#data processing

[stone-cold-groove]

60s era Sperry Rand UNIVAC nameplate.

[mudwerks]

Victoria Composite High School vocational classes: Data Processing, Edmonton, Alberta, 1966

[vavuska]

Seems that EA is now allegedly accused of a mayor privacy violation, having used tracking tools on The Sims FreePlay app to secretly gather and transmit players’ personal information to Facebook for advertising purposes. This data potentially includes unique Facebook IDs, which can be used to match players’ in-game activities to their individual Facebook profiles. Attorneys suspect that these potential data-sharing practices may violate a federal privacy law and are now gathering players to take action.

So, there are at least two class action against EA, because it seems to collect data from players using the Meta Pixel software to harness data from players and sell it to the Meta company, who owns Instagram, Facebook and other social networks.

It would be interesting to learn if this allegations are true and how this would be seen in the eyes of GDPR, European Regulation 679/2016, which allows the processing of personal data only with consent given by the data subjects and also in the context of (online) games.

Consent in the context of the GDPR must be understood as an unambiguous indication of an informed and freely given choice by the data subject, relating to specific processing activities. The burden of proof that these criteria are fulfilled falls upon the controller (i.e., the game developer).

Google Play list the privacy condition of EA for its games, including The Sims Freeplay. Basically EA claims to use players data only to give them "better game experience", which seems vague but not less legit. The only less transparent thing I noticed is that the instructions to opt out of targeted marketing of in-game ads are in English and not in Italian: downloading the game, players allows EA to share their account information with third-party partners to customize advertising experience, which is basically all app developers do, but it's weird that the instruction to opt out doesn't have been translated at all!

This is not the first time EA is accused of, well, unethical commercial practice, since EA has been sentenced to pay fines by Austrian (2023) and Belgian (2018) civil court, because their FIFA loot boxes violated local gambling laws.

Moreover, it's important to notice that in January 2023, the European Parliament adopted a report calling for harmonized EU rules to achieve better player protection in the online video game sector.

The Parliament called for greater transparency from developers about in-game purchases: player should be aware of the type of content before starting to play and during the game. Also, players should be informed on the probabilities in loot box mechanisms, including information in plain language about what algorithms are devised to achieve.

The Parliament further stressed that the proposed legislation should assess whether an obligation to disable in-game payments and loot boxes mechanisms by default or a ban on paid loot boxes should be proposed to protect minors, avoid the fragmentation of the single market and ensure that consumers benefit from the same level of protection, no matter of their place of residence.

The Parliament highlighted problematic practices, including exploiting cognitive biases and vulnerabilities of consumers through deceptive design and marketing, using layers of virtual currencies to mask/distort real-world monetary costs, and targeting loot boxes and manipulative practices towards minors.

[mister-forest]

Chiques fíjense de activar la opción de no compartir datos en el apartado "Visibilidad" en Ajustes ‼️‼️

[conclave-030]

The Ultimate Data Collection Handbook: Exploring Methods, Types, and Advantages

Data collection is a fundamental part of any research, business strategy, or decision-making process. Whether you're a student, a professional, or just curious about how data is gathered and used, understanding the basics of data collection can be incredibly useful. In this guide, we'll explore the methods, types, and benefits of data collection in a way that’s easy to understand.

What is Data Collection?

Data collection is the process of gathering information to answer specific questions or to support decision-making. This information, or data, can come from various sources and can be used to make informed decisions, conduct research, or solve problems.

Methods of Data Collection

Surveys and Questionnaires

What Are They? Surveys and questionnaires are tools used to gather information from people. They can be distributed in person, by mail, or online.

How Do They Work? Respondents answer a series of questions that provide insights into their opinions, behaviors, or experiences.

When to Use Them? Use surveys and questionnaires when you need to gather opinions or experiences from a large group of people.

Interviews

What Are They? Interviews involve asking questions to individuals in a one-on-one setting or in a group discussion.

How Do They Work? The interviewer asks questions and records the responses, which can be either structured (with set questions) or unstructured (more conversational).

