The best 5G mobile phone technology

5G mobile phone

In telecommunications, 5G is the acronym used to refer to the fifth generation of mobile telephony technologies.1 It is the successor to 4G technology. Its first standardized version (Release 15 - Stand Alone) is currently available, although telecommunications companies continue to research new technologies for later releases. Although the first commercial networks were launched in 2019, their use is expected to expand exponentially from 2020.2 3 4 5

The speed at which this technology is allowed to be navigated on mobile devices is up to 1.2 gigabits per second.
Development
The Swedish company Ericsson was the first to reach 5G speeds, with live demonstrations of the standard prior to network technology (prestandard) 5G. In November 2014, Huawei announced the signing of an agreement with Russian mobile operator MegaFon for standardize and develop 5G test networks, with a view to the 2018 World Cup. In February 2017, the United Nations-dependent International Telecommunication Union (ITU) revealed some of the specifications of 5G technology; These include minimum download speeds of 20 Gbps and. Gbps upload, and 4 ms latency. It is intended to optimize devices to make it as efficient as possible for the Internet of Things (IoT). Everyone is expected to use that connectivity by 2025.

Projects and research

The South Korean program "5Gmobile communication systems based on beam-divisionmultiple access and relays with group cooperation" was formed in 2008. In Europe, Neelie Kroes, the European Commissioner, received EUR 50 million for research in 2013 with the aim of delivering 5G mobile technology by 2020. In particular, the METIS 2020 Project was driven by an automobile manufacturer and several telecommunications companies, and plans to reach a global consensus on the future mobile communications system. As a result, in 2013, another project was initiated, called 5Green, linked to the METIS project and with the aim of developing 5G Mobile green networks. Here, the goal is to develop guidelines for the definition of the next generation network with special attention to aspects of energy efficiency, sustainability and accessibility.
On Wednesday, December 20, 2017, the 3rd Generation Partnership Program (3GPP) approved, in Lisbon (Portugal), the specifications of New Radio 5G Non-Autonomous (NSA 5G NR, or Non-Standalone 5G New Radio). This is the first Fifth Generation (5G) standard for mobile networks officially approved by 3GPP.

In early 2018, the US company Qualcomm published on its website that 5G mobile telephony would be available during 2019.
Bruno Jacobfeuerborn, CTO of Deutsche Telekom, stated: "We think about that both the non-independent and autonomous method of the new radio are similarly significant for the trustworthiness of the standard 5G particular. This timely completion of the NSA is an important step in that journey and in the development of the 5G ecosystem. It is crucial that the industry now redoubles its focus in Standalone mode to make progress towards a complete 5G system, we can deliver key 5G innovations, such as network segmentation to our customers."

On February 20, 2018 Vodafone and Huawei completed the world's first 5G mobile call in Spain.
In August 2018, Samsung unveiled that it had ready the Samsung Exynos Modem 5100, its first 5G modem, compatible with the final specifications of the 3GPP. It is built with 10nm technology and is prepared for the latest 5G specifications, 5G-NR, and for previous radio versions (4G, 3G, 2G, GSMA, etc).

On November 20, 2018, Nokia and T-Mobile announced the first 5G data transmission in the low 600 MHz band, opening up good prospects for this frequency with a view to 2020.
In Chile, the first tests of the 5G network were carried out in November 2017 and May 2018, the first test being carried out by the operator Claro together with the Finnish telecommunications company, Nokia, subsequently, in May 2018, the national operator Entel together with the Swedish telecommunications company, Ericsson, they conducted the second test in the country, reaching a record data transfer speed in South America, of 24Gbps.

On February 20, 2019 Samsung unveiled its first 5G mobile, the "Samsung Galaxy S10 5G"
On February 24, 2019, Huawei unveiled its first 5G mobile, the Huawei Mate X.
On 25 February 2019 at the Mobile World Congress in Barcelona, Spain, Subtel Undersecretary Pamela Gidi of Chile announced that by March 2019 the tender would be launched to national operators for the development of the 5G Network. 60Mhz spectrum would be made available nationally between the 700 MHz and 3.5 GHz bands.

