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All You Need to Know About MATLAB (Matrix Laboratory) – Spiceworks News and Insights

October 1, 2022 | Author: admin

MATLAB is defined as the proprietary software app and programming language by MathWorks, which facilitates complex data analysis tasks such as algorithm implementation, interacting with other apps, and manipulating a data matrix. This article explains the purpose of MATLAB, its key concepts, and use cases in 2022.

MATLAB is the proprietary software app and programming language by MathWorks, which facilitates complex data analysis tasks such as algorithm implementation, interacting with other apps and manipulating a data matrix.

How MATLAB Operates | Source

MATLAB stands for Matrix laboratory. It was designed by Cleve Moller and developed by MathWorks. It is a multipurpose programming language for numerical computation.

The LINPACK and EISPACK projects were responsible for initial development so that they could offer access to the Matrix software they had created. With over 4 million users, MATLAB has become a must-have tool. Advanced engineering and science courses are used as instructional tools. Researchers use it in industries as a development and analysis tool.

MATLAB has features such as built-in editing, debugging tools, and data structure. It has easy-to-use graphic commands and various built-in commands and math functions that enable users to perform mathematical calculations. This software allows users to manipulate matrices, run algorithms, design user interfaces, and visualize multiple functions and data types. It is used for signal processing, image and audio processing, machine learning, and deep learning.

Here are the key features of MATLAB:

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MATLAB offers users numerous benefits, making it such an effective tool. It:

Further, errors in MATLAB are easy to fix as it is not a compiled language but an interpreted one. It also provides a platform for users to perform symbolic math operations using symbolic manipulation algorithms and tools.

However, there are a few constraints to remember. It is designed for scientific computing and, therefore, unsuitable for other applications. MATLAB as an interpreted language, is slower than other compiled languages such as C++. It is not a general-purpose programming language such as Fortran or C. Users have to create different files for different functions as MATLAB does not allow them to make functions in a single .m file, unlike other programming languages.

Finally, most MATLAB commands lack a direct equivalent in other programming language commands, as those commands are specific to MATLAB use only. This makes your skills non-transferable. Before we discuss how MATLAB works in more detail, here are some software applications that offer similar functionality:

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As a fourth-generation programming language, MATLAB is primarily applied in technical computing. It provides a user-friendly environment that allows them to perform computation, visualization, and programming functions.

When a program is written on MATLAB, it uses a just-in-time compiler to make the written program fast. Afterward, it assigns mathematical processing jobs to the computers central processing unit and optimizes library calls. Thus, it ensures that the program is solved more easily. The following components power MATLABs working:

The term MATLAB environment refers to the collection of tools and infrastructure made accessible to users on the MATLAB platform. Capabilities to manage variables in the workspace, and facilities to import and export data, are included in this component. Tools for organizing, creating, debugging, and profiling M-files and programs designed with MATLAB are also available in the environment.

A MATLAB environment can be used as an interactive calculator or a programming environment. In calculator mode, the built-in functions, algorithms, and toolboxes of MATLAB provide an all-in-one environment to perform calculations and visualize results using graphical plotting. On the other hand, MATLAB, in programming mode, has an editor, a debugger, and a profiler that enables users to write their functions and scripts.

When users start MATLAB, a window with several panels appears. This window has a workspace panel, a command window, a current directory panel, and a command history panel. The command window has a command line prompt used to run functions that work on variables. All variables are made and stored in the workspace, where the workspace panel lets users access them easily.

Users can view saved data files on the current directory panel. Users can access the history of all commands that have been executed from the command history panel. Additionally, MATLAB has other window panels that one can access as the need arises. Such windows include a debugger window, an array editor window, and a help browser window. Users can access helpful information about any function or toolbox can be accessed through the command line help function in the help browser.

On the command line, arrays are built from the ground up. In MATLAB, data is structured into multidimensional arrays. Users can modify arrays through addition or multiplication to achieve different objectives. In addition, individual elements are added, while one can achieve multiplication via matrix multiplication or multiplication of elements.

In calculator mode, you can change the elements of an array by double-clicking on the arrays name in the workspace panel. This opens the array editor, which lets you change the array by hand.

Meanwhile, in programming mode, you can change the elements by making a part of an array on the left-hand side of an assignment statement. Users can delete a row or column by putting it in an empty array. A 0 x 0 matrix is the same as an empty array.

In MATLAB, every variable is an array or a matrix. Variables in the workspace are visible from the workspace panel. Users can also access variables using the who command. The who command displays variables currently in memory, their types, the memory allocated to each variable, and whether they are complex variables or not. Data files are saved in the current directory accessible via the existing directory panel in the format .mat.

In MATLAB, functions are used to perform computational tasks. They were created to improve the basic functionalities of MATLAB. They only interact with one another via the arguments that act as the input and output. Functions have their isolated workspace for their variables. In calculator mode, users can write each line of the function one after the other at the command line, execute and copy it, if it works as expected.

A script refers to a file that consists of several sequential lines of MATLAB commands. In the workspace, scripts use variables. Functions and scripts consist of text files with a .m extension. To differentiate functions from scripts in MATLAB, the keyword function appears in the first line of the text content, right at the beginning.

N-D rays are multidimensional arrays used in MATLAB. Either the fundamental 2-D arrays are extended to generate them, or the arrays are constructed from scratch directly by applying functions such as zeros and ones. Dense arrays have to be represented by N-D arrays since they cant be stored in the same way as sparse arrays.

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A function is a collection of statements that, when combined, carry out a specific job or task. MATLAB functions are specified in separate files, such as script files. One may download MATLAB here. The functions name and the files name must always be comparable.

In general, functions will take in more than one parameter and may return more than one argument after processing those arguments. Variables inside a functions workspace, known as the local workspace, are subject to the functions operations. This workspace is distinct from the base workspace, which may be reached using the MATLAB command prompt.

In MATLAB, functions can be created using the below syntax: function [out1,out2, , outN] = run (in1,in2,in3, , inN). From this function, run is the name of the function that accepts the input arguments in1, in2inN and returns output out1, out2outN.

Now let us turn to the five types of MATLAB functions:

Primary functions are usually defined within a file. They are listed first in the function file. Additionally, main functions may be invoked from outside the file in which they are defined, either by additional functions or via the command line.