When to Use Them? Use interviews when you need detailed, qualitative insights or when you want to explore a topic in depth.

Observations

What Are They? Observations involve watching and recording behaviors or events as they happen.

How Do They Work? The observer notes what is happening without interfering or influencing the situation.

When to Use Them? Use observations when you need to see actual behavior or events in their natural setting.

Experiments

What Are They? Experiments involve manipulating variables to see how changes affect outcomes.

How Do They Work? Researchers control certain variables and observe the effects on other variables to establish cause-and-effect relationships.

When to Use Them? Use experiments when you need to test hypotheses and understand the relationships between variables.

Secondary Data Analysis

What Is It? This method involves analyzing data that has already been collected by someone else.

How Does It Work? Researchers use existing data from sources like government reports, research studies, or company records.

When to Use It? Use secondary data analysis when you need historical data or when primary data collection is not feasible.

Types of Data

Quantitative Data

What Is It? Quantitative data is numerical and can be measured or counted.

Examples: Age, income, number of products sold.

Use It When: You need to quantify information and perform statistical analysis.

Qualitative Data

What Is It? Qualitative data is descriptive and involves characteristics that can be observed but not measured numerically.

Examples: Customer feedback, interview responses, descriptions of behavior.

Use It When: You need to understand concepts, opinions, or experiences.

Benefits of Data Collection

Informed Decision-Making

Data provides insights that help individuals and organizations make informed decisions based on evidence rather than guesswork.

Identifying Trends and Patterns

Collecting data allows you to identify trends and patterns that can inform future actions or strategies.

Improving Services and Products

By understanding customer needs and preferences through data, businesses can improve their products and services to better meet those needs.

Supporting Research and Development

Data is crucial for researchers to test hypotheses, validate theories, and advance knowledge in various fields.

Enhancing Efficiency

Data helps in streamlining processes and improving operational efficiency by highlighting areas that need attention or improvement.

Conclusion

Understanding the methods, types, and benefits of data collection can greatly enhance your ability to gather useful information and make informed decisions. Whether you're conducting research, running a business, or just curious about the world around you, mastering data collection is a valuable skill. Use this guide to get started and explore the many ways data can help you achieve your goals.

To know more: A Guide to Data Collection: Methods, Types, and Benefits

Outsource Data Collection Services

[jcmarchi]

Transforming AML: Exploding the potential of AI solutions

New Post has been published on https://thedigitalinsider.com/transforming-aml-exploding-the-potential-of-ai-solutions/

Transforming AML: Exploding the potential of AI solutions

Do you need a captivating method of presenting your anti-money laundering (AML) system to clients? As a result of the increasing number of online transactions, fraudsters, and money launderers have had their work made easier.

An AML AI solution is a powerful tool that can help you fend off these threats and protect your financial environment. A sentiment not far off from what President Abraham Lincoln once noted about the future saying, “The best way to predict the future is to create it.” Nowadays, AI systems are leading by example and improving the process of combating money laundering.

AI technology used in AML systems provides distinctive features essential to AML compliance. It speeds up transaction monitoring, enhances it, and helps institutions beat offenders while keeping the law on their side. This paper discusses how AML AI solutions can change the economic crime-fighting landscape in this article.

1. Facilitated efficiency of transaction monitoring

An AML AI solution boosts transaction monitoring by reducing errors by an average of 90%. Modern AI systems work in real-time processing data significantly faster than employing ordinary methods.

They outperform other traditional methods in pattern recognition and identification of suspicious activities, and greatly reduce the number of false alarms. For instance, false positives within AI web services have decreased by up to 80% recently rendering significant time and resources to financial institutions.

This means that AML systems are able to invest their efforts in any suspicions or alerts that may be genuine. This precision helps in making faster and more accurate responses to financial crimes.

Artificial intelligence is a major asset in contemporary AML initiatives due to its capability to provide great results. In addition, financial organizations that have chosen to implement AI-based AML platforms have delivered improved performance, indicating that money, time, and resources can be better managed.

