Domen Zavrl: What Is Cryptography? – The Merkle Hash

Cryptography is a field of information security that is often misunderstood. Although the fundamental principles of algorithms generally stay the same, as infections and attacks evolve so too must these algorithms to maintain the security of confidential information.

Cryptography incorporates three key principles: encryption, integrity, and authentication.

Encryption

To encrypt a file data is converted into an unreadable form, protecting its privacy during storage, transfer and reception. Encrypted data is decrypted via a process known as decryption.

In essence, encryption and decryption require a special key, so that while data appears scrambled, both the sender and the desired recipient can still read and understand it.

Integrity

Cryptography assures message integrity, meaning that messages are accurately communicated and not altered or intercepted en route from the sender to the recipient. This is often achieved by hashing data, or cryptographically mapping out its path.

Maintaining message integrity requires technical skills. It can be accomplished using one of the following three techniques:

Authentication

Authentication is used to verify the senders identity. It consists of a short string of information that is used to confirm that the message originated from the stated sender. Method authentication code systems generally consist of three algorithms:

What Is the Difference Between Asymmetric and Symmetric Cryptography?

With asymmetric cryptography, two different keys are used to encrypt and decrypt the file. All participants in an asymmetric cryptosystem have both a public key and a private key. The public key can be freely distributed, but the private key is kept secret.

Data encrypted using a public key can only be decrypted using a corresponding private key.

With symmetric cryptography, both encryption and decryption are carried out using the same key. The sender and recipient must both already have the shared key.

Symmetric cryptography is generally more suitable for encrypting large amounts of data, since this form tends to be faster. Asymmetric cryptography is only suitable for encrypting files that are smaller than the size of the key, i.e. 2048 bits, or smaller.

Domen Zavrl has two PhDs: one in Applied Macroeconomics and the other in System Dynamics. Mr Zavrl has also studied Cryptology at Stanford University. He is an associate of Framingham Asset Management, as well as a member of The Institute of Internal Auditors.

Image(s): Shutterstock.com

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Domen Zavrl: What Is Cryptography? - The Merkle Hash

Switzerland files criminal complaints over crypto spying on behalf of CIA – Financial World

The Swiss Government had filed a criminal lawsuit over the United States Central Intelligence Agencys alleged abuse of a cryptography firm to spy on several Governments secret intel and communications, the Swiss Attorney Generals office said in a statement on Sunday, the 1st of March 2020.

Besides, according to the court documents, the criminal complaint was filed against persons of unknown origin who had been allegedly breaching the international law of exports controls. On top of that, latest accusation from the Swiss Government over CIAs abuse of a cryptography firm to execute covert operations on behalf of them followed a similar high-profile lawsuit regarding alleged breach of Government data named as Operation Rubicon dated back to the 2007s, which for decades involved United States CIA and Germanys BND to covertly spying over other nations encrypted messages by utilizing a technology sold by Swiss firm Crypto AG.

Meanwhile, adding that the Swiss Attorney Generals office would review the complaints before proceeding in to criminal jurisdictions, the Attorney Generals office said in its Sundays (March 1st) statement, The Office of the Attorney General confirms it has received a criminal complaint by the State Secretariat for Economic Affairs (SECO) dated Feb. 2, 2020 regarding possible violations of export control law.

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Onchain Custodian Picks IBM’s Solution to Securely Expand its Wallet Management Offering – Fintech News Singapore

Onchain Custodian (ONC), the Sequoia-backed digital assets custody service provider headquartered in Singapore, has just released the next version of its SAFE platform, integrating cryptography services on an infrastructure securely hosted by IBM.

The digital asset custody SAFE platform complements its cold storage offering with a wallet management service underpinned by the IBM Cloud Hyper Protect Crypto Services. IBM Cloud Hyper Protect Crypto Services allows for secure key generation and protection of key by taking advantage of the industrys first and only FIPS 140-2 Level 4 1 certified Hardware Security Module (HSM) available in the cloud, which means that its highly tamper-resistant.