Sub-functions are similarly defined within a file. Optional sub-functions may appear after primary functions within a file. Unlike major functions, sub-functions cant be invoked from other functions or the command line outside of the file that specifies them. They are accessible to the principal function and additional sub-functions inside a function file that specifies sub-functions.

Nested functions are defined within another function or parent function. A nested function can access and alter the variables declared by its parent function. They are declared inside the context of some other function and have access to the workspace of the parent function.

A function is defined in MATLAB with a single statement. It consists of a single MATLAB statement and an unbounded quantity of input and output parameters. One may create anonymous functions at the command line or inside a script or function in MATLAB. This allows users to build essential functions without creating a separate file. Thus, they are not stored in program files.

Private functions are only accessible to a small subset of other functions. It is a type of primary function that resides in subfolders known as private. Users can create private functions to avoid revealing the implementation of a function. Users cant invoke private functions from the command line or outside their parent folder. The following are examples of essential MATLAB functions:

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MATLAB is used in several industries, including the automotive, biotech, and pharmaceutical sectors and the electronics, artificial intelligence, robotics, and communication sectors. It is helpful for data scientists, mechanical engineers, machine learning experts, computational finance specialists, and research scientists. The following are the uses of MATLAB:

Data analytics involves studying and analyzing data to get valuable insights. Data analytics is usually done with software and tools. MATLAB provides an environment where data scientists, engineers, and IT specialists can effectively analyze data. They can also build big data analytics systems for instance, for financial analysis.

Organizations can use MATLAB to perform an economic assessment. It has tools that financial specialists can use to evaluate factors such as profitability, solvency, liquidity, and organizational stability.

MATLAB provides a platform where users can easily control whole systems and devices. Users can use MATLAB to create a control system for various industrial systems. The control systems are based on the control loop. Through the control system, users can give commands to the parts of the system, manage them, and regulate their behavior.

For instance, engineers can create a control system using MATLAB to enable them to control heating systems easily. Additionally, MATLAB has a control system toolbox that allows users to analyze algorithms and apps and design linear control systems.

Embedded systems refer to computer components comprising more than 90% hardware and 10% software. They are designed to perform specific tasks. MATLAB has a unique push button feature that generates a code and runs it on the hardware after it is pushed. Examples of embedded systems are microwaves, cameras, and printers.

Motor control algorithms are helpful in the regulation of speed and other performance characteristics of an application. MATLAB algorithms help with precision control, energy efficiency, and system protection. In the development stage, MATLAB can help users reduce the time to develop algorithms and cost-save them before committing to expensive hardware testing.

Testing and measuring electronic products is a standard manufacturing best practice. Electronic products are subjected to various tests during this process to ensure that only quality and standard products are sent to the market. Physical examinations are carried out to identify any material defects, while functional tests are carried out to ensure that the products work as expected.

MATLAB allows engineers to perform tasks while testing and measuring electronic products. It provides them with the necessary tools and helps them automate tasks. Additionally, they can use MATLAB to perform live visualization and data analysis from the data they collect.

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Computers and unique digital signal processors perform various signal processing operations in digital signal processing. The MATLAB environment makes it easier for users to use signal processing techniques when analyzing time series data. It also provides a unified workflow for developing streaming applications and embedded systems.

Robotics is a multidisciplinary field of science and engineering that involves the creation of robots or human-like machines. MATLAB provides an all-in-one environment where robotic researchers and engineers can design robots. They can use MATLAB to create and tune algorithms, generate codes automatically and make real-world model systems.

Mechatronics combines the scientific fields of electronics and mechanical engineering. In mechatronic systems, electrical, mechanical, control, and embedded software subsystems are integrated. MATLAB provides an all-in-one environment where mechatronic engineers can design and simulate all those subsystems.

Image processing focuses on processing raw images to prepare them for other tasks, such as computer vision. In image processing, pixels of images are managed through the modification of matrix values with the help of math techniques. Meanwhile, computer vision involves looking at pictures as the human eye does, then understanding and predicting the visual output.

MATLAB provides an environment where the vital process of building algorithms and analysis of images can be done. For instance, it includes machine learning algorithms that support applications that enhance pictures by using face beauty and scanning barcodes. Digital image processing is also helpful in transmitting, receiving, and decoding data from satellites.

Engineers design predictive maintenance techniques to determine the equipments condition to figure out when users must conduct maintenance. MATLAB has a predictive maintenance toolbox that engineers can use to level data, design condition indicators, and estimate the remaining useful life of a machine.

Wireless communication involves connecting two or more devices using a wireless signal. Engineers working in teams can boost productivity by working with MATLAB. With MATLAB, they can reduce development time as they can easily exchange ideas and eliminate design problems early by pointing out overlooked errors. MATLAB also provides streamlined testing and verification of wireless devices.

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MATLAB is indispensable for technical teams working with data operations and user interfaces (UI). It simplifies complex calculations, makes it easy to work out AI and ML algorithms, and facilitates UI simulation and design. MATLAB is also available directly online via your web browser, removing the need to install software locally. Ultimately, MATLAB combines visualization, advanced computation, and programming in an easy-to-use way.

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Top 10 Best Countries to Study AI for Indian Aspirants in 2023 – Analytics Insight

October 1, 2022 | Author: admin

Life without data is quite difficult to even think about. There isnt a single activity you do that doesnt involve data. With such humungous data available, it is important that the data collected is put to the best possible use. This is where technology plays a significant role and why artificial Intelligence is critical. If you are an AI aspirant, you have landed at the right place. On that note, have a look at the top 10 best countries to study AI for Indian aspirants in 2023.

UK has gained wide recognition as a place that has succeeded in establishing the link between artificial intelligence (AI) and the Financial Technology industry (FinTech). That being said, can anything get better than being a part of a wide range of international summits such as the Deep Learning Summit, AI Summit, ODSCs European Conference, etc.?

Canada takes pride in being the home to the top universities in the world in the field of artificial intelligence. The universities here have contributed to various scholarly disciplines and commercial innovations involving AI and Big Data.

India, too, has countless opportunities in the field of AI. Out of the many cities in this context, Bangalore, Mumbai, and Hyderabad are considered to be a hub for every significant technological advancement. These cities also boast of several dedicated Artificial intelligence labs, thus making India an absolute favorite place to get educated from.