2. Compliance to set laws and regulations

It is not easy to work through and overcome various moving hurdles of regulations that exist in life. Anticipating the trend in law-making and change, AI solutions in AML bring to the table a flexible tool in software. One study revealed that 70% of financial institutions raised compliance levels by implementing AI-assisted AML systems.

These systems provide enhanced reporting invention and dramatically minimize human error in compliance tasks. Therefore, with improved AML undertakings through the integration of artificial intelligence, institutions stand protected from penalties and also reputational loss, thus making the financial world secure.

Also, these systems are capable of offering extensive records, which run a very crucial part during reviews made by a certain regulator.

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3. Early estimation of hyped operations

Besides, timely identification of suspicious activities is possible only if it is fast. AI solutions are recognized with real-time data processing, allowing institutions to identify fraud as it unfolds. Such a rapid response helps to prevent losses and build up the necessary protections against money laundering.

Even more importantly, AI goes beyond merely reporting strong outliers; it identifies trends that an analyst might overlook. These systems, using machine learning algorithms, are incredibly accurate and are of high value in combating financial crimes. Indeed, being proactive rather than reactive puts financial institutions in a better place regarding counteracting emerging evils.

4. Refrain from interference with human activity in the analysis

Traditional AML systems suffer from human error that results in such scenarios as omission of threats or providing false positives. Intelligent AML solutions drive down the level of manual processing, minimizing the chances of errors.

Large data sets can be examined by machine learning software in mere seconds, and potential threats are detected with great accuracy. For example, artificial intelligence-based AML systems caused a false positive reduction by the end of 2023 by 50%.

Such systems offer optimization and decrease the possibility of critical failures by simplifying certain essential tasks. On similar grounds, institutions can also scale down the pressure being applied to their AML compliance teams so that they can engage in more core activities.

5. The last control on the cost efficiency of AML management

Organization of an efficient AML system is costly; however, implementation of AI solutions significantly lowers these costs. With the retention of data for analysis and constant monitoring of transactions, these systems greatly reduced the amount of time and resources required in the compliance and fraud detection processes.

The use of AI in AML systems now allows for tasks to be solved in minutes that previously took hours, given the multiple resources and operational costs saved. The efficiency managers of the financial institutions get to cut down a huge cost while at the same time, they are easily able to cope with the requirements of tackling money laundering cases.

According to received data, AI minimizes the number of false positives by 70%, and it significantly decreases the cost for firms all around the world. Furthermore, these efficiencies enable small institutions to work state-of-the-art AML measures that were previously out of their reach.

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6. Better data processing attributes

Contemporary AML systems must analyze vast volumes of information when seeking to detect economic offenses. Traditionally, it may be challenging to deal with such huge sets of data and avoid essential details; with the help of AI solutions, dealing with these datasets is not a problem.

Their advanced algorithms analyze obscure relationships and certain patterns with a great level of speed.

The capability of analyzing and handling data makes AI laudable in helping institutions detect risks and alert institutions to suspicious activities. Another finding is that AI solutions have brought down false positives by 50 percent and enhanced detection systems and effectiveness.

Besides, these systems can be extended by connecting other technologies like blockchain for more detailed monitoring.

7. Learning or education as multi-dimensional and interconnected

They are an AI environment for compliance management that evolves constantly and grows as a living organism. Such systems learn from large amounts of data, and, by identifying trends in this data, enhance over time. This keeps several institutions informed of the latest gimmicks by fraudsters alike who are ever-evolving new strategies.

Through machine learning, AI transforms its operation and effectiveness in that it prevents and prosecutes economic crimes. Through effective ways to change its tune and respond effectively to new threats, AI guarantees that financial institutions hold efficient and aggressive stands against money laundering and fraud.

Additionally, the institutions that use this capability can also gain the capacity for predicting future risks as well as designing preventive measures to augment the firm’s security systems.

