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This is by (source: ). Lokenauth has 15 years of experience in banking and tech. Twitter thread Andrew Lokenauth @FluentInFinance 07-08-2023 https://twitter.com/fluentinfinance/status/1677784579111899141?s=46&embedable=true&thread=true The lead image for this article was generated by HackerNoon's via the prompt “Dollar Bill.” AI Image Generator

10 Money Lessons I Wish I Knew When I Was 25

This is by (source: ). Lokenauth has 15 years in banking and tech. Twitter thread Andrew Lokenauth @FluentInFinance 07-04-2023 https://twitter.com/fluentinfinance/status/1676327143351959552?s=46&embedable=true&thread=true The lead image for this article was generated by HackerNoon's via the prompt “Billionaires.” AI Image Generator

This Is How Old These Billionaires Where When They Earned Their First Billion

This is by (source: ). Lokenauth has contributed to Forbes and TIME. Twitter thread Andrew Lokenauth @FluentInFinance 03-10-2023 https://twitter.com/fluentinfinance/status/1634437894818480128?s=46&t=hLXLvA2wSOmlARX4ZS1fvA&embedable=true&thread=true Feature image generated via prompt of ‘Dominos falling down’ HackerNoon Stable Diffusion

Is SVB's Failure the Beginning of a Domino Effect?

This thread was originally published on Twitter by Andrew Lokenauth 1. Importing data from websites: With Excel, you can connect to multiple data sources, text files, other Excel files, databases & websites. Select 'Data' > Get & Transform > From Web Press CTRL+V to paste the URL into the text box, then select OK This will save hours! 2. Sparklines: Sparklines allow you to insert mini charts inside any cell, and provides a visual representation of data! Use sparklines to show trends or patterns in data. On the 'Insert tab', click 'Sparklines' 3. Goal Seek: Get fast answers with Goal Seek. It is also known as What-if-Analysis. Goal Seek basically uses trial & error to back-solve a problem, by plugging in guesses until it arrives at the correct answer. https://twitter.com/FluentInFinance/status/1582484621014740992/photo/1?embedable=true 4. Conditional Formatting Conditional formatting helps to visualize data and shows patterns & trends in your data Select 'Home' > Conditional Formatting > Highlighting Cell Rules 5. Wildcards: Wildcards are special characters that allow you to perform partial matches in your Excel formulas. Excel has three wildcards: Tilde ( ~ ) Asterisk ( * ) Question mark ( ? ) 6. Transpose Transpose will transform items in rows, to instead be shown in columns (or vice versa) To transpose a column to a row: • Select the data in the column • Select the cell you want the row to start • Right click, choose paste special, select transpose 7. Duplicate data from the cell above Ctrl + D fills and overwrites a cell with the contents of the cell above it 8.Remove Duplicates Remove duplicates in a set of data in Excel • Use the shortcut: Alt + A + M You can filter a column to show a specific product or date. You can also sort in ascending or descending order. (10) Slicer: Slicers provide buttons that you can click to filter tables, or PivotTables 9. Filter Filter allows you to filter data. You can filter a column to show a specific product or date. You can also sort in ascending or descending order. 10. Slicer Slicers provide buttons that you can click to filter tables, or PivotTables Select 'Home', go to Insert > Slicer 11. Pivot Tables A powerful tool to calculate, summarize & analyze data, which allows you to compare or find patterns & trends in data. To access this function, go to "Insert" in the Menu bar, and then select "Pivot Table" 12. Auto-fill: With large data sets, instead of typing a formula multiple times, use auto-fill. There are 3 ways to do this: • Double click mouse on the lower right corner of a 1st cell, or • Highlight a Section and type Ctrl + D, or • Drag the cell down the rows DatedIf: Calculate the number of (1) days, (2) months, or (3) years between two different dates =DATEDIF(X,Y,"D") X = Start date cell Y = End date cell "D"= Time interval • D = Days M = Months Y = Years 14. TRIM TRIM helps to remove the extra spaces in data. TRIM can be useful in removing irregular spacing from imported data. =TRIM( ) 15. Index Match The main difference between VLOOKUP and INDEX MATCH is the column reference. VLOOKUP uses a static column reference but INDEX MATCH uses a dynamic column reference. Index Match is much more flexible as you can search by row, or by column, or by both. 16. XLOOKUP: XLookup is an upgrade compared to VLOOKUP or Index & Match. Use the XLOOKUP function to find things in a table or range by row. Formula: =XLOOKUP (lookup value, lookup array, return array) 17. IF The IF function makes logical comparisons & tells you when certain conditions are met. For example, a logical comparison would be to return the word "Pass" if a score is >70, and if not, it will say "Fail" An example of this formula would be =IF(C5>70,"Pass","Fail") 18. SUMIF Sum the values in a range, if they meet a certain criteria 19. SUMIFS: Sum the values in a range that meet multiple criteria Use it if you want the sum of two criteria: Apples & Pete Formula =SUMIFS (sum_range, criteria_range1, criteria1, ...) 20. COUNTIF: Counts the number of cells that satisfy a query. (Count the number of times a word has been mentioned). 21. COUNTIFS: Counts the number of times a criteria is met For example, it counts the number of times that both (1) apples and (2) price > $10, are mentioned. 22. UPPER, LOWER, PROPER: =UPPER, Converts text to all uppercase, =LOWER, Converts text string to lowercase, =PROPER, Converts text to proper case 24. CONVERT This converts one measurement to another. There are multiple conversions that you can do. An example is meters to feet, or Celsius to Fahrenheit. 24. Stock Market Data You can get stock data in Excel Enter a list of stock ticker symbols. then select the cells and go to the Data tab, then click the Stocks button within the Data Types group Excel will attempt to match each cell value to a company stock, and fill in data 25. Geography / Maps: Instead of researching geographical data or maps, use Excel With the Geography data type, you can retrieve data like population, time zone, area leaders, gasoline prices, language, and more Type the data you need, then go to Data Tab -> Geography Microsoft Excel should be taught in all schools! Excel is an underrated skill, which will help your career! Also published here.