On April 9, 2019 in Uruguay, the state telephone ANTEL, with nokia support, successfully completed the installation of the first 5G commercial network in Latin America and the third in the world after the United States and South Korea.
In June 2019, Vodafone Spain began giving 5G in the main cities of Spain.

Find out about all cyber attack or computer attack

Find out about all cyber attack or computer attack

On computers and computer networks an attack is an attempt to expose, alter, destabilize, destroy, delete to gain unauthorized access or use an asset. A cyberattack or computer attack, is any offensive maneuver of deliberate exploitation that aims to take control, destabilize or damage a computer system (computer, private network, etc.). The attacker is an individual or organization that tries to gain control of a computer system to use it for malicious purposes, to steal information or to harm its target. An cyberattack uses malicious code, to corrupt codes, private data or algorithms, generating consequences that compromise and breach the security of information systems.

Some cyberattacks, depending on where it is carried out, to whom or when, are part of a computer war or a cyber-terrorism attack. Attacks are now more sophisticated in even more ingenious.

Phenix Direct describes it this way:

A computer attack also involves exploiting some vulnerability or weakness in software or hardware, mainly with the aim of gaining some economic benefit. Cyberattacks usually tend to be carried out by lone individuals. However, sometimes when the attacks are carried out together they are usually done by groups, organizations or gangs, who call themselves hackers, since they focus on doing harm through attacks known as computer crimes.

Consequences

Computer attacks often have consequences of different types, from small damage to personal computers to million-dollar damage. For example, americaeconomia.com is estimated to have been attacked by April 8 billion by April 2019 alone.

Trivial or minor damages are attacks that do not cause much damage or can cause loss of functionality in certain applications, such as a computer virus or information erasure. Severe or major damage is attacks that result in total loss of information or even physical damage, such as hard drive erasure, personal data theft, and even industrial attacks. A 2015 study found that in just 2 years this type of cyberattacks generated an increase of $512 million in 2012 to $800 million in 2014. For 2019 in the World Economic Forum's annual Occupational Risk Perception Survey, cyberattacks are considered among the top 10 threats most likely to occur, ranking fifth and seventh as the highest economic impact

Types of attacks

A cyberattack is any type of offensive maneuver made by individuals or organizations that attack information systems such as infrastructures, computer networks, databases that are hosted on remote servers, through acts malicious ones usually originated from anonymous sources that also steal, alter or destroy a specific target by hacking a vulnerable system.

Indistinct attacks

These attacks are broad, global and do not distinguish between governments, companies or civilians.

WannaCry ransomware attacks.
Operation Shady RAT, a series of persistent computer attacks that began in 2011 and ended up affecting more than 70 international organizations.
Stuxnet
World of Hell, a collective of hackers that claimed several high-level computer attacks, some of its targets were the Information Systems Defense Agency, Rolex, Hard Rock Café, etc.
Attack on Sony Pictures, it was an attack perpetrated by the North Korean group "Guardians of Peace" (GOP) in which more than 100TB of information was disclosed on the internet.
Red October, found in 2012, worked worldwide for a long time before its disclosure, transmitting data extending from conciliatory insider facts to individual data, including from cell phones.
Destructive attacks

These attacks refer to inflicting damage on specific organizations.

Great Hacker War. A war of two hacker groups, Masters of Deception (MOD) and Legion of Doom (LOD).
LulzRaft, a hacker group known for low-impact attacks in Canada.
Operation Ababil, conducted against American financial institutions.
Cyberattack and resulting breakup on TV5 Monde April 2015.
Vulcanbot
Shamoon, a modular computer virus, was used in 2012 in an attack on 30,000 Saudi Aramco workstations, causing the company to spend a week fixing its services.
Wiper - In December 2011, the malware successfully erased information from hard drives at the headquarters of the Ministry of Petroleum.

