work today, with millions of bots working alongside humans.

The majority of RPA implementations make use of software bots that automate operations based on pre-defined or established criteria. As corporate processes became more sophisticated, artificial intelligence technology such as machine learning began to impact how bots might accomplish more. With the inclusion of ML, bots continually learn from human activities, emulating human cognition to a great extent. The more training and learning supplied, the closer machines can get to reading, visualizing, learning, and thinking like humans.

How to apply machine learning to process automation

The simplicity with which a workflow or process can be automated is RPA’s biggest strength. However, it has a restriction in that it cannot be automated if it requires decision-making supported by knowledge application. This is where machine learning comes in; it assists in the creation of a knowledge base based on past data, which is then used for decision-making and prediction.

Integrating Machine Learning with robotic process automation can result in strong automation solutions. Many RPA software suppliers are striving to include this functionality inside the tool itself in order to broaden its capabilities. However, because ML is a new subject, it needs the expertise of engineers with extensive knowledge and abilities in order to construct correct ML models.

Improving the Execution of Automation

Algorithms for machine learning can also be used to improve the delivery of automated services. Algorithms can be used in computer vision, for example, to teach robots how to detect and interact with onscreen fields and components. Machine learning models are also employed for error management, and recursion is frequently used to minimize code complexity and optimize robot runtime.

Another growing use of machine learning in automation is task mining. In this case, robots are taught to assess daily task information obtained from employees in order to create process maps and recommend procedures for automation based on the maximum return on investment (ROI.) This application can be a mixed bag since it needs extensive training to reach the proper balance between ROI, degree of effort, and overall suitability for automation.

Attended Automation

Attended automation, also known as Remote Desktop Automation (RDA), is when robots work alongside people to augment their job or help them make better decisions. Machine learning may be used to absorb data from several sources in real-time, allowing robots to assist humans in determining the optimal next step in their workflow.

Machine learning may also be integrated with other cognitive skills, such as Natural Language Processing (NLP), to allow robots to emulate the easier decision-making inside a human’s workflow, bringing us even closer to end-to-end automation.

Conclusion

From the above breakdown of process automation technologies, we can surmise that investing in machine learning to augment BPA and RPA can provide various advantages to businesses. It is a powerful and continuously evolving technology, and its ramifications will only grow in the future years. As a result, it is a worthwhile improvement over manual processes and a crucial step towards helping your teams become more productive. Machine learning algorithms can help to reduce risk and eliminate the cost of operating clunky, outdated software.

Reimagine business efficiency with automation and data solutions built by VBeyond Digital.

Get in touch to speak with our experts.

[2-dsimp]