Undoubtedly, the USA is one of the dream locations to work for the ones interested in making a career in AI. A point worth a mention is that one can find the headquarters of almost every major big American tech company in the USA.

The manner and extent to which artificial intelligence, data science, and other technical aspects are adopted in France are quite astonishing. Thus, theres no denying that the future of AI here is bright and is all set to open opportunity doors for artificial Intelligence aspirants and professionals.

This European country pays you quite handsomely way beyond your expectation levels in the AI domain. With top organizations such as Dell, HP, IBM, Microsoft, Google, and Oracle, to name a few, having ample opportunities in the field of artificial intelligence here, you know where to go!

Yes, Germany is known for varied opportunities in the automobile sector. Additionally, the country also boasts of an ample number of career options/job roles in the field of AI. Well, you will have a promising career here.

Singapore boasts of a string of top-rated universities such as the National University of Singapore, Nanyang Technological University, Singapore Management University, and more. Having said that, you know why the country features in the list of the top 10 best countries to study AI for Indian aspirants.

China is home to several amazing technology-oriented Universities right from Peking University to Tsinghua University, Fudan University to Zhejiang University. You now have a portfolio of Universities to choose from.

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Behind the blackout triggered by Hurricane Fiona is a long-embattled history of Puerto Rico’s weak and outdated electrical grid – CNN

October 1, 2022 | Author: admin

CNN

Less than two weeks after Hurricane Fiona made landfall on Puerto Rico, triggering an islandwide blackout for 1.5 million customers, power has been restored to 84% of residents, officials said.

Fiona hit the US territory as a Category 1 storm September 18, dropping record rainfall, unleashing mudslides, flooding neighborhoods and leaving most of the island without power or water. The islands health department said at least 25 deaths may be linked to the storm.

Fiona made landfall almost exactly five years after 2017s Category 4 Hurricane Maria left many residents without electricity for months and delivered a blow from which the island has never fully recovered.

Power outages on the island have been a long-running source of frustration for Puerto Ricans who rely on a fragile and poorly-maintained power grid, with modernization efforts slow to materialize over several decades, first by a publicly-owned entity and today by a private caretaker.

The highly centralized grid has one major power line which, if compromised, shatters the entire system. The grid continues to suffer from a history of underinvestment and an outdated energy infrastructure, making it vulnerable to natural disasters and prone to extensive outages.

Because the whole system hasnt been properly cared for or modernized, we are in a position where anytime a storm hits or there is some sort of natural disaster, the whole grid falls apart, said Lpez Varona of the Center for Popular Democracy, an advocacy group organizing recovery efforts for Puerto Rico.

LUMA Energy, the Canadian-American power company responsible for power distribution and transmission on the island, took over management of the grid in 2021 from the government-owned Puerto Rico Electric Power Authority, known as PREPA, which has relied on fossil fuels to power the system.

LUMA, which landed a 15-year operation and maintenance deal, has faced growing criticism from activists and residents for steep billing costs and widespread blackouts, as well as calls for the government to terminate its contract.

PREPA, which is still in charge of power generation on the island, was created in 1941 as the islands sole electric utility.

It filed for bankruptcy in 2017 under Title III of the Puerto Rico Oversight, Management, and Economic Stability Act of 2016, which created a legal framework for restructuring the US territorys $74 billion debt. In September, mediation talks to restructure the PREPAs $9 billion debt to bondholders ended without a deal.

Gov. Pedro Pierluisi gave his first public criticism of LUMA in August, saying he is not satisfied with its performance and said it must make changes to significantly improve its services.

The House of Representatives Committee on Energy and Commerce wrote to LUMA on September 27 expressing deep concerns over the power outages following Fiona. It said the company had not prepared the islands energy infrastructure to withstand a Category 1 hurricane.

Ongoing outages and the complete disruption of power following Hurricane Fiona amplify concerns that LUMA has failed to adequately develop and maintain crucial electrical infrastructure in Puerto Rico despite its lucrative 15-year contract, three committee leaders wrote to LUMA Energy President and CEO Wayne Stensby.

In addition to LUMAs service being riddled with chronic power outages and disruptions, the letter states, Puerto Ricans spend 8% of their income on electricity, compared to just 2.4% spent by the average citizen in mainland United States.

In September 2020, the US Federal Emergency Management Agency (FEMA) dedicated more than $9.4 billion in funding for projects to transform the grid and more funds have been added since, bringing the total to roughly $12 billion, which represents the largest allocation of funds in FEMAs history.

FEMA approved an additional $107.3 million in funding earlier this year to modernize the grid in the aftermath of Maria, which includes 15 major projects to repair and restore the system.

Dr. Shay Bahramirad, senior vice president of Engineering, Asset Management and Capital Programs for LUMA who is serving as the incident command for Fionas impact, told CNN significant progress has been made through the projects funded by FEMA.

LUMA has made improvements during the first year of its contract, such as improved safety for employees, 30% fewer outages than PREPA, replacing more than 3,000 broken utility poles and connecting more than 25,000 customers to rooftop solar power energy, according to a progress update released in June.

Bahramirad said the poor design of the system, along with a lack of maintenance and management, has created cascaded outages.

There is a sense of urgency. I completely understand the frustration of our customers. However it is equally important to do this right, Bahramirad said, adding it is critical the system is getting built based on data science and sound engineering per industry standards.

Hurricane Maria led to an overall vision for the need of reforming the energy system, said Alejandro Figueroa, director of infrastructure for the Financial Oversight and Management Board for Puerto Rico, which was established under the oversight legislation in 2016. He previously served as general counsel for the Puerto Rico Energy Bureau, the islands energy regulator.

The board was created to restructure PREPAs debt, putting it on a path of fiscal stability and overseeing efforts to make the system more resilient.

Figuroa pointed to two key causes of the systems problems: A revolving door of management of the publicly-owned entity, which he said mimicked political cycles, as new governors would bring in new managers, and PREPAs rates, which remained unchanged for nearly two decades.

He explained as inflation and recessions raised the cost of materials and labor, and the population of the island dwindled, the company could not keep up with maintenance, office operations or its debt, and the system was ripe for disaster when Maria struck.