8. Better customer trust and company image

The hostile application of advanced AML AI solutions doesn’t bring benefits to the internal processes only it also improves customers’ confidence and brand image. Consumers prefer to work with financial firms that present sound practices concerning fraud and related compliance measures.

Through preventing scenarios related to fraud as well as guaranteeing smooth compliance institutions render themselves reputable and safe for use according to the views/interests of their clients. This increased level of trust can give the current customer better loyalty and execute competition edge inside the market.

Bonus: Discovering AI-based AML systems

If you are ready for a change in how your business approaches and implements your AML systems, then we can help. Organizations are welcome to visit our website to learn about new AI solutions tailored to their requirements. Enable your institution to strengthen its compliance, contain costs, and predict and prevent financial crimes.

Conclusion

AI technology is making tremendous impacts in the Anti Money Laundering processes as we speak, providing faster and more accurate solutions. Basically, AI integrated AML systems provide unprecedented advantages ranging anything from more efficient monitoring of transactions to sound compliance.

These solutions make the financial world safer by minimizing human mistakes, decreasing expenses, and adapting to new problems. People engaged in digital transactions now make it mandatory to use AI to support AML endeavors.

AI AML systems today not only enable institutions to shield themselves and consumers from financial crimes, but it also puts them ahead for the future of safe financial operations. So why not take the step to embrace innovation and strengthen your firewalls—that is, your clients and stakeholders—will surely be grateful.

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

The technology industry has seen significant advancements over the past few decades, but nothing quite as transformative as quantum computing promises to be. Why Quantum Computing Will Change the Tech Landscape is not just a matter of speculation; it’s grounded in the science of how we compute and the immense potential of quantum mechanics to revolutionise various sectors. As traditional…

[d0nutzgg]

Speechy Research Devlog: Some New Tools & New Discoveries

Hey everyone, so it is about 8:30pm and I am sure that by the time I write this it will be nearly 9 but I wanted to update everyone who is following my Speechy research on here. I programmed 2 new programs today, a Prosodic Pitch Analyzer (PPA), and an RMS Energy Analyzer using my handy-dandy new favorite library librosa.

Prosodic Pitch Analyzer

The PPA calculates the fundamental frequency (F0) or pitch of an audio signal and visualizes it using a line plot. This is a useful tool for analyzing prosodic features of speech such as intonation, stress, and emphasis.

The code takes an audio file as input, processes it using the librosa library to extract the fundamental frequency / pitch, and then plots the pitch contour using matplotlib.

The output plot shows the pitch contour of the audio signal over time, with changes in pitch represented by changes in the vertical position of the line. The plot can be used to identify patterns in the pitch contour, such as rising or falling intonation, and to compare the pitch contour of different audio signals. The prosodic pitch analyzer can be used to detect changes in pitch, which can be indicative of a neurological speech disorder. For example, a person with ataxic dysarthria, which is caused by damage to the cerebellum, may have difficulty controlling the pitch and loudness of their voice, resulting in variations in pitch that are not typical of normal speech. By analyzing changes in pitch using a tool like the prosodic pitch analyzer, it is possible to identify patterns that are indicative of certain neurological disorders. This information can be used by clinicians to diagnose and treat speech disorders, and to monitor progress in speech therapy.

RMS Energy Analyzer

The program that calculates the energy of a person's speech processes an audio file and calculates the energy of the signal at each time frame. This can be useful for analyzing changes in a person's speech over time, as well as for detecting changes in the intensity or loudness of the speech.

The program uses the librosa library to load and process the audio file, and then calculates the energy of each frame using the root-mean-square (RMS) energy of the signal. The energy values are then plotted over time using the matplotlib library, allowing you to visualize changes in the energy of the speech.

By analyzing changes in energy over time, you can gain insight into how the speech patterns of people with these disorders may differ from those without.

Analysis with PPA

The research that I've been focused on today primarily looked at the speech recording of myself, the mid-stage HD patient with chorea, the late-stage HD patient (EOL), and a young girl with aphasia.