Top 25 Microsoft Excel Tips That Will  Put You in the Top 1%

In this video, I will openly share everything about deep nets for computer vision applications, their successes, and the limitations we have yet to address. Watch the video References Read the article: https://www.louisbouchard.ai/ai-in-computer-vision/ Yuille, A.L., and Liu, C., 2021. . International Journal of Computer Vision, 129(3), pp.781–802, . Deep nets: What have they ever done for vision? https://arxiv.org/abs/1805.04025 Video Transcript 00:00 if you clicked on this video you are 00:01 certainly interested in computer vision 00:04 applications 00:04 like image classification image 00:06 segmentation object detection 00:08 and more complex tasks like face 00:10 recognition image generation or even 00:12 star transfer application 00:14 as you may already know with the growing 00:16 power of our computers 00:17 most of these applications are now being 00:19 realized using similar deep neural 00:21 networks 00:22 what we often refer to as artificial 00:25 intelligence models 00:26 there are of course some differences 00:28 between the deep nets used in these 00:30 different vision applications 00:31 but as of now they all use the same 00:34 basis of convolutions 00:35 introduced in 1989 by yan loken the 00:38 major difference here 00:40 is our computation power coming from the 00:42 recent advancements of gpus 00:44 to quickly go over the architecture as 00:46 the name says convolution 00:48 is a process where an original image or 00:50 video frame which is 00:51 our input in a computer vision 00:53 applications is convolved 00:55 using filters that detect important 00:57 small features of an image 00:59 such as edges the network will 01:01 autonomously learn 01:02 filter values that detect important 01:04 features to match the output we want to 01:06 have 01:07 such as the object's name in a specific 01:09 image sent as input 01:11 for a classification task these filters 01:13 are usually of size 01:15 3x3 or 5x5 pixel squares allowing them 01:18 to detect the direction of the edges 01:20 left right up or down just like you can 01:23 see in this image 01:24 the process of convolution makes a dot 01:26 product between the filter and the 01:28 pixels it faces 01:29 it's basically just a sum of all the 01:31 filter pixels multiplied with the values 01:34 of the images pixels 01:35 at the corresponding positions then it 01:38 goes to the right and does it again 01:40 convolving the whole image once it's 01:42 done these convolved features 01:44 give us the output of the first 01:46 convolution layer which we call this 01:48 output a feature map 01:49 we repeat this process with many other 01:51 filters giving us multiple feature maps 01:54 one for each filter used in the 01:56 convolution process 01:57 having more than one feature map gives 01:59 us more information about the image 02:01 and especially more information that we 02:03 can learn during training 02:04 since these filters are what we aim to 02:06 learn for our task 02:08 these feature maps are all sent into the 02:10 next layer 02:11 as input to produce many other smaller 02:14 sized 02:14 feature maps again the deeper we get 02:17 into the network the smaller these 02:18 feature maps gets 02:20 because of the nature of convolutions 02:21 and the more general the information of 02:23 these feature maps become 02:25 until it reaches the end of the network 02:27 with extremely general information 02:29 about what the image contains disposed 02:31 of our many feature maps 02:33 which is used for classification or to 02:35 build a latent code 02:37 to represent information present in the 02:39 image in the case of a gan architecture 02:41 to generate a new image 02:43 based on this code which we refer to as 02:45 encoded information 02:47 in the example of image classification 02:49 simply put 02:50 we can see that at the end of the 02:52 network these small feature maps contain 02:54 the information about the presence of 02:56 each possible class telling you whether 02:58 it's a dog a cat 03:00 a person etc of course this is super 03:03 simplified 03:04 and there are other steps but i feel 03:06 like this is an accurate summary of 03:07 what's going on 03:08 inside a deep convolutional neural 03:10 network 03:11 if you've been following my channel and 03:13 posts you know that deep neural networks 03:15 proved 03:16 to be extremely powerful again and again 03:18 but they also have weaknesses and 03:20 weaknesses 03:21 that we should not try to hide as with 03:24 all things in life 03:25 deep nets have strength and weaknesses 03:27 while strengths are widely shared 03:30 the latter is often omitted or even 03:32 discarded by companies 03:34 and ultimately by some researchers this 03:36 paper 03:37 by alan yule and chenxileo aims to 03:40 openly share 03:41 everything about deep nets for vision 03:43 applications their success and the 03:45 limitations we have to address 