Under the restructuring, power distribution, transmission and maintenance were shifted to LUMA, a private operator, a move many activists have criticized, arguing it has only worsened the systems reliability.

Why are there so many blackouts? Mostly, its because of lack of investment in the system, Figuroa said. You have frequent equipment breakdowns, you have a maintenance program that reacts to an outage and tries to fix it as quickly as possible instead of proactively identifying a weak point in the system and properly fixing it to reduce the chance of an outage over time.

Additionally, Figuroa said, the lack of investment by PREPA to maintain its distribution and transmission lines has made the system especially vulnerable to clashing with trees and other aspects of the tropical islands ecosystem.

If you have a wooden pole instead of an aluminum pole, when a hurricane comes, the pole is more likely to fall and therefore causes longer outages than if you had strengthened the system, he said.

Since LUMA took control in 2021, outages happen less often than under PREPA, but are longer by an average of 30 minutes, Figuroa pointed out, but with a caveat: The islands energy regulator concluded PREPA grossly underreported its numbers for 2019 and 2020.

A lot of people are frustrated because they perceive the service being delivered today has not improved, or in some cases they perceive that its getting worse. But a lot of that has to do with the amount of investment and work that needs to be put into fixing and improving one of the United States largest and most complex energy systems in existence, Figuroa said.

The process will unavoidably take time, he added, for customers to feel a meaningful improvement in the quality of services LUMA provides.

Varona of the Center for Popular Democracy said the organization believes LUMAs contract should be canceled, adding LUMA has not been an effective administrator of the grid.

The advocacy group has criticized the Financial Oversight and Management Board for using its power to impose devastating austerity measures and negotiate unsustainable debt restructuring plans that enrich Wall Street and hurt Puerto Ricans, said a report released last year.

There must be real investments in not only modernizing the grid, but decentralizing and replacing it with a renewable energy system, Varona added, echoing calls by activists and many residents to use FEMAs funds for a transition to solar power.

The Queremos Sol coalition has proposed a plan to be adopted by Puerto Ricos government to transform the grid with the goal of achieving 50% renewable energy generation by 2035 and 100% by 2050.

The proposal provides a pathway to a self-sufficient system relying on renewable resources, mainly solar, by using clean renewable technologies and inclusive structures and processes meant to eliminate partisan political interference and systemic corruption, it states.

We need to make sure that the dollars that were allocated to rebuild the electrical grid move faster so that we can rebuild the grid, Varona said. And hopefully when we do, we rebuild it through a decentralized system that relies more on the biggest source of power that Puerto Rico has: 365 days of sun.

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Getting Started with Pandas Cheatsheet – KDnuggets

October 1, 2022 | Author: admin

Pandas is one of, if not the, most widely-used and relied-upon libraries in the Python ecosystem. Pandas is often the first stop for data scientists for data processing, analysis, and manipulation.

Do you have tabular data you want to process? There is basically not way around using Pandas, and nor should you look for one. Pandas is rich in functionality, is incredibly powerful, and provides robust flexibility. Want to inspect data? Pandas can help. Need to query data? Pandas has you covered. Have to prepare tabular data for machine learning? Pandas is here for you.

KDnuggets' Abid Ali Awan further describes Pandas as follows:

Pandas is a flexible and easy-to-use tool for performing data analysis and data manipulation. It is widely used among data scientists for preparing data, cleaning data, and running data science experiments. Pandas is an open-source library that helps you solve complex statistical problems with simple and easy-to-use syntax.

Do you know how to leverage Pandas in your projects? You really should! There are plenty of resources to help with this, but getting right to work and dirtying your hands is always a great idea. But where do you turn for a quick reference?

To help, KDnuggets has put together this fantastic Pandas primer, which covers some of the important first steps in your Pandas journey.

The following quick reference cheatsheet guide will provide you with the basic Pandas operations needed to start querying and modifying DataFrames, the basic data structure of the library. It will show you how to create DataFrames, import and export data to and from them, inspect the DataFrames, as well subset, query, and reshape the DataFrames. Once you master these introductory operations, you should be ready for more advanced Pandas tasks.

Download the quick reference cheatsheet guide PDF here!

Learning Pandas is worth the effort. Beginners are often discouraged by the breadth of operations and the at-first intimidating syntax. But by taking it step by step, mastering the basics, and keeping a reference handy while you practice (like, say, this cheatsheet), you will be making progress with Python's most data processing ubiquitous library in no time.

Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.

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Ann Coulter’s One-of-a-Kind Take on the Midterms – The Clay Travis & Buck Sexton Show

October 1, 2022 | Author: admin

CLAY: Welcome back in Clay Travis, Buck Sexton show. We are here in New York. We are joined now by Ann Coulter, who was at Bucks engagement party last night. We were talking about how awesome it was. It was a good time, right?

COULTER: Yes. I want Buck to just get married every year.

CLAY: (laughing)

COULTER: We need more parties like that, Buck.

BUCK: We had fun putting it together but I gotta say when youve had a fun event afterwards one of the great things is you get to just chill for at bit.

CLAY: Yes.

COULTER: I walked in and ran into someone else moving to Florida who asked me, Whats it like down there? And I said, Ive been down there for a while, and my life has gotten a lot better since covid. Look around the room. One-third of the people here used to be New Yorkers and now live in Florida. (laughing)

BUCK: There was a very sizable contingent of New York to Florida corridor last night. And I think it was

CLAY: When did you go to Florida for the first time?

COULTER: Long time ago.

CLAY: So, you were way ahead of the curve, and now Florida, it feels like I saw a story something like 50,000 New Yorkers in the last year have relocated to Florida?

COULTER: New York City has lost billions of dollars in taxable income.

CLAY: No doubt.

COULTER: I wrote about it in my UK Spectator article. I used to say I write my books in Florida, but if I wanted to have fun go to L.A. and New York or import L.A. and New York to me. Nope! (laughing) Now youre all living there.

BUCK: Theres been a lot of migration.

COULTER: I wont have to, you know, make beds, clean the bathroom.