The patient with aphasia had slurred speech and varied rising and falling much like an AD patient. Earlier I saw her ROS and was surprised at the differences between my rate of speech and hers (aphasia v AD)

My rate of speech

The girl with aphasia's rate of speech

So I decided to compare our speech pitches as well and this is what ours looked like side-by-side.

Hers is on the left, mine on the right.

Her pitch seemed to start off higher (unstable though) like mine, but mine fell during my recording and wobbled for a while. She had some drastic pitch differences but mine had around 16 peaks, where hers had around 18-19. Her latter peaks weren't as high frequency as mine, as my frequency peaks ended up mostly very high in the 1600hz or around 1000hz. There is quite a bit of instability in both our pitches though.

Her energy levels in the 15 seconds of speech started off at high-mid energy, then dropped around 1 second in until almost 3 seconds, shot back up and varied in high, high-mid energy, then had several "dips, and higher moments of energy. At the end around 13 seconds she got a huge boost of "gusto" (well.. energy). She had around 7 breaths (noted by the dips / flatlines)

This was mine. It seems like as the 15 seconds went on I started to run out of steam. I wasn't able to keep my energy higher. Mine had around 11 breaths so I was running out of breath eg having a breathier voice more than she was.

Research Conclusion for Today

Although we have quite a bit in common with our speech energy and pitches, our rate of speaking isn't. She used more syllables at a constant rate which made it pretty obvious she had a lot of slurring / overshooting, mine was a lot less syllables and rate of speech was quite slow and varied more than hers. This illustrates my cognitive difficulties and use of placeholder words along with slight slurring.

As far as pitch, seems that we had similar issues with pitch throughout the 15 second clips, mine spiked in the latter when I was getting "wore out" and hers spiked earlier when she had more energy.

Our energy levels differ because although she had moments of energy, I tuckered out pretty quickly.

I hope this helps shed some insight into both aphasia patients and ataxic dysarthria / HD patients speech / some cognitive differences.

Will update again tomorrow when I am done with another day of programming and research!

[redbixbite-solutions]

Everything You Need to Know About Machine Learning

Ready to step into the world of possibilities with machine learning? Learn all about machine learning and its cutting-edge technology. From what do you need to learn before using it to where it is applicable and their types, join us as we reveal the secrets. Read along for everything you need to know about Machine Learning!

What is Machine Learning?

Machine Learning is a field of study within artificial intelligence (AI) that concentrates on creating algorithms and models which enable computers to learn from data and make predictions or decisions without being explicitly programmed. The process involves training a computer system using copious amounts of data to identify patterns, extract valuable information, and make precise predictions or decisions.

Fundamentally, machine Learning relies on statistical techniques and algorithms to analyze data and discover patterns or connections. These algorithms utilize mathematical models to process and interpret data. Revealing significant insights that can be utilized across various applications by different AI ML services.

What do you need to know for Machine Learning?

You can explore the exciting world of machine learning without being an expert mathematician or computer scientist. However, a  basic understanding of statistics, programming, and data manipulation will benefit you. Machine learning involves exploring patterns in data, making predictions, and automating tasks.

 It has the potential to revolutionize industries. Moreover, it can improve healthcare and enhance our daily lives. Whether you are a beginner or a seasoned professional embracing machine learning can unlock numerous opportunities and empower you to solve complex problems with intelligent algorithms.

Types of Machine Learning

Let’s learn all about machine learning and know about its types.

Supervised Learning

Supervise­d learning resemble­s having a wise mentor guiding you eve­ry step of the way. In this approach, a machine le­arning model is trained using labele­d data wherein the de­sired outcome is already known.

The­ model gains knowledge from the­se provided example­s and can accurately predict or classify new, unse­en data. It serves as a highly e­ffective tool for tasks such as dete­cting spam, analyzing sentiment, and recognizing image­s.

Unsupervised Learning

In the re­alm of unsupervised learning, machine­s are granted the autonomy to e­xplore and unveil patterns inde­pendently. This methodology mainly ope­rates with unlabeled data, whe­re models strive to une­arth concealed structures or re­lationships within the information.