Storing keys in such an environment means they are highly secured not even IBM, the cloud infrastructure provider, can access the keys. Only Onchain Custodian, responding to an authenticated customer instruction, can access them. The platform update enables digital asset exchanges, fund managers, and projects, among others, to outsource partially or fully the safe hosting of their wallets, while securely automating transaction flows based on threshold and other security measures.

Alexandre Kech

Alexandre Kech, CEO of Onchain Custodian, said

Being able to work with IBM and use their technology to create a highly secure platform was a privilege. IBM Cloud Hyper Protect Crypto Services is exactly what we were looking for. Onchain Custodian can focus on building the best execution platform and user experience with our technology provider Onchain while IBM provides us with the best HSM on cloud solution in the market to securely host, maintain and operate our SAFE key management infrastructure.

Rohit Badlaney

Rohit Badlaney, Director, IBM Z as a Service, said

By taking advantage of IBM Cloud Hyper Protect Services, Onchain Custodian can build a highly resilient and secure cloud-based solution that digital asset custody deserves. Supporting custodians like Onchain Custodian is an ideal use case for IBM Cloud Hyper Protect Services.

Onchain Custodian is backed by Fosun, DHVC and Sequoia Capital, the venture capital firm which has backed companies that now control $1.4 trillion of combined stock market value, including Apple, Google, Oracle and PayPal, and is gaining significant foothold as it builds up its portfolio of institutional clients across the Asia Pacific region.

Da Hongfei

Da Hongfei, Onchain Custodians Chair of the Board, concluded:

Since our debut, we have been relentlessly growing our customer base and open finance service offering with our curated partners. With our enhanced custody solution now live, Onchain Custodian is ready to expand further. We are looking for strategical investors to accelerate our scaling.

Independent third-party digital asset custody has become an essential piece of infrastructure that many experts consider critical for the long-term sustainability of digital assets and cryptocurrencies. By providing a focused approach to security, operational efficiency and risk management to institutional players holding cryptocurrencies and digital assets, Onchain Custodian allows its customers to focus on their core business.

Featured image: Alexandre Kech, CEO of Onchain Custodian

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Onchain Custodian Picks IBM's Solution to Securely Expand its Wallet Management Offering - Fintech News Singapore

A change in Safari will soon prevent website owners from using TLS certificates for longer than 13 months – Gizmo Posts 24

Within a short while, Apples browser Safari will warn users when a website theyre visiting is using a TLS/SSL certificate that is valid for more than 398 days. It doesnt have to be an expired one either. Any certificate that has been valid for more than 398 days since issuing will now be automatically flagged by the browser.

This has come after the 49th CA/Browser Forum in Slovakia, and The Register reported that the goal is plain- ensuring that web developers are using the latest certificates and technology available. Before this came to be, developers were able to assign certificates for multiple years, resulting in using technology that was long out of date.

As reported by an insider, the aim is to improve website security by making sure Deva uses certificates with the latest cryptographic standards and reducing the number of old and outdated certificates that has a high chance of getting stolen and re-used for phishing and drive-by malware attacks. If any miscreant happens to break the cryptography in an SSL/TLS standard, people will be switching over to more secure certificates witn hin a year.

Like any other thing, this is also going to have its set of negative points. According to Tim Callan, who is from the SSL management firm Sectigo, more certificate replacements means more chances of something going wrong.

In a report to The Register, he stated, Companies need to look for automation to assist with certificate deployment, renewal, and lifecycle management to reduce human overhead and the risk of error as the frequency of certificate replacement increases.

As of now, GitHub and Microsoft are using two-year certificates, with microsoft.com set to be renewed in October. If they plan to keep going with their two-year policy, you can expect Safari to tell you that the website isnt secure.

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A change in Safari will soon prevent website owners from using TLS certificates for longer than 13 months - Gizmo Posts 24

Cloud Security Alliance 2020 Initiatives Changing the Face of IT Audit and Cloud Assurance – AiThority

Certificate of Cloud Auditing Knowledge and Cloud Controls Matrix v4 represent critical progress to modernize the audit profession and align cloud assurance with technology innovations

TheCloud Security Alliance (CSA), the worlds leading organization dedicated to defining standards, certifications and best practices to help ensure a secure cloud computing environment, announced a call for subject-matter experts to support the ongoing review of its flagship document, the Cloud Controls Matrix (CCM), Version 4 of which will be released later this year. CCM v4 will reflect the current cloud technology landscape, providing cloud users with a better, more comprehensive security framework and guidelines to facilitate both implementation and audit.