BUCK: So, were hoping youre able to give us some good news on the midterms and as youre seeing it right now. You are a prognosticator of these things. Most famously of course on the Bill March show, he gave you credit for, calling the 2016 election when everybody At first they booed and then they cheered when you came back whatever it was many months later for getting that one right. And were seeing right now Fetterman-Oz, Pennsylvania, this is one where I look at Clay, cause sometimes I have to I look at Clay for a sanity check. Im like, Am I crazy, or is it crazy that anybody could vote for this guy, John Fetterman?

COULTER: Yes. Yes. Oh, my gosh.

BUCK: The poll I think today was a two-point difference between the two.

COULTER: I say that all the time, who is voting for him? Its like Michael Moore is running.

BUCK: Yes.

COULTER: Hes slovenly. Hes always wearing I mean, this sounds like a minor thing. Im not even getting to his policies yet. He has tattoos, that ridiculous goatee. He looks like, you know, one of those Kanamits from the Twilight Zone, isnt that what they were called? Its a cookbooook! To Serve Man.

BUCK: (laughing) I dunno.

CLAY: If you were trying to design the worst possible statewide candidate I think you would pick John Fetterman. He is not well spoken because of the stroke, right, but even

COULTER: Yes. Even before that, yes.

CLAY: He said: Im a Bernie Sanders clone. And hes running.

COULTER: Bernie Sanders is more attractive I can understand Bernie Sanders. All he does, Fetterman, is suck up to criminals and release proved murderers, vicious murderers whove engaged in, you know, anti-Hispanic hate murderers and hes out campaigning with them and lying and saying, Oh, they were proved innocent. No, they werent.

BUCK: So, we have a theory that weve been talking about, Ann, with the crime issue which the polls are all showing this is a big thing and that fortunate the American people have seen the last few years. This is Democrats.

COULTER: Yes.

BUCK: This is their lunatic prosecutors, their lunatic state legislatures, the past whether its the no bail thing. Prosecutors are among the very worst its really not the cops although there are far fewer cops.

COULTER: Right. For good reason.

BUCK: The cops are still trying to do their jobs. Theres just a lot less of them because of Democrats.

COULTER: And they cant do anything. I mean, they arrest these people and then they get released. Thats why you have to vote for Zeldin.

BUCK: Yeah. This is on the prosecutor side.

COULTER: And anyone who lives in New York or could plausibly claim to live in New York, you have got to register to vote; you have got to vote. In New York City, people are talking about crime nonstop because criminals are really helping us out here cause theyre not slowing down. Every day. You talk to right wingers and theyre all, Oh, no, no, Republicans cant win. A Republican can win in New York state. Republicans, please vote. Zeldin and Oz are my two upset votes. Who knows right now. The polls are pretty close, I think, in both cases. Maybe the Democrat is ahead. But these are cases where I think the Republicans can stage an upset win.

CLAY: And what weve been talking about is, you look at Washington, I think Tiffany Smiley could eventually beat Patty Murray.

COULTER: Yeah. Yeah. Thats wild. Patty Murray is the stupidest senator.

BUCK: That is bold. That is a bold call. Mazie Hirono wants a word.

COULTER: I know. I know. Also, speaking of how Fetterman looks, look at Patty Murray. She looks like a homeless woman. Oh, no, she just hangs out in front of the library, doesnt really bother anyone. Collects bottles.

CLAY: Shes potentially gonna get beat. We got Christine Drazan, who we had on the show last week, I think, in Oregon may end up being the first Republican governor in Oregon. And then Colorados got some spice to it in terms of the governors race and the Senate race

BUCK: Making these Democrats uncomfortable.

CLAY: Thats right. And so, to your point, what we keep echoing and want to make sure everybody out there hear is sometimes its frustrating to lose all the time.

COULTER: Yes, yes.

New York gubernatorial candidate @LeeZeldin explains why some minority communities might lean toward voting Republican in upcoming elections.

Listen here for his full take: https://t.co/qgtPYBOQtc pic.twitter.com/RoNHVsXy45

The Clay Travis & Buck Sexton Show (@clayandbuck) July 2, 2022

CLAY: If youre in New York youve seen over those years Washington, Oregon, Colorado, a lot of losses stack up and eventually gets in your mind. You think, Oh, we cant win.

COULTER: Why bother? Ill just get on a jury list.

CLAY: Yeah.

COULTER: No. Bother this year. Please, please, please, Republicans.

CLAY: They can win in all those places.

BUCK: I think there could be, maybe there isnt, maybe Ill be wrong, but I think we all think there could be a secret anti-crime vote, as there was to get Giuliani in the first time. Giuliani was losing, according to the polls, but even liberals on the Upper West Side just couldnt take the crime anymore, and the crime is way worse now, and Im sure they told all their Upper West Side liberal friends, No, Im voting for Dinkins. Im voting for Dinkins, and then they get into the privacy of the voting booth and they voted for Giuliani and he saved New York City. The rest is history. So please, please. Oh, one point on which I think you will have to concede even with Mazie Hirono, Patty Murray being the stupidest United States senator, after the 9/11 attacks, she gave a speech in which she said, Well, you have to understand why Osama Bin Laden is so appealing to people. Hes out there building, you know, health care centers and day care centers. (laughing)

CLAY: I dont even remember that.

COULTER: For the people. You know, cause he was really into career women! Day care centers. Im not making that up. (laughing)

BUCK: Ann Coulter, by the way. Subscribe to her Substack. It is always illuminating and spicy.

CLAY: How dumb do you think Kathy Hochul is?

COULTER: Very, very, very dumb. Oh, and another point on Zeldin is he needs money, he needs money, he needs money. What is Hochuls advantage in right now shes the governor, and shes utterly at least from what Ive been told by many people, whats the word for it? Oh, corrupt. So, shes hitting up all the big donors in the corporations who have to do business with the state saying, Dont you dare give money to Zeldin.

CLAY: Yeah.

COULTER: So, even people who are probably voting for Zeldin wont give him money and, yeah, we all see the videos on Twitter, as horrible as Twitter is, God bless the internet where you can see these videos. No, he needs to go get that stuff up on TV. So, maybe some of you rich people in other states, instead of writing another check to Tim Scott could write a check to Lee Zeldin.

BUCK: We had the founder of @LibsOfTikTok on yesterday, by the way, speaking of the internet and showing things.

COULTER: Shes great.