It can be likene­d to solving a puzzle without prior knowledge of what the­ final image should depict. Unsupervise­d learning finds frequent application in dive­rse areas such as clustering, anomaly de­tection, and recommendation syste­ms.

Reinforcement Learning

Reinforce­ment learning draws inspiration from the way humans le­arn through trial and error. In this approach, a machine learning mode­l interacts with an environment and acquire­s knowledge to make de­cisions based on positive or negative­ feedback, refe­rred to as rewards.

It's akin to teaching a dog ne­w tricks by rewarding good behavior. Reinforce­ment learning finds exte­nsive applications in areas such as robotics, game playing, and autonomous ve­hicles.

Machine Learning Process

Now that the diffe­rent types of machine le­arning have been e­xplained, we can delve­ into understanding the encompassing proce­ss involved.

To begin with, one­ must gather and prepare the­ appropriate data. High-quality data is the foundation of any triumph in a machine le­arning project.

Afterward, one­ should proceed by sele­cting an appropriate algorithm or model that aligns with their spe­cific task and data type. It is worth noting that the market offe­rs a myriad of algorithms, each possessing unique stre­ngths and weaknesses.

Next, the machine goes through the training phase. The model learns from making adjustments to its internal parameters and labeled data. This helps in minimizing errors and improves its accuracy.

Evaluation of the machine’s performance is a significant step. It helps assess machines' ability to generalize new and unforeseen data. Different types of metrics are used for the assessment. It includes measuring accuracy, recall, precision, and other performance indicators.

The last step is to test the machine for real word scenario predictions and decision-making. This is where we get the result of our investment. It helps automate the process, make accurate forecasts, and offer valuable insights. Using the same way. RedBixbite offers solutions like DOCBrains, Orionzi, SmileeBrains, and E-Governance for industries like agriculture, manufacturing, banking and finance, healthcare, public sector and government, travel transportation and logistics, and retail and consumer goods.

Applications of Machine Learning

Do you want to know all about machine learning? Then you should know where it is applicable.

Natural Language Processing (NLP)- One are­a where machine le­arning significantly impacts is Natural Language Processing (NLP). It enables various applications like­ language translation, sentiment analysis, chatbots, and voice­ assistants. Using the prowess of machine le­arning, NLP systems can continuously learn and adapt to enhance­ their understanding of human language ove­r time.

Computer Vision- Computer Vision pre­sents an intriguing application of machine learning. It involve­s training computers to interpret and compre­hend visual information, encompassing images and vide­os. By utilizing machine learning algorithms, computers gain the­ capability to identify objects, faces, and ge­stures, resulting in the de­velopment of applications like facial re­cognition, object detection, and autonomous ve­hicles.

Recommendation Systems- Recomme­ndation systems have become­ an essential part of our eve­ryday lives, with machine learning playing a crucial role­ in their developme­nt. These systems care­fully analyze user prefe­rences, behaviors, and patte­rns to offer personalized re­commendations spanning various domains like movies, music, e­-commerce products, and news article­s.

Fraud Detection- Fraud dete­ction poses a critical concern for businesse­s. In this realm, machine learning has e­merged as a game-change­r. By meticulously analyzing vast amounts of data and swiftly detecting anomalie­s, machine learning models can ide­ntify fraudulent activities in real-time­.

Healthcare- Machine learning has also made great progress in the healthcare sector. It has helped doctors and healthcare professionals make precise and timely decisions by diagnosing diseases and predicting patient outcomes. Through the analysis of patient data, machine learning algorithms can detect patterns and anticipate possible health risks, ultimately resulting in early interventions and enhanced patient care.

In today's fast-paced te­chnological landscape, the field of artificial inte­lligence (AI) has eme­rged as a groundbreaking force, re­volutionizing various industries. As a specialized AI de­velopment company, our expe­rtise lies in machine le­arning—a subset of AI that entails creating syste­ms capable of learning and making predictions or de­cisions without explicit programming.