03:47 moreover just like for our brain we 03:50 still do not fully understand their 03:52 inner workings 03:53 which makes the use of deep nets even 03:55 more limited since we cannot maximize 03:57 their strength 03:58 and limit weaknesses as stated by o 04:00 hobart 04:01 it's like a road map that tells you 04:02 where cars can drive but doesn't tell 04:04 you when or where 04:06 cars are actually driving this is 04:08 another point they discuss 04:09 in their paper namely what is the future 04:12 of computer vision algorithms 04:14 as you may be thinking one way to 04:16 improve computer vision applications is 04:18 to understand our own visual system 04:20 better starting with our brain which is 04:23 why 04:24 neuroscience is such an important field 04:26 for ai 04:27 indeed current deep nets are 04:29 surprisingly different than our own 04:31 vision system 04:32 firstly humans can learn from very small 04:35 numbers of examples 04:37 by exploiting our memory and the 04:39 knowledge we already acquired 04:40 we can also exploit our understanding of 04:43 the world and its physical properties to 04:45 make 04:45 deductions something that a deep net 04:47 cannot do in 1999 04:50 gupp nick ital explained that babies are 04:52 more like tiny scientists 04:54 who understand the world by performing 04:56 experiments 04:57 and seeking causal explanations for 05:00 phenomena rather than 05:01 simply receiving stimulus from images 05:03 like current 05:04 deep nets do also we humans 05:07 are much more robust as we can easily 05:10 identify an object from any viewpoint 05:12 texture it has occlusions it may 05:14 encounter and novel context 05:16 as a concrete example you can just 05:18 visualize the annoying captcha you 05:20 always have to fill in 05:22 when logging into a website this captcha 05:24 is used to detect butts since they are 05:26 awful 05:27 when there are occlusions like this as 05:30 you can see here 05:31 the deep net got fooled by all the 05:33 examples because of the jungle context 05:35 and the fact that a monkey 05:37 is not typically holding a guitar this 05:39 happens because it's certainly not in 05:41 the training data set 05:43 of course this exact situation might not 05:45 happen very often 05:46 in real life but i will show some more 05:48 concrete examples 05:49 that are more relatable and that already 05:52 happened later on 05:53 in the video deep nets also have 05:55 strength that we must highlight 05:57 they can outperform us for face 05:59 recognition tasks since 06:00 humans are not used to until recently 06:03 seeing more than a few thousands of 06:05 people 06:05 in their whole lifetime but this 06:07 strength of deep nets also comes with a 06:09 limitation 06:10 where these faces need to be straight 06:12 centered clear 06:14 without any occlusions etc indeed 06:17 the algorithm could not recognize your 06:19 best friend at the alwyn party 06:21 disguised in harry potter having only 06:23 glasses and a lightning bolt on the 06:25 forehead 06:26 where you would instantly recognize him 06:28 and see 06:29 whoa that's not very original it looks 06:31 like you just put glasses on 06:33 similarly such algorithms are extremely 06:36 precise radiologists 06:37 if all the settings are similar to what 06:39 they have been seeing 06:40 during their training they will 06:42 outperform any human 06:43 this is mainly because even the most 06:45 expert radiologists have only seen 06:48 a fairly small number of ct scans in 06:50 their lives as they suggest 06:51 the superiority of algorithms may also 06:54 be because they are doing a low priority 06:56 task 06:57 for humans for example a computer vision 06:59 app on your phone can identify the 07:02 hundreds of plants in your garden much 07:04 better than 07:05 most of us watching the video can but a 07:08 plant expert 07:08 will surely outperform it and all of us 07:11 together as well 07:12 but again this strength comes with a 07:14 huge problem 07:15 related to the data the algorithm needs 07:17 in order to be this powerful 07:19 as they mentioned and as we often see on 07:21 twitter and article titles 07:23 there are biases due to the data set 07:25 these deep nets are trained on 07:28 since an algorithm is only as good as 07:31 the data set it is evaluated on 07:33 and the performance measures used this 07:35 dataset limitation 07:37 comes with the price that these deep 07:39 neural networks are much 07:40 less general purpose flexible and 07:43 adaptative 07:44 than our own visual system they are less 07:46 general purpose 07:47 and flexible in the way that contrary to 07:50 our visual system 07:51 where we automatically perform edge 07:53 detection binocular stereo 07:55 semantic segmentation object 07:57 classification scene classification and 07:59 3d 08:00 depth estimation deep nets can only be 08:02 trained to achieve 08:03 one of these tasks indeed simply by 08:05 looking around 08:06 your vision system automatically 