Calling all today cloud subject-matter experts! @cloudsa is asking for help to support the ongoing review of its flagship document, the Cloud Controls Matrix (CCM) Version 4. Join Us!

Additionally, CSA is pleased to announce that theCertificate of Cloud Auditing Knowledge (CCAK)subject-matter expert working group has held initial program development meetings and that the CCAK credential and courseware will be previewed at CSAsSECtemberconference (Seattle, Sept. 14-18). The CCAK is a new credential for industry professionals that demonstrates expertise in the essential principles of assessing and auditing cloud computing systems and will be released in the second half of 2020. The CCAK will provide a common baseline of knowledge and shared nomenclature to ensure that IT and security professionals, as well as auditors, have the right expertise and tools to appropriately and accurately understand and measure the effectiveness of cloud security controls.

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For 11 years, the Cloud Security Alliance has led the industry in delivering the necessary innovations to build the trusted cloud ecosystem on a global basis. In 2020, CSA will focus on supporting the cloud community in acquiring the necessary tools, skills, and expertise to ensure that the many iterations of cloud meet robust security and privacy objectives, said Daniele Catteddu, Chief Technology Officer, Cloud Security Alliance. As organizations adopt DevOps, CI/CD, and related innovations, the audit function must keep pace. With the release of CCM and CCAK, we continue to support the community in their cloud journeys.

The Cloud Controls Matrix is the de facto standard in the market. Its latest iteration will include new control objectives in areas such as container and microservices, cryptography, and identity and access management, along with implementation guidance, and will improve upon the auditability of existing controls.

Recommended AI News: Cubic Signs Agreement With US Special Operations for Intelligence, Surveillance and Reconnaissance R&D

Cloud auditing skills are becoming a mandatory requirement for IT auditors and will become fundamental expertise for any IT manager and professional, especially in the areas of governance, risk management, compliance, and vendor/supply chain management. Traditional IT audit education and certification do not adequately prepare professionals for the challenges cloud provides. Recent breaches demonstrate the knowledge and responsibility gap that comprehensive cloud auditing frameworks such as the CCAK will solve.

Those interested in contributing to the development of the CCAK are encouraged to join the CSACloud Audit Expert Group. Group members should be familiar with CSAs best practices and control frameworks, such as theCloud Controls Matrix (CCM), theConsensus Assessment Initiative Questionnaire (CAIQ), andCSA STAR levels of assessment, as well as have knowledge in such key areas as cloud risk management, compliance, continuous auditing, and more. Members will be tasked with reviewing and providing advice on the scope, curriculum, objectives structure, go-to-market, and value proposition for the CCAK.

Recommended AI News: AiThority Interview with Adrian Leer, Managing Director Triad Group Plc

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Global Blockchain Technology in Healthcare Market was Estimated to be US$ 633.99 Mn in 2018 and is Expected to Reach US$ 2464.50 Mn by 2027 Growing at…

In terms of revenue, global blockchain technology in healthcare market was evaluated at US$ 633.99 Mn in 2018 and is expected to reach US$ 2,464.50 Mn by 2027, growing at a CAGR of 16.34%

PUNE, India, March 2, 2020 /PRNewswire/ -- The global blockchain technology in healthcare market is expected to gain significant traction owing to the ability of blockchain technology to eradicate the incidences of healthcare data breaches. The healthcare industry is prone to numerous causes of data breaches including, unauthorized access/disclosure, hacking/IT incident, and improper disposal amongst others. According to a report published by the Health Insurance Portability and Accountability Act (HIPAA) on healthcare data breach, there was a 44.44% (month-over-month) increase in the healthcare data breaches in October 2019. Nearly 661,830 healthcare records were reported as impermissibly disclosed, exposed, or stolen in those breaches. For instance, in May 2019, the American Medical Collection Agency was hacked for nearly eight months, which resulted in compromised patient data. Similarly, an American clinical laboratory, Quest Diagnostics Incorporated reported the breach of personal and financial data, impacting up to 12 million patients so far. Furthermore, the healthcare sector has witnessed nearly 15 million patient records that have been compromised in 503 breaches in 2018, which is expected to push pharmaceutical companies, healthcare providers, and payers to leverage blockchain technology for a secured flow of information.