Machine learning's wide­spread applications across multiple domains have transforme­d businesses' operations and significantly e­nhanced overall efficie­ncy.

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[stone-cold-groove]

A page from Sperry UNIVAC’s computer brochure - 1976.

[pythonprogrammingsnippets]

python matching with ngrams

# https://pythonprogrammingsnippets.com def get_ngrams(text, n): # split text into n-grams. ngrams = [] for i in range(len(text)-n+1): ngrams.append(text[i:i+n]) return ngrams def compare_strings_ngram_pct(string1, string2, n): # compare two strings based on the percentage of matching n-grams # Split strings into n-grams string1_ngrams = get_ngrams(string1, n) string2_ngrams = get_ngrams(string2, n) # Find the number of matching n-grams matching_ngrams = set(string1_ngrams) & set(string2_ngrams) # Calculate the percentage match percentage_match = (len(matching_ngrams) / len(string1_ngrams)) * 100 return percentage_match def compare_strings_ngram_max_size(string1, string2): # compare two strings based on the maximum matching n-gram size # Split strings into n-grams of varying lengths n = min(len(string1), len(string2)) for i in range(n, 0, -1): string1_ngrams = set(get_ngrams(string1, i)) string2_ngrams = set(get_ngrams(string2, i)) # Find the number of matching n-grams matching_ngrams = string1_ngrams & string2_ngrams if len(matching_ngrams) > 0: # Return the maximum matching n-gram size and break out of the loop return i # If no matching n-grams are found, return 0 return 0 string1 = "hello world" string2 = "hello there" n = 2 # n-gram size # find how much of string 2 matches string 1 based on n-grams percentage_match = compare_strings_ngram_pct(string1, string2, n) print(f"The percentage match is: {percentage_match}%") # find maximum ngram size of matching ngrams max_match_size = compare_strings_ngram_max_size(string1, string2) print(f"The maximum matching n-gram size is: {max_match_size}")

[goodoldbandit]

How to Use Telemetry Pipelines to Maintain Application Performance.

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in Optimize application performance with telemetry pipelines—enhance observability, reduce costs, and ensure security with efficient data processing. 🚀 Discover how telemetry pipelines optimize application performance by streamlining observability, enhancing security, and reducing costs. Learn key strategies and best…

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Discover the key aspects of UAE’s Personal Data Protection Law (PDPL) – scope, individual rights, business obligations, data breach reporting, and penalties. Stay compliant with UAE’s data privacy regulations.

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[jcmarchi]

Non-fiction books that explore AI's impact on society  - AI News

New Post has been published on https://thedigitalinsider.com/non-fiction-books-that-explore-ais-impact-on-society-ai-news/

Non-fiction books that explore AI's impact on society  - AI News

.pp-multiple-authors-boxes-wrapper display:none; img width:100%;

Artificial Intelligence (AI) is code or technologies that perform complex calculations, an area that encompasses simulations, data processing and analytics.

AI has increasingly grown in importance, becoming a game changer in many industries, including healthcare, education and finance. The use of AI has been proven to double levels of effectiveness, efficiency and accuracy in many processes, and reduced cost in different market sectors. 

AI’s impact is being felt across the globe, so, it is important we understand the effects of AI on society and our daily lives. 

Better understanding of AI and all that it does and can mean can be gained from well-researched AI books.

Books on AI provide insights into the use and applications of AI. They describe the advancement of AI since its inception and how it has shaped society so far. In this article, we will be examining recommended best books on AI that focus on the societal implications.

For those who don’t have time to read entire books, book summary apps like Headway will be of help.

Book 1: “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom

Nick Bostrom is a Swedish philosopher with a background in computational neuroscience, logic and AI safety. 

In his book, Superintelligence, he talks about how AI  can surpass our current definitions of intelligence and the possibilities that might ensue.

Bostrom also talks about the possible risks to humanity if superintelligence is not managed properly, stating AI can easily become a threat to the entire human race if we exercise no control over the technology. 