08:08 achieves all these tasks with extreme 08:10 precision 08:10 where deep nets have difficulty 08:12 achieving similar precision on one of 08:14 them 08:14 but even if this seems effortless to us 08:17 half of our neurons are at work 08:19 processing the information 08:20 and analyzing what's going on we are 08:23 still 08:23 far from mimicking our vision system 08:26 even with the current depth of our 08:27 networks 08:28 but is that really the goal of our 08:30 algorithms will it be better to just 08:33 use them as a tool to improve our 08:35 weaknesses i couldn't say 08:36 but i am sure that we want to address 08:39 the deep nets limitations 08:40 that can cause serious consequences 08:43 rather than omitting them 08:44 i will show some concrete examples of 08:47 such consequences 08:48 just after introducing these limitations 08:50 but if you are too intrigued you can 08:52 skip 08:52 right to it following the timestamps 08:54 under the video and come back to the 08:56 explanation 08:57 afterwards indeed the lack of precision 08:59 we previously mentioned 09:01 by deepnets arises mainly because of the 09:03 disparity between the data we use to 09:05 train our algorithm 09:06 and what it sees in real life as you 09:09 know an algorithm needs to see a lot of 09:11 data 09:11 to iteratively improve at the task it is 09:14 trained for 09:14 this data is often referred to as a 09:17 training data set 09:18 this data disparity between the training 09:20 data set and the real 09:22 world is a problem because the real 09:23 world is too complicated to accurately 09:25 be represented 09:27 in a single data set which is why deep 09:29 nets are less additive than our vision 09:31 system 09:32 in the paper they call this the 09:34 combinatorial complexity explosion of 09:36 natural images the combinatorial 09:38 complexity 09:39 comes from the multitude of possible 09:41 variations within a natural image 09:43 like the camera pose lighting texture 09:46 material 09:47 background the position of the objects 09:49 etc biases can appear at any of these 09:52 levels of complexity 09:53 the data set is missing you can see how 09:56 these large data sets now seem 09:57 very small due to all these factors 10:00 considering that having only 10:02 let's say 13 of these different 10:04 parameters and we allow only 10:06 1000 different values for each of them 10:08 we quickly jump to this number of 10:10 different images 10:11 to represent only a single object the 10:14 current data sets only cover a handful 10:16 of these multitudes of possible 10:18 variations for each object 10:20 thus missing most real-world situations 10:22 that it will encounter in production 10:24 it's also worth mentioning that since 10:26 the variety of images is very limited 10:28 the network may find shortcuts to 10:31 detecting some objects as we saw 10:33 previously with the monkey where it was 10:34 detecting a human instead of a monkey 10:37 because of the guitar in front of it 10:39 similarly you can see that it's 10:40 detecting a bird here 10:42 instead of a guitar probably because the 10:44 model has never 10:45 seen a guitar with a jungle background 10:48 this is called 10:49 overfitting to the background context 10:51 where the algorithm does not focus on 10:53 the right thing 10:54 and instead finds a pattern in the 10:56 images themselves rather than on the 10:58 object of interest 11:00 also these data sets are all built from 11:03 images taken by photographs 11:04 meaning that they only cover specific 11:06 angles and poses that do not transfer to 11:09 all orientation possibilities in the 11:11 real world 11:12 currently we use benchmarks with the 11:14 most complex data sets possible to 11:16 compare the current algorithms and rate 11:18 them 11:19 which if you recall are very incomplete 11:21 compared to the real world 11:23 nonetheless we are often happy with 99 11:26 accuracy for a task on such benchmarks 11:29 firstly the problem is that this 11:31 one-person error is determined on a 11:32 benchmark data set 11:34 meaning that it's similar to our 11:35 training data set in the way that it 11:37 doesn't 11:37 represent the richness of natural images 11:40 it's normal because it's impossible to 11:42 represent the real world in just a bunch 11:44 of images 11:45 it's just way too complicated and there 11:47 are too many situations possible 11:49 these benchmarks we use to test our data 11:52 set to determine whether or not 11:53 they are ready to be deployed in the 11:55 real world application are not really 11:57 accurate to determine how well it will 11:59 actually perform 12:00 which leads to the second problem that 12:02 is how it will actually perform 12:04 in the real world let's see that the 12:06 benchmark data set is huge 12:08 and most cases are covered and we really 12:11 have 99 12:12 accuracy what are the consequences of 12:14 the one percent of cases where the 12:16 algorithm fails in the real