Cyber warfare
See also: Cyber Warfare
These are destructive, politically motivated attacks aimed at sabotage and espionage.

2007 cyberattacks in Estonia, extensive attack on government and commercial institutions.
2010 cyberattacks in Estonia, relating to the 2010 Burmese general elections.
2010 South Japan-Korea Cyber Warfare.
2013 cyberattacks in Singapore, attack by Anonymous "in response to web censorship regulations in the country, especially in the news media".
OpIsrael, a broad "anti-Israel" attack.
Cyberattacks during the Russian-Geogiana War.
Cyberattacks in July 2009, against South Korea and the United States.
Operation Olympic Games, against Iranian nuclear facilities, purportedly conducted by the United States.
Operation Tunisia, attack by Anonymous during the Tunisian Revolution.
Espionage to the government
These attacks relate to the theft of information from/about government organizations.

2010 Cyberattack in the United States, cyberespionage targeting U.S. Army computers.
Cyberattack during the G20 summit in Paris, directed towards G20 documents including financial information.
GhostNet.
Moonlight Maze.
Operation Newscaster, cyberespionage through a secret operation supposedly done by Iran.
Operation Cleaver, cyberwarfare through a secret operation supposedly done by Iran.
Shadow Network, attacks on India by China.
Titan Rain, directed at U.S. defense contractors.
Google - In 2009, Chinese hackers breached Google's corporate servers by gaining access to a database with classified information about suspected spies, agents and terrorists under the supervision of the U.S. government.
Gauss Trojan, found in 2012, is a state-supported PC spying activity that utilizations best in class programming to remove a great deal of delicate information from a large number of machines for the most part situated in the Middle East.
Office of Personnel Administration for Data Breach - December 2014, volation on U.S. government employee data.
Corporate Espionage
These attacks relate to the theft of data from corporations related to patented methods or emerging products/services.

Operation Aurora.

Operation Socialist, United Kingdom obtained information from a Belgian telecommunications company.
Hacking by Sony Pictures Entertainment.
Theft of email addresses and login credentials
These attacks refer to the theft of access information for specific web resources.

PlayStation Network outage in 2011, attack resulting from credential theft and causing network outages.

Gawker - by 2010, a band of anonymous hackers had settled on the site's servers and filtered half a gigabyte on private data.

IEEE - in September 2012, users, passwords and web activities of nearly 100,000 members were exposed.

LivingSocial - in 2014 the company suffered a security breach that exposed the names, emails and passwords of more than 50 million of its users.

RockYou - in 2009, the company suffered a security breach resulting in the exposure of more than 32 million accounts.

Yahoo!- In 2012, hackers revealed login credentials from more than 453,000 accounts. It was repeated in January 2013 and January 2014.

Theft of credit cards and financial data

Violation of information in JPMorgan Chase 2014, purportedly done by a group of Russian hackers.

MasterCard - In 2005, the company announced that 45.1 million cardholders may have suffered information theft from their accounts due to the hacking of payment processors.

VISA and MasterCard - in 2012, they warned bank card issuers that a third party of the payment processor suffered a security breach, affecting up to 10 million credit cards.

Metro - in 2012, two Romanian men confessed to partaking in a global connivance that hacked charge card installment terminals on in excess of 150 Subway establishments and took information from in excess of 146,000 records.
StarDust - in 2013, they compromised 20,000 cards in active campaign, affecting American merchants.

Target - in 2013, approximately 40 million credit and debit cards have reported to have been affected by a failure of those cards. As per another gauge, it traded off upwards of 110 million Target clients.

Goodwill Industries - In September 2014, the company suffered credit card failures that affected charitable retailers in at least 21 states.

Home Depot - In September 2014, cyber criminals who compromised Home Depot's network and installed malware on point-of-sale systems that roughly stole information from 56 million payment cards.

Medical data theft

In May 2015, three health organizations were attacked in the United States: Anthem Inc., Premera Blue Cross and CareFirst. All three attacks offset information on more than 91 million people.

