Tuesday, February 28
How to Think - VERSUS - What to Think
That may seem like a silly statement because people think all the time.
- Do I need to eat?
- Do I need to sleep?
- Do I need to work?
- Should I take a shit now or later?
- How much alcohol should I drink tonight?
Of course there are more serious decisions that these that most of us have to make but you get the point... we think about what to do all the time...
But, what happens when it comes time to make the serious decisions like IN WHAT DO I REALLY BELIEVE?
Do I believe then what people tell me to believe or tell me what to think about what I believe... or, do I make those decisions alone.
I am reminded of what a relative once said to me... Baptist teach people what to think while Methodists teach people how to think.
I also remember teaching college classes to college students who obviously had graduated from high school... and I gave them minimal instructions about a project I wanted them to work on as a team. The project was expanding the current campus into a nearby city 50 miles away.
These college students who were actually getting ready to graduate so that had almost completed 16 years of education, did not know where to begin because I gave them no specifics. They bitched, pissed, and moaned not knowing what I wanted... If they did not know what I wanted, then they could be guaranteed of an "A". If I told them exactly what I wanted, then they would give me that and expect an "A".
I told them their potential employers were not going to give them specifics either and would be expecting them to figure all that out on their own because they were college graduates...
I had forced them to think rather than telling them what to think...
NO ONE HAD TAUGHT THEM HOW TO THINK ON THEIR OWN...
I would not have hired any of these students to work at my company after seeing them in this class...
As a side note... The Democrats are telling us what to think rather than teaching us how to think...
Autonomous Vehicles 2023
The past decade has seen a wave of progression and regression in autonomous driving. Automakers have continued to prioritize R&D, yet progress remains incremental and not at a pace where Level 4 autonomy is near mass deployment.
Deployment of fully autonomous vehicles hasn’t progressed as quickly as we’d hoped, though incremental algorithm development has made advanced driver-assistance system technology and other subsystems safer in the progressive march to autonomy.
Last year saw a few major milestones in autonomous driving, including General Motors and Trimble logging 34 million miles (54.7 million km) of successful hands-free driving and Mobileye’s spin-out from Intel which raised $861 million during the IPO. So, interest and progress in the industry persist.
As we begin 2023, we remain hopeful for more solid advancements in the self-driving world.
Until now, the industry at large has used an ADAS approach, providing semi-autonomous driving via cameras and lidar sensors. While it’s cost-effective to use only cameras and basic sensors, a more robust and safer option encompasses more sensors. This is where the future lies, but the cost for mass deployment of these sensors is not currently where it needs to be to make mass-market adoption achievable.
So, the question remains: How do we develop a mass market approach to full autonomy at a price point similar to what we have today – or even less expensive? The answer lies in moving past sensor technology and learning to apply better algorithms that can spot pedestrians, see lane markers, account for bad weather, automatically update in real-time and have overall better perception through new techniques coming to market.
In current ADAS technology, if your car sensor gets muddied by road grime or weather conditions, it isn’t functional, and the driver is forced to take over control of the vehicle. General Motors’ Super Cruise is a good example. It’s an assistance mechanism that provides some level of autonomy and is affordable, but comprehensive maps would make it more robust and closer to full autonomy. Keeping maps accurate in current semi-autonomous vehicles is a laborious process, including specialized vehicles that gather, relay and download information, followed by humans who declutter and clean up the maps before they can be useful and downloaded into a vehicle. By the time this all happens, the maps easily could be outdated.
To achieve full autonomy, the promise of mass-deployable, solid-state sensors in a true fused array needs to be realized, which is tough when operating in complex environments and all weather conditions. In addition, real-time driving condition and map updating is critical in serving as a continuous feedback process that is enabled by 5G knowing exactly where a vehicle is relative to others and the road.
In 2023, the industry must overcome two principal challenges if autonomy is to move to the next level. First, we need precise absolute positioning in all current GNSS-denied or -obscured environments, no matter the weather or road condition. Precise absolute positioning is defined as lane-level (10 cm [4 in.]) precision. Achieving that in all facets of a typical drive from freeway to downtown corridor and underground is essential.