world 12:19 this number will be represented in 12:21 misdiagnosis 12:22 accidents financial mistakes or even 12:25 worse 12:25 death such cases could be a self-driving 12:28 car 12:28 during a heavy rainy day heavily 12:30 affecting the death sensors 12:32 used by the vehicle causing it to fail 12:34 many depth estimations 12:36 would you trust your life to this 12:37 partially blind robot taxi 12:40 i don't think i would similarly would 12:42 you trust a self-driving car at night to 12:44 avoid 12:44 driving over pedestrians or cyclists 12:47 where even yourself had difficulty 12:49 seeing them 12:50 these kinds of life-threatening 12:51 situations are so broad 12:53 that it's almost impossible that they 12:55 are all represented in the training data 12:57 set 12:57 and of course here i use extreme 12:59 examples of the most relatable 13:01 application 13:02 but you can just imagine how harmful 13:04 this could be 13:05 when the perfectly trained and tested 13:07 algorithm misclassifies your ct scan 13:09 leading to misdiagnosis just because 13:12 your hospital has different settings in 13:13 their scanner or because you didn't 13:15 drink enough water 13:16 or die anything that would be different 13:19 from your training data 13:20 could lead to a major problem in real 13:22 life even if the benchmark 13:24 used to test it says it's perfect also 13:27 as it already happened 13:28 this can lead to people in 13:30 underrepresented demographics being 13:32 unfairly treated by these algorithms 13:34 and even worse this is why i argue that 13:37 we must focus on the task 13:38 where the algorithms help us and not 13:41 where they replace 13:42 us as long as they are that dependent on 13:44 data 13:45 this brings us to the two questions they 13:47 highlight how can we efficiently test 13:49 these algorithms to ensure that they 13:51 work on these enormous data sets 13:53 if we can only test them on a finite 13:55 subset and two 13:57 how can we train algorithms infinite 13:59 size data sets so that they can perform 14:01 well 14:02 on the truly enormous datasets required 14:04 to capture the combinatorial complexity 14:07 of the real world 14:08 in the paper they suggest to rethink our 14:11 methods for benchmarking performance 14:13 and evaluating vision algorithms and i 14:15 agree entirely 14:17 especially now where most applications 14:19 are made for real life users instead of 14:21 only academic competitions 14:23 it's crucial to get out of these 14:24 academia evaluation metrics 14:26 and create more appropriate evaluation 14:28 tools we also have to accept that data 14:31 bias exists 14:32 and that it can cause real world 14:34 problems of course we need to learn to 14:36 reduce these biases 14:38 but also to accept them biases are 14:40 inevitable due to the combinatorial 14:42 complexity of the real world 14:44 that cannot be realistically represented 14:46 in a single data set of images 14:48 yet thus focusing our attention without 14:51 any play of words with transformers 14:53 on better algorithms that can learn to 14:55 be fair 14:56 even when trained on such incomplete 14:58 data sets 14:59 rather than having bigger and bigger 15:01 models trying to represent the most data 15:04 possible 15:05 even if it may look like it this paper 15:07 was not a criticism of current 15:08 approaches 15:09 instead it's an opinion piece motivated 15:11 by discussions with other researchers in 15:14 several disciplines 15:15 as they state we stress that views 15:17 expressed in the paper 15:18 are our own and do not necessarily 15:20 reflect 15:21 those of the computer vision community 15:23 but i must say 15:24 this was a very interesting read and my 15:27 views are quite similar 15:28 they also discuss many important 15:30 innovations that happen over the last 40 15:32 years in computer vision 15:34 that is definitely worth reading as 15:36 always the link to the paper 15:38 is in the description below to end on a 15:40 more positive note we are nearly 15:42 a decade into the revolution of deep 15:44 neural networks that started in 2012 15:47 with alexnet and the imagenet 15:49 competition since then 15:51 there has been immense progress on our 15:53 computation power 15:54 and the deep net architectures like the 15:56 use of batch normalization 15:58 residual connections and more recently 16:00 self-attention 16:01 researchers will undoubtedly improve the 16:03 architecture of deep nets but we shall 16:05 not forget that there are other ways to 16:07 achieve intelligent models than going 16:09 deeper and using more data of course 16:12 these ways are yet to be discovered 16:14 if this story of deep neural networks 16:16 sounds interesting to you 16:18 i made a video of one of the most 16:19 interesting architecture 16:21 along with a short historical review of 16:23 deep nets i'm sure you'll love it 16:25 thank you for watching Also published on: https://www.louisbouchard.me/ai-in-computer-vision/