Bostrom offers strategies that might curb existential risks, talks about how Al can be aligned with human values to reduce those risks and suggests teaching AI human values.

Superintelligence is recommended for anyone who is interested in knowing and understanding the implications of AI on humanity’s future.

Book 2: “AI Superpowers: China, Silicon Valley, and the New World Order” by Kai-Fu Lee

AI expert Kai-Fu Lee’s book, AI Superpowers: China, Silicon Valley, and the New World Order, examines the AI revolution and its impact so far, focusing on China and the USA. 

He concentrates on the competition between these two countries in AI and the various contributions to the advancement of the technology made by each. He highlights China’s advantage, thanks in part to its larger population. 

China’s significant investment so far in AI is discussed, and its chances of becoming a global leader in AI. Lee believes that cooperation between the countries will help shape the future of global power dynamics and therefore the economic development of the world.

In thes book, Lee states AI has the ability to transform economies by creating new job opportunities with massive impact on all sectors. 

If you are interested in knowing the geo-political and economic impacts of AI, this is one of the best books out there. 

Book 3: “Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark

Max Tegmark’s Life 3.0 explores the concept of humans living in a world that is heavily influenced by AI. In the book, he talks about the concept of Life 3.0, a future where human existence and society will be shaped by AI. It focuses on many aspects of humanity including identity and creativity. 

Tegmark envisions a time where AI has the ability to reshape human existence. He also emphasises the need to follow ethical principles to ensure the safety and preservation of human life. 

Life 3.0 is a thought-provoking book that challenges readers to think deeply about the choices humanity may face as we progress into the AI era. 

It’s one of the best books to read if you are interested in the ethical and philosophical discussions surrounding AI.

Book 4: “The Fourth Industrial Revolution” by Klaus Schwab

Klaus Martin Schwab is a German economist, mechanical engineer and founder of the World Economic Forum (WEF). He argues that machines are becoming smarter with every advance in technology and supports his arguments with evidence from previous revolutions in thinking and industry.

He explains that the current age – the fourth industrial revolution – is building on the third: with far-reaching consequences.

He states use of AI in technological advancement is crucial and that cybernetics can be used by AIs to change and shape the technological advances coming down the line towards us all.

This book is perfect if you are interested in AI-driven advancements in the fields of digital and technological growth. With this book, the role AI will play in the next phases of technological advancement will be better understood.

Book 5: “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” by Cathy O’Neil

Cathy O’Neil’s book emphasises the harm that defective mathematical algorithms cause in judging human behaviour and character. The continual use of maths algorithms promotes harmful results and creates inequality.

An example given in  the book is of research that proved bias in voting choices caused by results from different search engines.

Similar examination is given to research that focused Facebook, where, by making newsfeeds appear on users’ timelines, political preferences could be affected.

This book is best suited for readers who want to adventure in the darker sides of AI that wouldn’t regularly be seen in mainstream news outlets.

Book 6: “The Age of Em: Work, Love, and Life when Robots Rule the Earth” by Robin Hanson

An associate professor of economics at George Mason University and a former researcher at the Future of Humanity Institute of Oxford University, Robin Hanson paints an imaginative picture of emulated human brains designed for robots. What if humans copied or “emulated” their brains and emotions and gave them to robots?

He argues that humans who become “Ems” (emulations) will become more dominant in the future workplace because of their higher productivity.

An intriguing book for fans of technology and those who love intelligent predictions of possible futures.

Book 7: “Architects of Intelligence: The truth about AI from the people building it” by Martin Ford

This book was drawn from interviews with AI experts and examines the struggles and possibilities of AI-driven industry.

If you want insights from people actively shaping the world, this book is right for you!

CONCLUSION

These books all have their unique perspectives but all point to one thing – the advantages of AI of today will have significant societal and technological impact. These books will give the reader glimpses into possible futures, with the effects of AI becoming more apparent over time.

For better insight into all aspects of AI, these books are the boosts you need to expand your knowledge. AI is advancing quickly, and these authors are some of the most respected in the field. Learn from the best with these choice reads.