BUCK: It is fascinating to watch how the Big Tech Democrat left has now gone to the point where, when you learn what they think, if its at the wrong time for them or in the wrong way, it is hate speech now.

COULTER: Yes.

BUCK: It is almost like saying, Oh, you guys wrote this manifesto? Were gonna pass it around. You cant pass around our manifesto! Thats all she really does.

COULTER: Yes. Yes. Yes. No. If only Trump had done something about it because all of his followers got thrown off.

CLAY: By the way, the Kanamits, thats a pretty good comparison to the Twilight Zone.

COULTER: No, I sent it out on my Substack.

CLAY: Yeah, Ali just texted it to us to make sure.

COULTER: Excellent. Than you, Ali.

CLAY: Shes been doing great research on the Fetterman comparison.

BUCK: What do you think? If Democrats who are still gonna vote for this guy

COULTER: Isnt that crazy how much he looks like that guy?

CLAY: It is pretty crazy, yes.

COULTER: And they were gigantic beings from some planet far away and they told the earthling were here to help you; so everybody, the spaceship

CLAY: He also has a certain Uncle Fester look to him.

COULTER: they translated the to serve man and right at the last minute they find out, Its a cookbook! So, anyway, hes they all look alike and they all look like Fetterman and theyre gigantic, like he is, this gigantic freak with tattoos and you know he wasnt paying taxes in the place where he was mayor.

BUCK: And he cant speak. You see the guy on stage. He goes, Here I am in Honolulu. Its like, No. Thats not right, and they dont care.

COULTER: Hes not that bad.

BUCK: Oh, hes pretty bad. Fetterman?

COULTER: Yeah, yeah, yeah. Ive seen him interviewed on MSNBC. So, dont lower expectations cause he can put a couple of sentences together. Look at his record. Look at how he dresses. He has to keep When he was lieutenant governor, he had to keep a suit in the office for when he had people who complained about it, cause all he wears is jeans and a sweatshirt. No, Im sorry, Bernie Sanders puts on a suit.

BUCK: Yeah, you mean, theyre button-down collars but he does wear collars.

COULTER: And hes not Mr. Release Criminals, Bernie Sanders. I dont know why Im defending Bernie Sanders. He had a lot of Trumpian policies. No, this is Michael Moore running, and I dont understand what is the constituency for that? Soccer moms?

CLAY: I think a lot of people have not paid attention, candidly, and theyre just now becoming aware. And I think Oz is gonna end up winning this race comfortably before all is said and done.

COULTER: Im also worried and I think you guys should get on one of the issues you are so good on. I think the media is gonna help Fetterman get out of a debate. Looks like thats gonna hurt him. I know theyre gonna do the same thing with Biden when they rerun Biden.

CLAY: Bidens not running.

BUCK: with me on this one, by the way, we had yesterday, Joe Concha, what if Bidens not running? Like, oh, Bidens running again.

COULTER: How can the media help them? Another covid scare. We cant, we cant. We have two of them. You said Trump would be the nominee. We have a steak dinner bet on that president, the other one Im not going to say cause Im betting against my interests, but its a Republican that I was betting is going to lose. Right now, hes up in the polls. Who knows what will happen, but I dont want to admit publicly that Im betting because Im not betting against him. Id love for him to win. Its just a bet. And its not Zeldin and its not Oz.

BUCK: Blake Masters?

COULTER: I hope so. I hope so. I love autistic candidates, and he is. He has an incredibly high IQ and he is like one of these tech guys like Peter Thiel is helping fund him, he just (laughing) I mean, the alleged like missteps he has, if you have any really high IQ autistic friends Like, oh, my gosh. Its the exact same thing. But I think he has one of the toughest races, Blake Masters in Arizona because hes running against Mark Kelly, who votes with Chuck Schumer, but he doesnt look like Chuck Schumer.

CLAY: He doesnt do or say anything.

COULTER: Were used to Democrats looking really hateable like Fetterman, and Kelly looks like hes normal. He isnt Arizona! (laughing)

BUCK: So Democrats basically gonna lie about what they really think and hide from the public until Election Day and theyre just hoping people dont realize this. That seems to be the strategy.

COULTER: Yes, especially when they rerun Biden. (laughing)

BUCK: Theyre rerunning Biden.

CLAY: Hes dropping out in the spring!

COULTER: I just posted a little Substack video, its only half an hour for people. I start off with why and how theyre going to do the Weekend at Bernies thing, which I will give you a short preview of.

CLAY: Its Weekend at Bernies Part II.COULTER: Google right now, This is the last time we vote for an all-white ticket. That was 2016 when black people said, Nope, were not. They were ticked off that both Hillary and Tim Kaine were white guys; so, once they get rid of Joe Biden, they are done with the white guys, the Democratic Party. I mean, blacks give the Democratic Party 90% of the vote. Thats never gonna change; so just give it up, Republicans. But thats why I think they have to do Weekend at Bernies, because what do they go to next? AOC? Ilhan Omar?

CLAY: Its gonna be Gavin Newsom.

COULTER: Theyre dumb. Theyre not that dumb.

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Ann Coulter's One-of-a-Kind Take on the Midterms - The Clay Travis & Buck Sexton Show

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AlphaGo Zero Explained In One Diagram | by David Foster – Medium

October 1, 2022 | Author: admin

The AlphaGo Zero Cheat Sheet (high-res link below)

Download the AlphaGo Zero cheat sheet

Recently Google DeepMind announced AlphaGo Zero an extraordinary achievement that has shown how it is possible to train an agent to a superhuman level in the highly complex and challenging domain of Go, tabula rasa that is, from a blank slate, with no human expert play used as training data.

It thrashed the previous reincarnation 1000, using only 4TPUs instead of 48TPUs and a single neural network instead of two.

The paper that the cheat sheet is based on was published in Nature and is available here. I highly recommend you read it, as it explains in detail how deep learning and Monte Carlo Tree Search are combined to produce a powerful reinforcement learning algorithm.

Hopefully you find the AlphaGo Zero cheat sheet useful let me know if you find any typos or have questions about anything in the document.