Question: Why is Yuujin so

Kind-hearted

Compassionate

Empathetic

Generous

Thoughtful

Considerate

Supportive

Encouraging

Loyal

Trustworthy

Reliable

Honest

Genuine

Authentic

Caring

Loving

Friendly

Warm

Welcoming

Optimistic

Cheerful

Positive

Enthusiastic

Passionate

Ambitious

Determined

Hardworking

Perseverant

Resilient

Courageous

Brave

Confident

Self-assured

Creative

Imaginative

Innovative

Curious

Open-minded

Flexible

Adaptable

Patient

Understanding

Tolerant

Forgiving

Humble

Modest

Grateful

Appreciative

Inspiring

Empowering

51. Wise

52. Knowledgeable

53. Intelligent

54. Sharp-witted

55. Quick-thinking

56. Analytical

57. Logical

58. Rational

59. Pragmatic

60. Resourceful

61. Versatile

62. Innovative

63. Visionary

64. Strategic

65. Organized

66. Detail-oriented

67. Efficient

68. Productive

69. Ambitious

70. Goal-oriented

71. Driven

72. Motivated

73. Dedicated

74. Tenacious

75. Focused

76. Disciplined

77. Patient

78. Calm

79. Tranquil

80. Composed

81. Balanced

82. Grounded

83. Centered

84. Resilient

85. Strong

86. Tough

87. Courageous

88. Fearless

89. Bold

90. Adventurous

91. Explorer

92. Creative

93. Artistic

94. Expressive

95. Imaginative

96. Curious

97. Inquisitive

98. Open-minded

99. Accepting

100. Non-judgmental

101. Tolerant

102. Compassionate

103. Empathetic

104. Kind-hearted

105. Loving

106. Affectionate

107. Friendly

108. Sociable

109. Charismatic

110. Charming

111. Engaging

112. Enthusiastic

113. Energetic

114. Vibrant

115. Optimistic

116. Positive

117. Inspiring

118. Motivating

119. Supportive

120. Encouraging

121. Nurturing

122. Considerate

123. Thoughtful

124. Generous

125. Selfless

126. Altruistic

127. Grateful

128. Appreciative

129. Humble

130. Modest

131. Sincere

132. Honest

133. Trustworthy

134. Dependable

135. Reliable

136. Responsible

137. Ethical

138. Principled

139. Integrity

140. Authentic

141. Genuine

142. Transparent

143. Fun-loving

144. Playful

145. Spontaneous

146. Adventurous

147. Free-spirited

148. Whimsical

149. Witty

150. Humorous

+ bonus. Iconic?

[thursdayisbetterthanfriday]

Applied AI - Integrating AI With a Roomba

AKA. What have I been doing for the past month and a half

Everyone loves Roombas. Cats. People. Cat-people. There have been a number of Roomba hacks posted online over the years, but an often overlooked point is how very easy it is to use Roombas for cheap applied robotics projects.

Continuing on from a project done for academic purposes, today's showcase is a work in progress for a real-world application of Speech-to-text, actionable, transformer based AI models. MARVINA (Multimodal Artificial Robotics Verification Intelligence Network Application) is being applied, in this case, to this Roomba, modified with a Raspberry Pi 3B, a 1080p camera, and a combined mic and speaker system.

The hardware specifics have been a fun challenge over the past couple of months, especially relating to the construction of the 3D mounts for the camera and audio input/output system.

Roomba models are particularly well suited to tinkering - the serial connector allows the interface of external hardware - with iRobot (the provider company) having a full manual for commands that can be sent to the Roomba itself. It can even play entire songs! (Highly recommend)

Scope:

Current:

The aim of this project is to, initially, replicate the verbal command system which powers the current virtual environment based system.

This has been achieved with the custom MARVINA AI system, which is interfaced with both the Pocket Sphinx Speech-To-Text (SpeechRecognition · PyPI) and Piper-TTS Text-To-Speech (GitHub - rhasspy/piper: A fast, local neural text to speech system) AI systems. This gives the AI the ability to do one of 8 commands, give verbal output, and use a limited-training version of the emotional-empathy system.

This has mostly been achieved. Now that I know it's functional I can now justify spending money on a better microphone/speaker system so I don't have to shout at the poor thing!

The latency time for the Raspberry PI 3B for each output is a very spritely 75ms! This allows for plenty of time between the current AI input "framerate" of 500ms.

Future - Software:

Subsequent testing will imbue the Roomba with a greater sense of abstracted "emotion" - the AI having a ground set of emotional state variables which decide how it, and the interacting person, are "feeling" at any given point in time.

This, ideally, is to give the AI system a sense of motivation. The AI is essentially being given separate drives for social connection, curiosity and other emotional states. The programming will be designed to optimise for those, while the emotional model will regulate this on a seperate, biologically based, system of under and over stimulation.

In other words, a motivational system that incentivises only up to a point.

The current system does have a system implemented, but this only has very limited testing data. One of the key parts of this project's success will be to generatively create a training data set which will allow for high-quality interactions.

The future of MARVINA-R will be relating to expanding the abstracted equivalent of "Theory-of-Mind". - In other words, having MARVINA-R "imagine" a future which could exist in order to consider it's choices, and what actions it wishes to take.