Hacktivismo

Hay diversos tipos de ataques informáticos, algunos de ellos son:

Denial-of-service attack, also called a Denial of Service (DoS) attack, is an attack on a computer system or network that causes a service or resource to be inaccessible to legitimate users, usually causing the loss of network connectivity by consumption of the victim's network bandwidth or overload of the victim's system computational resources.
Man in the middle, sometimes abbreviated mitM, is a situation where an attacker (usually through a port tracker) monitors communication between two parties and falsifies exchanges to impersonate one of them.
Re-injection attacks, a form of network attack, in which a valid data transmission is malicious or fraudulently repeated or delayed. It is carried out by the author or by an adversary that intercepts the information and relays it, possibly as part of a masked attack.
Zero-day attack, attack carried out against a computer, from which certain vulnerabilities are exploited, or security holes of any program or programs before they are known, or that, once the existence of the vulnerability is published, is carry out the attack before the release of the patch that solves it.

Logical attacks

Trashing:
This usually occurs when a user writes down their login and password on a piece of paper and then, when they remember, throws it away. This, however innocent it may seem, is the one an attacker can take advantage of to make a key to enter the system.

Monitoring:

This type of attack is done to observe the victim and his system, with the aim of establishing their vulnerabilities and possible forms of future access.

Authentication Attacks:

This type of attack aims to trick the victim's system into entering it. Usually this deception is done by taking the sessions already established by the victim or getting their username and password (their most common form is to receive an email with a fake shortcut link from the most visits pages).

Denial of Service(DoS):

Existing protocols were now designed to be made in an open community and with a relationship of mutual trust. The reality indicates that it is easier to disorganize the operation of a system than to access it; thus, Denial of Service attacks aim to overwhelm the victim's resources in such a way that the services provided by the victim are disabled.

Modification (damage):
modification or damage can be given as:

Tampering or Data Diddling:

This category refers to unauthorized modification of the data or SOFTWARE INSTALLED on the victim system (including file deletion).

Footprint Erase:

Fingerprint erasure is one of the most important tasks that the intruder must perform after entering a system, because, if his/her income is detected, the administrator will look for how to get "to plug the gap" security, prevent future attacks and even track the Attacker.

Other attacks

Brute force attack. It is not necessarily a procedure to be performed by computer processes, although this system would save time, energy and effort. The brute force attack system tries to recover a key by testing all possible combinations until it finds the one that is searched, and that allows access to the system, program or file under study.

Meet the best virtual reality goggles and their history

Virtual reality headset

A virtual reality headset, also called virtual reality goggles, virtual reality viewer or HMD (head-mounted display), is a helmet-like display device, which allows you to play computer-created images on a screen very close to the eyes or projecting the image directly onto the retina of the eyes. In this second case the virtual reality headset is called a virtual retina monitor.

Due to its proximity to the eyes, the virtual reality headset makes the images displayed much larger than those perceived by normal screens, and even allow to encompass the entire field of view of the user. Because the helmet is attached to the head, it can follow the user's movements, thus making it feel integrated into computer-created environments.

History

The Sega VR, announced in 1991 and seen in early 1993 at Winter CES, was never released. One of the first VR headsets, the Forte VFX1, was announced at CES in 1994. The VFX-1 has stereoscopic showcases, 3-hub head following and stereo earphones. Sony, another pioneer, propelled the Glasstron in 1997, which has a discretionary position sensor, which enables the client to see the environment, with point of view moving as their head moves, giving a profound sentiment of inundation. These VR headsets gave MechWarrior 2 players a new visual perspective of seeing the battlefield from inside their ship's cockpit. Notwithstanding, these early caps bombed economically because of their restricted innovation and were portrayed by John Carmack as "glancing through bathroom tissue tubes".

In 2012, he started a crowdfunding campaign for VR headsets known as the Oculus Rift; The project was led by several prominent video game developers, including Carmack 5, which later became the company's CTO 6. In March 2014, the parent company of the Oculus VR project was acquired by Facebook for $2 billion. The final consumer-oriented launch of Oculus Rift began on March 28, 2016.