If you would like to learn more about how our company, Applied Data Science develops innovative data science solutions for businesses, feel free to get in touch through our website or directly through LinkedIn.

and if you like this, feel free to leave a few hearty claps 🙂

Applied Data Science is a London based consultancy that implements end-to-end data science solutions for businesses, delivering measurable value. If youre looking to do more with your data, lets talk.

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AlphaGo Zero Explained In One Diagram | by David Foster - Medium

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Master’s in Artificial Intelligence | Hopkins EP Online

October 1, 2022 | Author: admin

With the expertise of the Johns Hopkins Applied Physics Lab, weve developed one of the nations first online artificial intelligence masters programs to prepare engineers like you to take full advantage of opportunities in this field. The highly advanced curriculum is designed to deeply explore AI areas, including computer robotics, natural language processing, image processing, and more.

We have assembled a team of top-level researchers, scientists, and engineers to guide you through our rigorous online academic courses. Because we are a hub and frontrunner in artificial intelligence, we can tailor our artificial intelligence online masters content to include the most up-to-date practices and offer core courses that address the AI-driven technologies, techniques, and issues that power our modern world.

The online masters in Artificial Intelligence program balances theoretical concepts with the practical knowledge you can apply to real-world systems and processes. Courses deeply explore areas of AI, including robotics, natural language processing, image processing, and morefully online.

At the programs completion, you will:

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Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics – Government Accountability…

October 1, 2022 | Author: admin

What GAO Found

Several machine learning (ML) technologies are available in the U.S. to assist with the diagnostic process. The resulting benefits include earlier detection of diseases; more consistent analysis of medical data; and increased access to care, particularly for underserved populations. GAO identified a variety of ML-based technologies for five selected diseases certain cancers, diabetic retinopathy, Alzheimers disease, heart disease, and COVID-19 with most technologies relying on data from imaging such as x-rays or magnetic resonance imaging (MRI). However, these ML technologies have generally not been widely adopted.

Academic, government, and private sector researchers are working to expand the capabilities of ML-based medical diagnostic technologies. In addition, GAO identified three broader emerging approachesautonomous, adaptive, and consumer-oriented ML-diagnosticsthat can be applied to diagnose a variety of diseases. These advances could enhance medical professionals capabilities and improve patient treatments but also have certain limitations. For example, adaptive technologies may improve accuracy by incorporating additional data to update themselves, but automatic incorporation of low-quality data may lead to inconsistent or poorer algorithmic performance.

Spectrum of adaptive algorithms

We identified several challenges affecting the development and adoption of ML in medical diagnostics:

These challenges affect various stakeholders including technology developers, medical providers, and patients, and may slow the development and adoption of these technologies.

GAO developed three policy options that could help address these challenges or enhance the benefits of ML diagnostic technologies. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. See below for a summary of the policy options and relevant opportunities and considerations.

Policy Options to Help Address Challenges or Enhance Benefits of ML Diagnostic Technologies

Evaluation (reportpage 28)

Policymakers could create incentives, guidance, or policies to encourage or require the evaluation of ML diagnostic technologies across a range of deployment conditions and demographics representative of the intended use.

This policy option could help address the challenge of demonstrating real world performance.

Data Access (reportpage 29)

Policymakers could develop or expand access to high-quality medical data to develop and test ML medical diagnostic technologies. Examples include standards for collecting and sharing data, creating data commons, or using incentives to encourage data sharing.

This policy option could help address the challenge of demonstrating real world performance.

Collaboration (reportpage 30)

Policymakers could promote collaboration among developers, providers, and regulators in the development and adoption of ML diagnostic technologies. For example, policymakers could convene multidisciplinary experts together in the design and development of these technologies through workshops and conferences.

This policy option could help address the challenges of meeting medical needs and addressing regulatory gaps.

Source: GAO. | GAO-22-104629

Diagnostic errors affect more than 12 million Americans each year, with aggregate costs likely in excess of $100 billion, according to a report by the Society to Improve Diagnosis in Medicine. ML, a subfield of artificial intelligence, has emerged as a powerful tool for solving complex problems in diverse domains, including medical diagnostics. However, challenges to the development and use of machine learning technologies in medical diagnostics raise technological, economic, and regulatory questions.

GAO was asked to conduct a technology assessment on the current and emerging uses of machine learning in medical diagnostics, as well as the challenges and policy implications of these technologies. This report discusses (1) currently available ML medical diagnostic technologies for five selected diseases, (2) emerging ML medical diagnostic technologies, (3) challenges affecting the development and adoption of ML technologies for medical diagnosis, and (4) policy options to help address these challenges.

GAO assessed available and emerging ML technologies; interviewed stakeholders from government, industry, and academia; convened a meeting of experts in collaboration with the National Academy of Medicine; and reviewed reports and scientific literature. GAO is identifying policy options in this report.

For more information, contact Karen L. Howard at (202) 512-6888 or howardk@gao.gov.

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Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics - Government Accountability...

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Reasons behind the Limited Use of Artificial Intelligence in Latin American Media – CIOReview

October 1, 2022 | Author: admin

Journalists are automating processes and using systems that mimic human behavior to design tasks related to news gathering, content creation, distribution, marketing, and subscriptions in Latin America.

FREMONT, CA: Artificial intelligence (AI) has stopped merely being a science fiction component and has become a reality in recent years. Automation of processes and the creation of systems that mimic human behavior have reached journalism and are being used to design tasks of news gathering, content creation, distribution, marketing, and subscriptions. Automating processes and creating systems that mimic human behavior have reached journalism.

The use of AI is currently somewhat limited, despite its enormous potential, and the region is ravenous for information regarding the subject.

Insufficient funds and a need for training

The organization needs AI training in addition to producing articles for publication in Latin American media.

Ironically, although there is a great deal of understanding of the potential benefits of AI systems in the media, very little or even nothing has been done at the organizational level to implement these systems.

There is an unmistakable desire present in newsrooms to implement AI. The majority of the newsrooms had not taken advantage of the chance to implement AI into their day-to-day operations, despite its availability. The typical reaction was either a lack of resources or an absence of a corporate vision to adopt AI technologies as an integral part of the organization's future. Both of these issues were cited as the reason for the failure.

Similarly, the request made by journalists was to obtain training and trade experiences with other forms of media. The region is willing to enter the field of AI, but it needs basic information about the currently available prospects.