This system is based, in part, upon the Dyna-lang model created by Lin et al. 2023 at UC Berkley ([2308.01399] Learning to Model the World with Language (arxiv.org)) but with a key difference - MARVINA-R will be running with two neural networks - one based on short-term memory and the second based on long-term memory. Decisions will be made based on which is most appropriate, and on how similar the current input data is to the generated world-model of each model.

Once at rest, MARVINA-R will effectively "sleep", essentially keeping the most important memories, and consolidating them into the long-term network if they lead to better outcomes.

This will allow the system to be tailored beyond its current limitations - where it can be designed to be motivated by multiple emotional "pulls" for its attention.

This does, however, also increase the number of AI outputs required per action (by a magnitude of about 10 to 100) so this will need to be carefully considered in terms of the software and hardware requirements.

Results So Far:

Here is the current prototyping setup for MARVINA-R. As of a couple of weeks ago, I was able to run the entire RaspberryPi and applied hardware setup and successfully interface with the robot with the components disconnected.

I'll upload a video of the final stage of initial testing in the near future - it's great fun!

The main issues really do come down to hardware limitations. The microphone is a cheap ~$6 thing from Amazon and requires you to shout at the poor robot to get it to do anything! The second limitation currently comes from outputting the text-to-speech, which does have a time lag from speaking to output of around 4 seconds. Not terrible, but also can be improved.

To my mind, the proof of concept has been created - this is possible. Now I can justify further time, and investment, for better parts and for more software engineering!

[fite-club]

this is the discourse sideblog so i'm just going to say it: i am not anti-ai-art. or anti-ai at all in general. i am pro-artist and anti-capitalist, but not anti-ai-art. if you are someone who argued against NFTs because you understand how idiotic and short-sighted it is to try and copyright images, then you can't also argue that we need to copyright images so they can't be used for ai. if what you're saying about ai art can also apply to photography ("it's not work, you just push a button on a machine! illustrators will be fired!") then think for a second if it's the tool you have an issue with or the system we're operating under where artists need jobs to survive

[bisexualbaker]

I've been thinking about this initial post for a while, and you make a great point. It finally made me sit down and try to figure out an important question:

What's true about various corporations and startups scraping AO3 that is not also true about, for example, Janelle Shane of "You Look Like a Thing and I Love You" and aiweirdness.com ?

At least one of Shane's AIs is trained on fanfiction, I can't imagine she got permission from every writer whose work she trained on to use it, and she demonstrably makes money from it. Why am I not angry at her in the way that I'm angry at those assholes?

For me, I think it comes down to the fact that Shane is not suggesting that AI should replace the majority of the creative workforce, or even that it can. She's not looking to make a quick buck or to avoid paying artists for their labor. She's learning and educating about AI and its limitations, and not pretending that it's ready to take over a lot of human jobs, especially when so many people are struggling to make ends meet already.

I'm okay with people like her using my work. For the people who do want to do the things I referenced, I'd just as soon not make things easy for them, even if there is nothing technically wrong with them doing it.

I don't care about data scraping from ao3 (or tbh from anywhere) because it's fair use to take preexisting works and transform them (including by using them to train an LLM), which is the entire legal basis of how the OTW functions.

[l-in-c-future]

Book Reading: Eastern Perspectives Humanistic AI-2b The contest between AI and natural intelligence

The questions about whether human beings are just sophisticated machines and therefore CAN BE and SHOULD BE fully displaced by AI prompt philosophers and technocracts to debate fundamental natures of human beings.

The pursuance of answers to these BIG QUESTIONS are within the studies of philosophers and theologians.

The philosophies of Aristotle's Doctrine of Mean; Immaunnel Kant; the theologies of Augustine were quotated by Jeu-Jeng Yuann to support his arguments for human minds has positive position for development of AI to preserve humanity and human dignity.