What Did AI Bring to Computer Vision?

Tech giants have transformed the modern world, bringing a new era of connectivity and innovation that touches every aspect of our lives. These powerhouses have been celebrated for their groundbreaking technologies and services that, among other things, redefine how business is conducted and how people connect with the rest of the world. The Dark Side of the Digital Titans However, behind the scenes, a darker side has frequently evaded public scrutiny: an intricate web of privacy concerns, monopolistic practices, and the immense societal influence held by these digital titans. , a veteran entrepreneur and the president of the St. Louis-based internet services and data center company Hostirian, has long been aware of the other side of the tech industry coin and has dedicated a good part of his career to warning or educating the general public regarding the industry's (mal)practices, particularly when it comes to people’s sensitive information. Ken Cox "The problems of data privacy need to be given much more attention as they are pivotal to our lives online," Cox says. "There are generations of people today that still maintain a sense of trust when they click the famous 'terms and conditions', for example, but what a lot of them don't understand is that, by doing that, companies can use the data for profit." Cox emphasizes that by freely sharing private information, ordinary people set themselves up for exploitation. In the last couple of years, there have been thousands of privacy breaches worldwide, with nearly 2000 in the US alone in 2022. reported "Now, more than ever, we need to raise awareness and push for regulations that will be able to address this massive problem." Furthermore, tech companies tremendously influence public discourse through their algorithms and advertising platforms, especially on news dissemination and certain political opinions. Questions have emerged regarding the transparency of these processes and their potential for manipulation or bias in shaping public opinion. This has also created specific ethical implications, including issues like algorithmic bias and the responsibility of these companies to address societal challenges such as misinformation, hate speech, and online harassment. "I understand that none of these can be completely eradicated, but where we stand right now, unless people educate themselves thoroughly on the matter, there’s barely anything they can do to protect themselves efficiently," Cox believes. Beyond the realm of information and privacy, the concentration of power in the hands of a few tech giants is also raising concerns about fair competition and market dominance. Certain companies have already been accused of monopolistic practices that stifle competition and limit consumer choice. Moreover, their influence on smaller businesses is indisputable, as they have much more power to drown them out. In contrast, have said they have been negatively affected by differential pricing based on company size. 41% of small business owners "Big companies have long been taking advantage of small businesses," Cox says. "Once upon a time, you could go outside and see all of these cool, unique shops, and now it's all about large chains at every corner. We as a society must start taking this power back before it gets completely out of hand." The exploitative nature of big tech companies when it comes to work conditions has also been a subject of debate. Some reports have highlighted questionable labor practices within the industry, including long hours and high-pressure environments. As Cox explains, as long as there are lax labor regulations or laws regarding worker safety, companies can continue to ignore workers' physical and mental well-being. "I sometimes can't believe it's 2023, and we still have to discuss forced labor. It’s horrible, so I’ve been trying to show people that things can be done differently as long as we persistently fight." Although the path toward a safer future in the technologically driven world is long and winding, Cox firmly believes that by providing comprehensive educational programs accessible to people from all walks of life, individuals can be equipped with the necessary knowledge and skills to make informed decisions, protect their privacy, and ensure the well-being of society as a whole. While large corporations often wield enormous power and influence, Cox maintains that united individuals can drive change and hold these corporations accountable for their actions. "Technology should be a tool for positive transformation rather than a source of inequality and harm. By advocating for transparency, ethical practices, and responsible use, we can steer the course toward a safer future," Cox says. This story was distributed as a release by Ascend under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

The Tech Giants' Influence Unveiled: Ken Cox's Call for Awareness and Regulation in the Industry

টেক জায়ান্টসের প্রভাব উন্মোচিত হয়েছে: শিল্পে সচেতনতা এবং নিয়ন্ত্রণের জন্য কেন কক্সের আহ্বান

Der Einfluss der Tech-Giganten enthüllt: Ken Cox‘ Aufruf zu Bewusstsein und Regulierung in der Branche

La influencia de los gigantes tecnológicos al descubierto: el llamado de Ken Cox a la concientización y la regulación en la industria

L'influence des géants de la technologie dévoilée : l'appel de Ken Cox à la sensibilisation et à la réglementation de l'industrie

टेक दिग्गजों के प्रभाव का अनावरण: उद्योग में जागरूकता और विनियमन के लिए केन कॉक्स का आह्वान

テクノロジー巨人の影響力が明らかに: ケン・コックス氏の業界への認識と規制の呼びかけ

공개된 거대 기술 기업의 영향력: Ken Cox의 업계 인식 및 규제 촉구

Revelada a influência dos gigantes da tecnologia: o apelo de Ken Cox por conscientização e regulamentação na indústria