User loyalty

It would appear that the holy grail of reader monetization is the application of algorithms to make sense of data. Many news organizations in Latin America are either actively investigating this topic or are eager to do so. Particularly in the case of television networks, which are not accustomed to working with this kind of instrument.

The media are utilizing machine learning and AI to gain a deeper understanding of specific consumer habits, user loyalty, and the integration of advertising campaigns.

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Reasons behind the Limited Use of Artificial Intelligence in Latin American Media - CIOReview

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Managing the risks of inevitably biased visual artificial intelligence systems – Brookings Institution

October 1, 2022 | Author: admin

Scientists have long been developing machines that attempt to imitate the human brain. Just as humans are exposed to systemic injustices, machines learn human-like stereotypes and cultural norms from sociocultural data, acquiring biases and associations in the process. Our research shows that bias is not only reflected in the patterns of language, but also in the image datasets used to train computer vision models. As a result, widely used computer vision models such as iGPT and DALL-E 2 generate new explicit and implicit characterizations and stereotypes that perpetuate existing biases about social groups, which further shape human cognition.

Such computer vision models are used in downstream applications for security, surveillance, job candidate assessment, border control, and information retrieval. Implicit biases also manifest in the decision-making processes of machines, creating lasting impacts on peoples dignity and opportunities. Moreover, nefarious actors may use readily available pre-trained models to impersonate public figures, blackmail, deceive, plagiarize, cause cognitive distortion, and sway public opinion. Such machine-generated data pose a significant threat to information integrity in the public sphere. Even though machines have been rapidly advancing and can offer some opportunities for public interest use, their application in societal contexts without proper regulation, scientific understanding, and public awareness of their safety and societal implications raises serious ethical concerns.

A worthy example for exploring such biases appear in biased gender associations. To understand how gender associations manifest in downstream tasks, we prompted iGPT to complete an image given a womans face. iGPT is a self-supervised model trained on a large set of images to predict the next pixel value, allowing for image generation. Fifty-two percent of the autocompleted images had bikinis or low-cut tops. In comparison, faces of men were autocompleted with suits or career-related attire 42 percent of the time. Only seven percent of male autocompleted images featured revealing clothing. To provide a comprehensive analysis of bias in self-supervised computer vision models, we also developed the image embedding association test to quantify the implicit associations of the model that might lead to biased outcomes. Our findings reveal that the model contains innocuous associations such as flowers and musical instruments being more pleasant than insects and weapons. However, the model also embeds biased and potentially harmful social group associations related to age, gender, body weight, and race or ethnicity. The biases at the intersection of race and gender are aligned with theories on intersectionality, reflecting emergent biases not explained by the sum of biases towards either race or gender identity alone.

The perpetuation of biases that have been maintained through structural and historical inequalities by these models has significant societal implications. For example, biased job candidate assessment tools perpetuate discrimination among members of historically disadvantaged groups and predetermine the applicants economic opportunities. When the administration of justice and policing relies on models that associate certain skin tones, races or ethnicities with negative valence, people of color wrongfully suffer the life-changing consequences. When computer vision applications directly or indirectly process information related to protected attributes, they contribute to said biases, exacerbating the problem by creating a vicious bias cycle, which will continue unless technical, social, and policy-level bias mitigation strategies are implemented.

State-of-the-art pre-trained computer vision models like iGPT are incorporated into consequential decision-making in complex artificial intelligence (AI) systems. Recent advances in multi-modal AI effectively combine language and vision models. The integration of various modalities in an AI system further complicates the safety implications of cutting-edge technology. Although pre-trained AI is highly costly to build and operate, models made available to the public are freely deployed in commercial and critical decision-making settings and facilitate decisions made in well-regulated domains, such as the administration of justice, education, the workforce, and healthcare. However, due to the proprietary nature of commercial AI systems and lack of regulatory oversight of AI and data, no standardized transparency mechanism exists, which officially documents when, where, and how AI is deployed. Consequently, the unintentional harmful side effects of AI live on long after their originators have been updated or deleted.

Establishing unacceptable uses of AI, requiring extra checks and safety for high-risk products (such as those in the European Unions draft Artificial Intelligence Act), and standardizing the model improvement process for each modality and multi-modal combination to issue safety updates and recalls are all promising approaches to tackle some of the challenges that might lead to irreparable harm. Standards can also help guide developers. For example, the National Institute of Science and Technology (NIST) released the special publication Towards a Standard for Identifying and Managing Bias in Artificial Intelligence in 2022 and a draft AI Risk Management Framework summarizing many of these risks and suggesting standards for trustworthiness, fairness, accountability, and transparency.

Third-party audits and impact assessments could also play a major role in holding deployers accountablefor example, a House bill in subcommittee (the Algorithmic Accountability Act of 2022) requires impact assessments of automated decision systemsbut third-party audits with a real expectation of accountability are rare. The bottom line is that researchers in AI ethics have called for public audits, harm incident reporting systems, stakeholder involvement in system development, and notice to individuals when they are subject to automated decision-making.

Regulating bias and discrimination in the U.S. has been an ongoing effort for decades. Policy-level bias mitigation strategies have been effectively but slowly reducing bias in the system, and consequently in humans minds. Both humans and vision systems inevitably learn bias from the large-scale sociocultural data they are exposed toso future efforts to improve equity and redress historical injustice will depend on increasingly influential AI systems. Developing bias measurement and analysis methods for AI, trained on sociocultural data, would shed light into the biases in social and automated processes. Accordingly, actionable strategies can be developed by better understanding the evolution and characteristics of bias. Although some vision applications can be used for good (for example, assistive and accessibility technologies designed to aid individuals with disabilities), we have to be cautious about the known and foreseeable risks of AI.

As scientists and researchers continue developing methods and appropriate metrics to analyze AIs risks and benefits, collaborations with policymakers and federal agencies inform evidence-driven AI policymaking. Introducing the required standards for trustworthy AI would affect how the industry implements and deploys AI systems. Meanwhile, communicating the properties and impact of AI to direct and indirect stakeholders will raise awareness on how AI affects every aspect of our lives, society, world, and the law. Preventing a techno-dystopian reality requires managing the risks of this sociotechnical problem through ethical, scientific, humanistic, and regulatory approaches.

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