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Emerging blockchain technology offers a solution to data security in healthcare industry. The blockchain technology features decentralized storage, smart contracts, and cryptography that provides a secured framework for healthcare organizations, improving data protection while maintaining preventing unauthorized access along with data accuracy. In addition, blockchain technology allows patients to review their information before officially recording it into the database, which has generated opportunities for healthcare providers and patients to evaluate information and preserve the accuracy of data. Market participants in the blockchain technology in healthcare marketare enabling end-users to move patient health information to a decentralized storage solution by breaking the records into fragments, which has enabled healthcare organizations to protect patient information. Furthermore, the ability of blockchain technology to improve the interoperability of data between different providers along with improving the overall security of data is among the key factors anticipating in the increased adoption of blockchain technology. Thus, such factors are projected to propel the blockchain technology in healthcare market during the forecast period.

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The detailed research study provides qualitative and quantitative analysis of global blockchain technology in healthcare market. The market has been analyzed from demand as well as supply side. The demand side analysis covers market revenue across regions and further across all the major countries. The supply side analysis covers the major market players and their regional and global presence and strategies. The geographical analysis done emphasizes on each of the major countries across North America, Europe, Asia Pacific, Middle East & Africa and Latin America.

Key Findings of the Report:

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From the in-depth analysis and segregation, we serve our clients to fulfill their immediate as well as ongoing research requirements. Minute analysis impact large decisions and thereby the source of business intelligence (BI) plays an important role, which keeps us upgraded with current and upcoming market scenarios.

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Global Blockchain Technology in Healthcare Market was Estimated to be US$ 633.99 Mn in 2018 and is Expected to Reach US$ 2464.50 Mn by 2027 Growing at...

Machine Learning at the Push of a Button – EE Journal

Physician, heal thyself Luke 4:23

My Thermos bottle keeps hot drinks hot and cold drinks cold. How does it know?

An electrical engineer would probably design a Thermos with a toggle switch (HOT and COLD), or a big temperature dial, or if you work in Cupertino an LCD display, touchpad, RTOS, and proprietary cable interface. Thankfully, real vacuum flasks take care of themselves with no user input at all. They just work.

It would sure be nice if new AI-enabled IoT devices could do the same thing. Instead of learning all about AI and ML (and the differences between the two), and learning how to code neural nets, and how to train them, and what type of data they require, and how to provision the hardware, etc., itd be great if they just somehow knew what to do. Now that would be real machine learning.

Guess what? A small French company thinks it has developed that very trick. It uses machine learning to teach machine learning. To machines. Without a lot of user input. It takes the mystery, mastery, and mythology out of ML, while allowing engineers and programmers to create smart devices with little or no training.

The company is Cartesiam and the product is called NanoEdge AI Studio. Its a software-only tool that cranks out learning and inference code for ARM Cortex-Mbased devices, sort of like an IDE for ML. The user interface is pretty to look at and has only a few virtual knobs and dials that you get to twist. All the rest is automatic. Under the right circumstances, its even free.

Cartesiams thesis is that ML is hard, and that developing embedded AI requires special skills that most of us dont have. You could hire a qualified data scientist to analyze your system and develop a good model, but such specialists are hard to find and expensive when theyre available. Plus, your new hire will probably need a year or so to complete their analysis and thats before you start coding or even know what sort of hardware youll need.

Instead, Cartesiam figures that most smart IoT devices have certain things in common and dont need their own full-time, dedicated data scientist to figure things out, just like you dont need a compiler expert to write C code or a physicist to draw a schematic. Let the tool do the work.

The company uses preventive motor maintenance as an example. Say you want to predict when a motor will wear out and fail. You could simply schedule replacement every few thousand hours (the equivalent of a regular 5000-mile oil change in your car), or you could be smart and instrument the motor and try to sense impending failures. But what sensors would you use, and how exactly would they detect a failure? What does a motor failure look like, anyway?