Раскрытие влияния технологических гигантов: призыв Кена Кокса к повышению осведомленности и регулированию в отрасли

Teknoloji Devlerinin Etkisi Açıklandı: Ken Cox'un Sektörde Farkındalık ve Düzenleme Çağrısı

Ảnh hưởng của những gã khổng lồ công nghệ được tiết lộ: Lời kêu gọi nâng cao nhận thức và quy định trong ngành của Ken Cox

科技巨头的影响力揭晓：肯·考克斯呼吁行业提高意识和监管

Ensuring Reproducibility in AI Research: Code and Pre-trained Weights Open-Sourced

Joe Rogan is ten years older than me. So, when I say that I totally dig his tech-time-travelling vibe, I mean it. A time leap from cord phones to the newest iPhones can only be compared to Kubrick’s from bone to satellite scene in “2001: A Space Odyssey.” https://youtu.be/avjdKTqiVvQ?si=utAq8sni-PGK3-MN&embedable=true I still remember when my baby girl, who was still in kindergarten at the time, smashed her grandfather’s Nokia phone into pieces. She thought it was broken because the screen was unresponsive. We laughed when she asked what all those buttons were for. My father wasn’t angry. He just couldn’t get used to touchscreen phones. “Nah, leave it. Nowadays, kids are born with a remote in their hands. I’ll get a new phone. Let’s play. I kept some of your plushies. Disney is timeless.” Here’s the thing. GenZs, and especially Generation Alpha, take all these tech wonders for granted. I used to run with a walkman. Now, I feel so blessed and grateful for being able to use Galaxy Buds. The Joe Rogan Experience Episode #2220 You should definitely watch this one with Francis Foster and Konstantin Kisin as guests. It’s refreshing to talk about tech and AI even for a moment rather than to admire the presidential candidate’s superpower to talk for three hours straight without going to a bathroom. Joe was spot on comparing 2014 and 2024 from a tech perspective. In essence, nothing changed, dramatically. Now, I’m paraphrasing: But, 2034 it’s going to be bananas because of AI. Look, there’s no way to sugarcoat the fact that AI is a serial job killer. It is what it is. The next inevitable industrial revolution. You just can’t have it both ways, can you? Denis Villeneuve made my little boy’s dreams come true with his movie adaptation of “Dune.” It was an epic mind-blowing experience. And, the part two as a complete IMAX show, it’s impossible to describe. So, when Rogan expressed his concerns about the year 2034, I put two and two together. So many people lost their jobs because of AI and automation. Look, there’s no need to take this the wrong way. I have a law degree and I write. Do you know what happens with lawyers and accountants in “The Hitchhiker's Guide to the Galaxy?” Well, Google it, if you committed a heresy of not reading all the books. My brutally honest point being: hey, we can survive the AI purge of designers and writers, but as Rogan pointed it out, the truckers are the next level. The mega-cities fully dependent on supply lines. The millions of trucker jobs. The scene is set for the Great Revolt also known as… The Butlerian Jihad 2.0 “After two generations of chaos led by the Butlerians, the god of machine-logic was overthrown by the masses and a new concept was raised: "Man may not be replaced." The Jihad itself had finished with the complete destruction of all intelligent machines that were originally built by humans throughout the worlds, but it proved to have many profound impacts on the socio-political and technological development of humanity throughout the new empires that emerged, including a large technological reversal of the entire human civilization.” I will let the Dune Wiki Fandom page make the long Butlerian Jihad story short. Thou shalt not make a machine in the likeness of a human mind “Even the simplest computers and calculators were banned, with the penalty for building or owning such a thinking machine technology being put to trial and sentenced to immediate death. This lack of thinking technology created a severe gap in humanity's quality of life, revolving around a need for humans to perform complex logical computations and calculations. This gap led to the creation of the mentat order: the Bene Gesserit, and the Spacing Guild.” Now, I have to walk on eggshells. It’s ridiculous to think of truckers as tech destroyers. I have friends who depend on tech to stay in touch with their loved ones while on the road for weeks, sometimes even months. But, at the same time, it’s not unimaginable for some self-driving trucks to be set on fire in the not-so-distant future. So, compromises would have to be made. We won’t have AI gods, but self-driving AI trucks can save lives on ice roads. “Ice road truckers face a fatality rate of 112 deaths per 100,000 workers. The most common cause of death for ice road truckers is vehicle accidents, often due to icy road conditions and poor visibility. Despite the danger, ice road truckers are essential for transporting goods to remote areas in Alaska and northern Canada. One study found that the risk of dying on an ice road is 21 times higher than the risk of dying in a regular car accident. Ice road truckers are more likely to suffer from fatigue-related accidents due to long, grueling hours on the road.” So, hit the road Jack in your truck, and you, I’m looking at you, AI: we need to talk. Joe Rogan is ten years older than me. So, when I say that I totally dig his tech-time-travelling vibe, I mean it. A