The Doctrine of Mean as balance to two extreme views

Technocrats' view represented by Elon Musk

Musk is not the only big technograt who hold such view, other iconic people are Zuckerberg's metaverse. They are gigiantic in development of AI technologies to support metaverses because they believe human beings can plausibly live in parallel universes (in the real physical world and the virtual metaverses). Eventually, human beings may not need a physical world to distinguish reality and virtual reality as EVERY HUMAN NEED can be satisfied by metaverse.

On the other hand, philosophers disagree with this on the basis that this kind of possibility won't be successful. Otherwise the FUNDAMENTAL MEANINGS for the existence human beings will cease. It will be the end of human dignity.

Yuann considered that those who believe metaverse WILL CERTAINLY succeed by displacement of human beings by AI and their needs in the physical world are too idealistic while those who insist that metaverse WILL CERTAINLY not successful are too assertively firm.

The extreme views of either side are bound to be flawed. Since philosophers generally believe in Aristotle's Doctrine of Mean, Yuann's personal view was that a mid-way stance was more reasonable.

What criterion must be met for full replacment of human beings by machines?

Based on the Doctrine of Mean, Yuann deduced 3 assumptions/criterion/conditions to exist simultaneously in order for full replacement of humans' natural intelligence by AI:

In whatever things human beings pursue, we ONLY aim at meeting our extrinsic needs and desires WITHOUT catering the intrinsic aspects of human desires. (i.e. the more complicated and advanced needs in Maslow's pyramid of needs.)

Achieving the ends are the ONLY reason for EVERYTHING humans conduct. (i.e. Not only the means to the ends are irrelevant, nothing else should be considered outside the ends. Everything becomes instrumentally based on utilisation values and capabilities. When machines and robots can FUNCTION better to achieve the ends than human beings, there will be NO MORE JUSTIFICATION for human's existence in those areas. In this sense, human beings' existence values are ALL AND ONLY based on functional contributions.) Assumption 2 can be seen as derivation from assumption 1 out of the ignorance and denial of human's inner complicated needs. This will reduce ALL our realisation and meaning of existence to something that can be ultimately satisifed by machines ONLY.

Denial of human dignity. (i.e. since human beings are not admitted to posses dignity, whether humanity should be prioritised or even preserved is NOT part of the consideration.) Assumption 3 is the natural fruit of assumption 2. IF human beings' existence is ONLY about functional values for efficiency and effectiveness, we are seen as nothing more 'organic machines'. There is no need to regard dignity.

Obviously, these views won't be agreeable by those who BELIEVE that human beings are MUCH MORE than machines.

The views about human nature as living beings It led to why the Kant and Augustine's philosopy and theology came into place to DEFEND humanity. Religions, in this case, Christianity, point to higher DIVINE existence values and MEANINGS of human beings. (We were made LIKE GOD according to HIS IMAGE out of HIGHER divine wisdom FOR having intimate relationships with God as a DIVINE BEING. We were not made AS machines to serve human program codes).

Philosphers emphaise the importance human dignity. Kant's view was that humans do not just have extrinsic aspects, we have instrinc sides. Technological advancements can't just focus on serving the ends. It is also necessary to combine the means and the ends together. While it is important to achieve the objectives, it is equally important to decide how the means work to meet the ends, especially complying with human wills and respectful of the human choices.

Basically, philosphers acknowledge the cognitive abilities of human beings, and the psychological and spiritual activities of human beings all working together to make us superior intelligent BEINGS. The translations of heart and soul in philosophical terms comprise of souls, minds and the cognitive intelligence of human minds. These qualities and abilities made us NOBLE creatures who deserve dignity.

The recognitions of these BASIC HUMAN NATURES contrast other school of philosophy proposed by Pierre-Simon Laplace.

Can everthing deterministic and predictable accurately in the universe?

The French mathematician and physicist Pierre-Simon Laplace proposed that if we know the precise position and velocity of every particle in the universe at a given moment, we could use the laws of physics to predict with perfect accuracy the future behavior of the entire universe, including the thoughts and actions of every human being.