With NanoEdge AI Studio, you give it some samples of good data and some samples of bad data, and let it learn the difference. It then builds a model based on your criteria and emits code that you link into your system. Done.

You get to tweak the knobs for MCU type, RAM size, and type of sensor. In this case, a vibration sensor/accelerometer would be appropriate, and the data samples can be gathered in real-time or canned; it doesnt matter. You can also dial-in the level of accuracy and the level of confidence in the model. These last two trade off precision for memory footprint.

NanoEdge Studio includes a software simulator, so you can test out your code without burning any ROMs or downloading to a prototype board. That should make it quicker to test out various inference models to get the right balance. Cartesiam says it can produce more than 500 million different ML libraries, so its not simply a cut-and-paste tool.

As another example, Cartesiam described one customer designing a safety alarm for swimming pools. They spent days tossing small children into variously shaped pools to collect data, and then several months analyzing the data to tease out the distinguishing characteristics of a good splash versus one that should trigger the alarm. NanoEdge AI Studio accomplished the latter task in minutes and was just as accurate. Yet another customer uses it to detect when a vacuum cleaner bag needs emptying. Such is the world of smart device design.

The overarching theme here is that users dont have to know much of anything about machine learning, neural nets, inference, and other arcana. Just throw data at it and let the tool figure it out. Like any EDA tool, it trades abstraction for productivity.

In todays environment, thats a good tradeoff. Experienced data scientists are few and far between. Moreover, you probably wont need his/her talents long-term. When the project is complete and youve got your detailed model, what then?

NanoEdge AI Studio is free to try but deploying actual code in production costs money. Cartesiam describes the royalty as tens of cents to a few dollars, depending on volume. Sounds cheaper than hiring an ML specialist.

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Machine Learning at the Push of a Button - EE Journal

Is Machine Learning Always The Right Choice? – Machine Learning Times – machine learning & data science news – The Predictive Analytics Times

By: Mark Krupnik, PhD, Founder and CEO, Retalon

Since this article will probably come out during Income tax season, let me start with the following example: Suppose we would like to build a program that calculates income tax for people. According to US federal income tax rules: For single filers, all income less than $9,875 is subject to a 10% tax rate. Therefore, if you have $9,900 in taxable income, the first$9,875 is subject to the 10% rate and the remaining $25 is subject to the tax rate of the next bracket (12%).

This is an example of rules or an algorithm (set of instructions) for a computer.

Lets look at this from a formal, pragmatic point of view. A computer equipped with this program can achieve the goal (calculate tax) without human help. So technically, this can be classified as Artificial Intelligence.

But is it cool enough? No. Its not. That is why many people would not consider it part of AI. They may say that if we already know how to do a certain thing, then the process cannot be considered real intelligence. This is a phenomena that has become known as AI Effect. One of the first references is known as Teslers theorem that says: AI is whatever hasnt been done yet.

In the eyes of some people, the cool part of AI is associated with machine learning, and more specifically with deep learning which requires no instructions and utilizes Neural Nets to learn everything by itself, like a human brain.

The reality is that human development is a combination of multiple processes, including both: instructions, and Neural Net training, as well as many other things.

Lets take another simple example: If you work in a workshop on a complex project, you may need several tools, for instance a hammer, a screwdriver, plyers, etc. Of course, you can make up a task that can be solved by only using a hammer or only screwdriver, but for most real-life projects you will likely need to use various tools in combination to a certain extent.

In the same manner, AI also consists of several tools (such as algorithms, supervised and unsupervised machine learning, etc.). Solving a real-life problem requires a combination of these tools, and depending on the task, they can be used in different proportions or not used at all.

There are and there will always be situations where each of these methods will be preferred over others.

For example, the tax calculation task described in the beginning of this article will probably not be delegated to machine learning. There are good reasons to it, for example:

the solution of this problem does not depend on data the process should be controllable, observable, and 100% accurate (You cant just be 80% accurate on your income taxes)

However, the task to assess income tax submissions to identify potential fraud is a perfect application for ML technologies.