time leap from cord phones to the newest iPhones can only be compared to Kubrick’s from bone to satellite scene in “2001: A Space Odyssey.” https://youtu.be/avjdKTqiVvQ?si=utAq8sni-PGK3-MN&embedable=true https://youtu.be/avjdKTqiVvQ?si=utAq8sni-PGK3-MN&embedable=true I still remember when my baby girl, who was still in kindergarten at the time, smashed her grandfather’s Nokia phone into pieces. She thought it was broken because the screen was unresponsive. We laughed when she asked what all those buttons were for. My father wasn’t angry. He just couldn’t get used to touchscreen phones. “Nah, leave it. Nowadays, kids are born with a remote in their hands. I’ll get a new phone. Let’s play. I kept some of your plushies. Disney is timeless.” Here’s the thing. GenZs, and especially Generation Alpha, take all these tech wonders for granted. I used to run with a walkman. Now, I feel so blessed and grateful for being able to use Galaxy Buds. The Joe Rogan Experience Episode #2220 The Joe Rogan Experience Episode #2220 You should definitely watch this one with Francis Foster and Konstantin Kisin as guests. It’s refreshing to talk about tech and AI even for a moment rather than to admire the presidential candidate’s superpower to talk for three hours straight without going to a bathroom. Joe was spot on comparing 2014 and 2024 from a tech perspective. In essence, nothing changed, dramatically. Now, I’m paraphrasing: But, 2034 it’s going to be bananas because of AI. But, 2034 it’s going to be bananas because of AI. Look, there’s no way to sugarcoat the fact that AI is a serial job killer. It is what it is. The next inevitable industrial revolution. You just can’t have it both ways, can you? Denis Villeneuve made my little boy’s dreams come true with his movie adaptation of “Dune.” It was an epic mind-blowing experience. And, the part two as a complete IMAX show, it’s impossible to describe. So, when Rogan expressed his concerns about the year 2034, I put two and two together. So many people lost their jobs because of AI and automation. Look, there’s no need to take this the wrong way. I have a law degree and I write. Do you know what happens with lawyers and accountants in “The Hitchhiker's Guide to the Galaxy?” Well, Google it, if you committed a heresy of not reading all the books. My brutally honest point being: hey, we can survive the AI purge of designers and writers, but as Rogan pointed it out, the truckers are the next level. The mega-cities fully dependent on supply lines. The millions of trucker jobs. The scene is set for the Great Revolt also known as… The Butlerian Jihad 2.0 The Butlerian Jihad 2.0 “After two generations of chaos led by the Butlerians, the god of machine-logic was overthrown by the masses and a new concept was raised: "Man may not be replaced." The Jihad itself had finished with the complete destruction of all intelligent machines that were originally built by humans throughout the worlds, but it proved to have many profound impacts on the socio-political and technological development of humanity throughout the new empires that emerged, including a large technological reversal of the entire human civilization.” "Man may not be replaced." I will let the Dune Wiki Fandom page make the long Butlerian Jihad story short. I will let the Dune Wiki Fandom page make the long Butlerian Jihad story short. Dune Wiki Fandom page Thou shalt not make a machine in the likeness of a human mind Thou shalt not make a machine in the likeness of a human mind Thou shalt not make a machine in the likeness of a human mind “Even the simplest computers and calculators were banned, with the penalty for building or owning such a thinking machine technology being put to trial and sentenced to immediate death. This lack of thinking technology created a severe gap in humanity's quality of life, revolving around a need for humans to perform complex logical computations and calculations. This gap led to the creation of the mentat order: the Bene Gesserit, and the Spacing Guild.” Now, I have to walk on eggshells. It’s ridiculous to think of truckers as tech destroyers. I have friends who depend on tech to stay in touch with their loved ones while on the road for weeks, sometimes even months. But, at the same time, it’s not unimaginable for some self-driving trucks to be set on fire in the not-so-distant future. So, compromises would have to be made. We won’t have AI gods, but self-driving AI trucks can save lives on ice roads. We won’t have AI gods, but self-driving AI trucks can save lives on ice roads. “Ice road truckers face a fatality rate of 112 deaths per 100,000 workers . The most common cause of death for ice road truckers is vehicle accidents, often due to icy road conditions and poor visibility. Despite the danger, ice road truckers are essential for transporting goods to remote areas in Alaska and northern Canada. One study found that the risk of dying on an ice road is 21 times higher than the risk of dying in a regular car accident. Ice road truckers are more likely to suffer from fatigue-related accidents due to long, grueling hours on the road.” 112 deaths per 100,000 workers So, hit the road Jack in your truck, and you, I’m looking at you, AI: we need to talk. So, hit the road Jack in your truck, and you, I’m looking at you, AI: we need to talk.

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