Essentially, Laplace’s demon is an all-knowing, all-powerful entity that could calculate the future state of the universe based on its complete knowledge of the present state. (i.e. everything in the universe is predictable and calculatable by some super calculators.)

Laplace's demon

Laplace’s demon is important in both philosophy and science for several reasons:

It challenges our understanding of determinism and free will through raising important questions about whether the universe is entirely deterministic, and if so, whether free will is an illusion. If the universe is deterministic and the future is set, then our choices and actions may be predetermined, and the idea of free will may be an illusion.

Another implication is that it may challenge our ideas about moral responsibility. If our choices and actions are predetermined by factors beyond our control, then it may be difficult to hold individuals responsible for their actions, as they may not have had a genuine choice in the matter.

The idea that an all-knowing entity could predict the future of the universe with perfect accuracy raises important questions about the limits of human knowledge and our ability to understand the universe.

Laplace assumes that all events are predetermined by prior causes, leaving no room for human agency or free will. However, humans have the ability to make choices that are not necessarily determined by prior causes.

Laplace's demon and AI

Laplace's demon is often cited in discussions about the potential of AI to predict human behavior with increasing accuracy, and the implications of this for privacy, ethics, and human autonomy.

Here are some key ways in which Laplace’s demon impacts AI research:

The limitations of machine learning: many machine learning algorithms are based on the assumption of determinism. However, these algorithms are limited by the same constraints of knowledge and the complexity of the systems being analyzed. This can lead to inaccuracies in machine learning models and the potential for unforeseen consequences.

The role of human agency: as AI systems become more sophisticated, there is a risk that human agency could be diminished or marginalized, leading to a loss of control over important decisions.

The importance of ethical considerations: the development of AI and machine learning raises important ethical questions on privacy, security, and social inequality.

The limits of prediction: Laplace’s demon assumes that it is possible to predict the future with perfect accuracy. However, as AI systems become more complex, it becomes increasingly difficult to predict their behavior. This raises questions about the limits of prediction in science and the potential for unforeseen consequences. Source of Laplace's Demon theory and its impacts: https://medium.com/@michellerichardson_11188/exploring-laplaces-demon-determinism-free-will-and-the-limits-of-human-knowledge-57c3f94ebbe0

The complexities surrounding science, technologies and the moral consequences and social impacts they posted about the future of hamanity will become constant important issues challenging the future of AI technologies.

The Future of AI from philosophical and moral perspectives

In the end of Yuann's article, he suggested 3 important principles:

The development and advancement of AI in any industrial and commercial society is not just about technological advancements, it creates deep inter-relating threats to human ethics and moral concerns.

The cognitive and comprehensive activities of human minds contribute to the progression of technologies because such advancements are WORKS OF HUMANS' NATURAL INTELLIGENCE. As Augustine suggested, the cognitive capability of human mind is the source of human dignity. Putting preference of AI over human mind is to put the cart before the horse. Thefore, we should always strive to defend the cognitive capabilities of human mind and make the best of which to guard and preserve human dignity.

The manifestation of the power of human mind and soul is NOT JUST about technological advancements, it is also to maintain the integrity of such advancements towards a human-centred future.

[mossy-covered-bones]

I keep sinking Hours And Hours into this internship search and im not even a tenth of the way through the amount another person i know had to send to get Two Whole Interviews

[orcelito]

Ok I got to actually looking at my classes for next semester. Reorganized things a little bit into clear categories. See the thing is, the 3 classes I thought I was gonna choose 2 from? None of them are being taught anymore lol. It's just that my enrollment term is so old, it's showing the old requirements. So I'm choosing 3 classes from the general list of classes, which makes me glad I picked out so many potential ones to choose from!!! So the 3 categories I've sorted these classes into are:

Quality assurance: "six sigma data quality" is the 1st choice, "quality engineering in IT" is 2nd choice

General theory: "policy, regulation, and globalization in IT" is 1st choice, "advanced systems development methodologies" is 2nd choice

Coding/computers: "applied machine learning" is 1st choice, "UNIX administration" is 2nd choice, "front end web coding" is 3rd choice

(Putting commentary under a cut bc it got a lil long)

'Cause how my school does scheduled now, we submit a list of requests & they compile them all and then figure out what classes work best for everyone. And u wanna include multiple options for each class slot in case the first doesn't work out for some reason. So that's why I have 7 choices, despite only needing 3.