Equipped with a number of well labelled data inputs (age, gender, address, education, National Occupational Classification code, job title, salary, deductions, calculated tax, last year tax, and many others) and using the same type of information available from millions of other people, ML models can quickly identify outliers.

What happens next? The outliers in data are not necessarily all fraud. Data scientists will analyse anomalies and try to understand the reason for these individuals being flagged. It is quite possible that they will find some additional factors that had to be considered (feature engineering), for example a split between tax on salary, and tax on capital gain of investment. In this case, they would probably add an instruction to the computer to split this data set based on income type. At this very moment, we are not dealing with a pure ML model anymore (as the scientists just added an instruction), but rather with a combination of multiple AI tools.

ML is a great technology that can already solve many specific tasks. It will certainly expand to many areas, due to its ability to adapt to change without major effort on a human side.

At the same time, those segments that can be solved using specific instructions and require predictable outcome (financial calculations) or those involving high risk (human life, health, very expensive and risky projects) require more control and if the algorithmic approach can provide it, it will still be used.

For practical reasons, to solve any specific complex problem, the right combination of tools and methods of both types are required.

About the Author:

Mark Krupnik, PhD, is the founder and CEO ofRetalon, an award-winning provider of retail AI and predictive analytics solutions for planning, inventory optimization, merchandising, pricing and promotions.Mark is a leading expert on building and delivering state-of-the-art solutions for retailers.

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Is Machine Learning Always The Right Choice? - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

Interest in machine learning and AI up, though slowing, one platform reports – HR Dive

Dive Brief:

As technologies such as AI and machine learning revolutionize the workplace, learning and development is coming to the forefront of talent management. Preparing workers for AI and automation will lead learning trends in 2020, according to a November 2019 Udemy report. While many workplaces will train employees to sharpen their tech skills, the report said, learning professionals will also need to focus on soft skills and skills related to project management, risk management and change management.

About 120 million workers around the world will need access to retraining opportunities, a need at least partly driven by AI and automation, according to a report from IBM. This need vastly outpaces the number of organizations equipped with resources that suffice for such an effort, however.

Platforms such as O'Reilly may aid in filling this gap. Third-party training programs are growing in popularity with seemingly positive results. Managers may prefer coders with training from a boot camp, for example, a recent report from HackerRank found. But there has been at least one report that external L&D programs boast false results;New York Magazine reported Lambda School, "a 'boot camp' for people who want to quickly learn how to code," has inflated the number of job placements secured by its graduates.

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Interest in machine learning and AI up, though slowing, one platform reports - HR Dive

AI and Machine Learning in Everyday Life – IMC Grupo

You may not know it, but machine learning and AI have pervaded our everyday lives and made it not just more convenient, but introduces new quality of life changes as well.

Predicting the Lottery

Before, it was very difficult to predict the next winning set of numbers in a lottery game as youll need a proven algorithm and the computing muscle of a powerful machine but that has changed with the emergence of artificial intelligence.

Today, predicting the results of an Arizona lottery are shown on websites such as The Lotto Pro. It uses both AI and machine learning for winning number recommendations, coupled with an advanced algorithm.

The site actually predicted the right numbers for the AZ lottery 2/25/2020 draw, which speaks a lot about its accuracy.

Knowing What Youre Searching For

Typing on a search query will result in a list of predictions that you can choose to save time. This feature draws on your past searches and artificial intelligence, along with personal details, age and location.

Search engines also get better over time as they collect data such as the length of time you spent on a page and your response when presented with a list of sites with your keyword.

AI Assistants

The rise of Siri, Google Assistant and Alexa have opened up ways on how we control technology. Without needing physical input, we can make our smart devices play music, turn on the lights, open the front door and more.

More than that, you can use these AI platforms to set up alarms, reminders and schedule meetings just like you would a real assistant.

Social Media

How you get your daily feed depend on AI and machine learning. Targeted ads, deleting inappropriate and offensive tweets and friend suggestions are all handled by an algorithm thats backed by artificial intelligence.

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AI and Machine Learning in Everyday Life - IMC Grupo