In March 2014, Sony introduced a prototype helmet for PlayStation 4, which was later called PlayStation VR. In 2014, Valve introduced some prototype helmets, which led to a partnership with HTC to produce the Vive, which focuses on "room scale" virtual reality environments with which users can navigate and interact naturally. The Vive was discharged in April 2016 and PlayStation VR in October 2016.

Virtual reality headsets and viewers are also designed for smartphones. Unlike helmets with built-in screens, these units are essentially boxes in which you can insert a smartphone. Virtual reality content is viewed from the device's screen through lenses that act as a stereoscope, rather than using dedicated internal displays. Google discharged various particulars and related DIY units for computer generated reality watchers known as Google Cardboard; these visors can be fabricated utilizing minimal effort materials, for example, cardboard (thus the name). Samsung Electronics partnered with Oculus VR to jointly develop the Samsung Gear VR (which is only compatible with recent Samsung Galaxy devices), while LG Electronics developed a helmet with committed presentations for its LG G5 cell phone known as LG 360 VR. Asian hardware manufacturers such as Xion and Kolke have developed low-cost virtual reality headsets. In 2017, chinese company Tencent announced that it was preparing to launch its VR headset that year.
Types

Monocular: Images are only reproduced over one eye. Technically it's an HMD but it's not for virtual reality. That's the case with Google Glass.
Binocular: the images are reproduced over both eyes, thus obtaining a stereoscopic image.
On the other hand, it is also possible to distinguish:

Virtual reality helmets or glasses: occupy the field of view of the user so that he has no perception of the surrounding environment, thus allowing the complete immersion of this in a virtual reality, since he will only perceive the images created by computer and reproduced on the screen.
Helmets or glasses of augmented reality or mixed reality: also known as optical HMD (or OHMD) allow the user to see the entire environment around it and introduce into this virtual objects or information, thus producing what is known as augmented reality or reality Mixed. This category includes smart glasses, whose main use is to display information available to smartphone users without using their hands.
Finally, according to their operability, they can be distinguished:

Mobile virtual reality glasses: they are really cases, which do not have their own screen or processor but are prepared to house a mobile phone, in which the images will be played back. Examples: Samsung Gear VR, Google Cardboard, and many others from different manufacturers.
Processorless virtual reality goggles: they include their own screen and sensors but connect to an external device (typically a personal computer) to receive the images. Examples: Oculus Rift, PlayStation VR, HTC Vive...
Autonomous virtual reality glasses: these include all the necessary components, such as the housing, display, sensors and processor. Example: Microsoft Hololens and others in development such as Intel's Project Alloy, Qualcomm and Google's Daydream Standalone, or Samsung's Exynos VR.
Models
See Virtual Reality: Products

Helmets or goggles

- Glasses with built-in display (Rift, Playstation RV, HoloLens, VIve, StarVR, FOVE VR)

- Mobile VR housings or goggles (Gear VR, Daydream View, Cardboard, Plastic Caracasa and other materials)

- Old models (Virtual Boy, Forte VFX1, eMagin Z800 3DVisor)

Position sensors

Controllers (Leap Motion, STEM System, PrioVR, Gloveone, PowerClaw)
Other peripherals (Virtuix Omni, Cyberith Virtualize)
Other Systems (CAVE System)
Features
There are several key concepts in technology used by virtual reality headsets. Among them we can highlight:

Screen resolution: it is a very important parameter because it depends mostly on the definition of the image perceived by the HMD user. A typical resolution today (early 2016) is 1080x1200 pixels for each eye of the Oculus Rift and HTC Vive.
Field of view – The amplitude of the user's field of view is occupied by the virtual image. The higher the, the better the immersive feeling. The Oculus Rift DK2 for example offers a 100o field of view.
Head tracking latency—This is the time that elapses between when the user moves their head and the time when the displayed image is readjusted to that movement. Manufacturers try to minimize it as excessive latency can cause user dizziness, as well as less realism. PlaySation VR registers at a latency of 18 ms.