As for the actual sections. I wanna make sure that my semester is as well-rounded as possible. I wanna get at least Some kind of coding in, to make sure I get some more practice before leaving. I have applied machine learning listed first for it bc I really know very little about how machine learning works, & with the way the IT field has gone, it's a little inevitable that I'll be working with it some. Best to learn about it now so I know what I'm doing later. But if I can't take that for some reason, I have the UNIX administration which would honestly kind of suck to take, but it'd be useful. And then front end web coding is less generally useful, but could still be a good skill to have.

The policy, regulation, & globalization in IT is one that I think would be very good for me to take. More of a theory class than a tech class, & it's focusing on learning about technology's effects on the world in economic, social, cultural, and ethical dynamics. I already have taken an ethics of IT sort of course so I've got some context for that, but I still think it'd be important to learn more about globalization. If for whatever reason it's not available tho, then another systems development class could be useful.

& then the data quality thing. Six sigma put at priority here bc it's smth ppl always talk about but I just don't know that much about it, outside of how it's supposed to help improve things. By taking this course, I'd also get a certificate in SS, which could only help in job searching I think. Then quality engineering in IT after that, which I don't know that much about but it seemed about in line with the goal of Data Quality stuff.

[susheelvisualpath]

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[captainjonnitkessler]

I think I can trace my intense hatred for the whole "regulations are just corporate bullshit, building codes are just The Man's way of keeping you down, we should return to pre-industrial barter and trade systems" nonsense back to when I first started doing electrical work at one of the largest hospitals in the country.

I have had to learn so much about all the special conditions in the National Electric Code for healthcare systems. All the systems that keep hospitals running, all the redundancies and backups that make sure one disaster or outage won't take out the hospital's life support, all the rules about different spaces within the hospital and the different standards that apply to each of them. And a lot of it is ridiculously over-engineered and overly redundant, but all of it is in the service of saving even one life from being lost to some wacky series of coincidences that could have been prevented with that redundancy.

I've done significantly less work in food production plants and the like, but I know they have similar standards to make sure the plants aren't going to explode or to make sure a careless maintenance tech isn't accidentally dropping screws into jars of baby food or whatever. And research labs have them to make sure some idiot doesn't leave a wrench inside a transformer and wreck a multi-million dollar machine when they try to switch it on.

Living in the self-sufficient commune is all fun and games until someone needs a kidney transplant and suddenly wants a clean, reliable hospital with doctors that are subject to some kind of overseeing body, is my point.

[mavensilicon]

How to Apply Machine Learning and Artificial Intelligence in VLSI Design and Verification?

The world of VLSI design and verification is evolving rapidly, driven by technological advancements that demand more efficient and reliable solutions. Machine Learning (ML) and Artificial Intelligence (AI) have emerged as powerful tools in this field, promising to revolutionize the way we approach VLSI design and verification. In this blog, we will explore the application of ML and AI in VLSI, highlighting their methods, potential, and future trends.

Introduction

VLSI design and verification entail creating complex integrated circuits and ensuring their functionality. ML and AI are becoming indispensable in these processes, offering innovative solutions to longstanding challenges.

Machine Learning and Artificial Intelligence Methods for VLSI Design

Layout Optimization:

ML and AI can optimize the layout of VLSI circuits by considering various design parameters. They can achieve a higher level of performance, power efficiency, and manufacturability.