Adding to this requirement is the need for Automotive Safety Integrity Level (ASIL) certification of the software, hardware, correction source and integrity of the management. With all ASIL-certified parts, OEMs will feel more confident the solutions can be used for mass production.
Second, to make these maps more accurate, crowdsourcing — using passenger or shared mobility vehicles, not vehicles dedicated solely for mapping – is crucial. Maps are only as good as they are current, so a continuous stream of data from road vehicles that regularly drive the same path is critical to keeping maps current and providing complete situational awareness of a vehicle. Techniques such as map-based localization are paramount to that process and fusing sensor data to help derive a correct position. By taking the GPS positions of a vehicle and using visual cues to understand where the vehicle is based on the odometer, map-based locations can perceive what is around the vehicle. READ MORE...
Monday, February 27
Deep Learnng of Machines
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
How does deep learning attain such impressive results?
In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images.
While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful:Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video.
Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.
Examples of Deep Learning at Work
Deep learning applications are used in industries from automated driving to medical devices.
Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.
Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.
Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells.
Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.
Electronics: Deep learning is being used in automated hearing and speech translation. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications. READ MORE...
At 75 Years of Age, What Have I Learned?
My first thought is that I have forgotten more than I remember which is probably not a good thing to admit but it is true. A lot of knowledge I learned that I never used... and, this goes back to high school where the courses were designed specifically for those students who wanted to go to college but not for those students who just wanted to get a job or learn a trade.
College was similar, especially my first two years where I have to take all these general education courses like history, science, math, religion, art, the humanities, etc. I don't recall any time during my career where I used any of this information...
And, sadly enough, this also includes a majority of the course I took in grad school. We had to pass 60 hours of classes whereas today that requirement for the same degree is 30 hours...
So, the main thing here that I learned is that I did not need to learn all that I needed to learn in order to have a 45+ year career... ALTHOUGH, if I did not learn what I did not need to learn, I would not have had the career that I had...
And, while that seems somewhat upside down to me and perhaps to you, it is the way our education system is currently designed...
What is A Psychopath?
Psychopathy is a neuropsychiatric disorder marked by deficient emotional responses, lack of empathy, and poor behavioral controls, commonly resulting in persistent antisocial deviance and criminal behavior.
Instead, psychopathy is characterised by an extreme lack of empathy. Psychopaths may also be manipulative, charming and exploitative, and behave in an impulsive and risky manner. They may lack conscience or guilt, and refuse to accept responsibility for their actions.
Signs of psychopathy
- behavior that conflicts with social norms.
- disregarding or violating the rights of others.
- inability to distinguish between right and wrong.
- difficulty with showing remorse or empathy.
- tendency to lie often.
- manipulating and hurting others.
- recurring problems with the law.
Showing sympathy for them plays into their hand, so keep discussions centered on facts only. Pointing out a psychopath's flaws can be the best way to disarm them. So when a psychopath blames someone else, turn the conversation back on them. Say something like, "Are you doing OK today?
Someone with this kind of personality disorder typically experiences four (4) or more of the following symptoms: failure to conform to social norms; deceitfulness; impulsivity; irritability and aggressiveness; a reckless disregard for other people's safety; consistent irresponsibility; and a lack of remorse.
Sunday, February 26
Advice From the Frog's Advisor
Jeremiah was a bullfrog
Was a good friend of mine
I never understood a single word he said
But I helped him a drink his wine
And he always had some mighty fine wine...
Advice From The Frog's Advisor
Please be seated and put all your electronic devices away as I would like to have your undivided attention... thank you.
I have been the Frog's advisor all of his adult life and it has been my pleasure to share my thoughts and recommendations with him, even though he seldom followed what I had to say... sometimes, I had the feeling that he had his mind already made up before he reached out to me.
And, it is this that I would like to talk with you about... having your mind already made up... it is a dangerous place in which to find yourself, because you are refusing to acknowledge and/or analyze other points of view.
We have a tendency to believe what is being told to us, rather than research that information ourselves to see if it even makes sense... for example: why would we want to END petroleum crude oil and go to electric vehicles, when the infrastructure to support those vehicles is not there? And, when you put the microscope on the concept, you find that our current electric grid will not support it either.
We are putting the cart before the horse because someone TOLD US that was the way to go, and we had our minds made up when other points of view were presented. Just like the Frog...
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