EDA Tool Enhancement:

Electronic Design Automation (EDA) tools are essential in VLSI design. ML and AI can enhance these tools by improving algorithms for place-and-route, timing analysis, and even suggesting design rule checks.

Predictive Analytics:

AI can predict potential design flaws, reducing the likelihood of errors and costly redesigns. It can analyze historical data to anticipate issues in the early stages of design.

Automatic Floorplanning:

Floorplanning is a crucial step in VLSI design. AI algorithms can automate floorplanning by optimizing the placement of functional blocks to achieve better performance and power efficiency.

Machine Learning and Artificial Intelligence Methods for VLSI Verification

Automated Testbench Generation:

Creating a testbench for VLSI verification is a time-consuming task. ML and AI can automate the testbench generation process, increasing the efficiency of verification.

Anomaly Detection:

ML algorithms can identify anomalies in simulation results, helping to pinpoint design flaws or unexpected behavior. This is particularly useful for uncovering hard-to-detect bugs.

Coverage Analysis:

AI techniques can analyze coverage data to ensure comprehensive testing. They can identify untested scenarios and guide verification engineers in creating more robust testbenches.

Formal Verification:

Formal verification is a mathematically rigorous method for proving correctness. ML and AI can enhance formal methods by making them more scalable and efficient.

Future Trends and Opportunities of Machine Learning and Artificial Intelligence in VLSI Design and Verification

Customized Design:

AI can lead to highly customized designs optimized for specific applications. This opens opportunities in domains like IoT, automotive, and healthcare where specialized chips are in demand.

Real-time Verification:

ML can enable real-time verification during the design process. Engineers can receive immediate feedback on potential issues, reducing design iterations.

Reduced Time to Market:

With ML and AI accelerating design and verification processes, the time to market for VLSI products can be significantly reduced, giving companies a competitive edge.

Design Space Exploration:

AI can explore a broader design space, uncovering innovative solutions that might not be evident to human designers.

Safety and Security:

ML and AI can enhance VLSI security by identifying vulnerabilities and potential attacks, crucial in our interconnected world.

Conclusion

The integration of Machine Learning and Artificial Intelligence in VLSI design and verification is a promising development. These technologies hold the potential to optimize designs, accelerate the verification process, and enable the creation of highly specialized and efficient circuits. The future of VLSI design and verification is closely intertwined with the continued advancements in ML and AI.

If you are curious to know more about VLSI and wish to start a career in the VLSI industry then check out the Job-oriented VLSI courses from Maven Silicon VLSI training institute.

[wanderingandfound]

Ugh.

It's late, and I didn't use my sleep machine last night, and I stayed up too late doing the polls and then got woken up too early by the automated pharmacy phone call that didn't even have the fucking audacity to actually work, and I've been unmedicated for my narcolepsy for weeks, and so all of this is why I'm literally crying over metaphorical spilled milk but fuck it. I'm at my grandma's and in addition to forgetting my laptop charger I forgot my thyroid medicine. I can go without my thyroid medicine for a night without any symptoms, but the thing is I'm overdue for a blood draw to test how well the medicine is working. It's supposed to be annual but like, COVID and graduating from college and the changing health insurance and just my lack of get-up-and-go fucked it all up. So like six months ago I got the blood drawn for like the first time in like four years and the doc was like "hmmm, levels are low, let's increase the meds" except the pharmacy wouldn't send me the new dosage because I had just had the old dosage refilled and I've been playing phone tag with so many people for so long trying to get medicated on something that works again for the narcolepsy that after trying once I was like fuck it, I will wait for the THREE MONTHS it takes to finish the lower dose, and then I STILL needed to make phonecalls to get the new dose. And then I needed to take the new dose for 6–8 weeks before a new blood draw and I've been doing SO good at taking it every night (I think at least) and now it's time for me to get tested but I haven't yet and now I've missed the dose and I just wanna sleep and not drive the hour+ home and like, fuck it fuck it fuck it! And I was totally ready to go home too, but I called my mom first just to have her check that I really left it there and without me even asking her opinion she said I should just stay here for the night and take it easy on myself and that took ALL the wind out of my sails but here I am literally crying about it.

I wish I wasn't sick I wish I didn't need medicine or machines (which I didn't bring but at least that was a decision made on purpose) I wish pharmacies and insurance and doctors didn't have so many hoops I need to perpetually jump through and most of all I which all this bullshit actually worked and that I felt not-bad and not-tired at some points in my life instead of just less-bad and less-tired.

[glitteringcrab]

Morty is so dumb that...

1. He regularly disarms Rick's neutrino bombs. The first time he did it it was completely on the fly, no prior experience. Yet, he did it.

2. He has a knack for learning alien languages... as for the tree people in the battery dimension, it was obviously done without any sort of translator or support. (And he took over as their leader)

3. He's quick on his feet and can think his way out in a stressful situation, figuring out things that Rick can't and coming up with innovative solutions.

4. He figured out how to use a portal gun.

5. He can figure out how machines he's never seen nor used before work, and employ them successfully.

6. Beat Rick (smartest man in the universe?) in a board game.

7. Can manipulate said "smartest man in the universe", if he so chooses.

8. Became a successful stock broker. Out of the blue. Just did it.

9. Run. Whole. Freaking. Civilizations (and also toppled them as Marta)

10. Pitches good ideas that Rick typically ignores

11. When suddenly becomes motivated to try, he is good at math

12. His ideas were good enough that he would have gotten a deal for a movie production...!

13. His default intelligence is maxed out.

...At this point, it's only a matter of time before he starts making his own inventions, Eyepatch-Morty-style.

GUYS.

The only reason we've been thinking that Morty is stupid is that Rick has been calling him stupid repeatedly.

Sure, Morty does plenty of dumb stuff, but so does Rick. Rick has the emotional intelligence of a four year old and throws tantrums of cosmic proportions whenever slighted (vat of acid? submit to the selfie?), while often going ahead with complicated, innovative ideas... that in reality solve nothing and are a waste of time (Pickle Rick?? Leg Rick?? Cloning his own daughter? The dumb time-loop in his own dimension? Replacing himself with a robot? Creating a robot ghost to scary Mr Poopybutthole instead of just telling him to leave??) Not to mention his many incredibly lame jokes.

Everyone does dumb stuff occasionally!!! No one is an impeccable genius of non-stop moments of brightness!! (even Eyepatch Morty, the most cautious character, the character who has made basically NO MISTAKES up to now, sounds dumb a couple of times: "I'm gonna do the thing I wanna do, with the curve thing" and "My biggest fear is other people being afraid. Of fear. Itself." lol).

If Rick hadn't been calling Morty a freaking idiot with every breath available, we wouldn't be thinking "oh look haha the moron became a stock broker, what a joke, must be some sort of fluke"; we would be thinking "what an incredibly gifted kid".

We would attribute Morty's many mistakes to lack of experience, to lack of wisdom, to youth, to enthusiasm, to idealism, to teenager hormones, to acting hastily.

We would wish to see him eventually mature, apply his time and effort to worthwhile endeavors instead (mainly) of inane teenage stuff. We would wish to see him do well in school, we would wish to see him reach his full potential and succeed in great things.

Only Rick keeps pounding our heads with how stupid Morty is, and all of Morty's successes are never mentioned again, but getting lost to oblivion in comparison to Rick's (who has 60+ years more experience) genius.

WE VIEWERS ARE BEING UNWITTINGLY MANIPULATED THE EXACT SAME WAY MORTY IS.

[bisexualbaker]

Oh goody, another excuse to share my alternative phrase for "to google":

"Duck around and find out"

😃

Maybe this goes without saying, but DO NOT trust google’s AI results if you search something about bugs. Every single AI result I’ve seen about bugs so far has been wildly incorrect.