Refresh rate—The number of images displayed per second. From 60 Hz is considered a good ratio. For example, the HTC Vive Pre headset and Oculus Rift CV1 run at 90 Hz, while the PlayStation VR reaches 120 Hz.
Head tracking (rotational tracking): using internal sensors (gyroscope, accelerometer, magnetometer) the HMD detects where the user's head is oriented.
Positional tracking—also known as absolute positioning, is achieved by a sensor, usually external to the glasses themselves, which detects where exactly the user's head is located and any changes that occur in that position. It is a feature that only incorporates the most advanced HMDs.
Eye tracking— Infrared sensors inside the helmet capture the movements of the eye. This allows things like replicating your eye movements in your virtual avatar, or provoking reactions from other characters depending on the way you look at them. Pioneer of this functionality is the FOVE VR model.

Stereoscopic vision: feature present in almost all virtual reality devices, which showing a slightly different image to each eye allows to visualize the environment in three dimensions.
Screen-door effect—A visual effect that happens on screens when lines separating pixels from screen-door effect become visible in the projected image. The result is similar to looking through an anti-mosquito fabric. It's a common effect on virtual reality viewers that aren't advanced enough.

Uses 

Medical training

Virtual reality headsets are currently being used as a means to train medical students for surgery. It enables them to perform fundamental techniques in a virtual, controlled condition. Students perform surgeries on virtual patients, allowing them to acquire the skills needed to perform surgeries on real patients. It also allows students to review surgeries from the perspective of the primary surgeon.


Traditionally, students had to participate in surgeries and essential parts were often lost. Now, with the use of VR headsets, students can view surgical procedures from the perspective of the lead surgeon without losing essential parts. Students can also pause, rewind, and quickly advance surgeries. They can also refine their techniques in a real-time simulation in a risk-free environment.

Artificial intelligence in digital marketing

Artificial intelligence in digital marketing

Currently, the processes associated with artificial intelligence (AI) have a vital impact on marketing and internet advertising as these facilitate the study of the market in brands. This has allowed to make delivery of advertising information appropriate to the characteristics and interests of users, a process also called behavior targeting. AI then serves to study, define, and segment users to create speeches and strategies that respond to the demands and attributes of their audience. To this end, AI also includes different forms of tracking and collection of information such as tracking cookies or data capture behind different free platforms that feed its databases. Thus, depending on the quantity and quality of the information, and the objectives and times of the companies, there is a certain type of learning for the optimal AI machine to develop the task. However, the generation of "solutions" and results it is a repetitive and incessant process as companies always seek to anticipate reality.

Data collection

Data collection is the set of processes by which it is possible to obtain the information to create or enrich a database; this can occur in a similar or virtual way. In the non-virtual world, people share their information when they fill out an application, register to vote, register a product for warranty, purchase a driver's license, or participate in a raffled. Sensitive data such as transactional data can also be known when people use their credit card or pay an invoice with a check. Virtual information, on the other hand, is mainly favored by the internet since in it cookies record every click, conscious searches in the browser and the interaction of people on social networks are recorded, the cell phones record their owners if they say "hello siri" or "ok google", there are cameras on the streets that keep records in databases etc. And, in the future, when the internet of things to everyday life is developed and integrated more, the amount of information will be much more detailed.

The following is the list of data typologies that an organization can collect:

Demographics: Name, Gender, Age, Race, Address, Phone, Fingerprint, Heart Rate, Weight, Device, Government ID.
History: Education, Career, Criminal Background, Press Exposure, Publications, Awards, Association Memberships, Credit Score, Legal Affairs, Divorce, Travel, Loans.
Preferences: Settings, Promotion of Ideas, Political Party, Social Groups, Social Likes, Entertainment, Hobbies, News sources, Browser History, Brand Affinity.
Possessions: Income, Home, Automobiles, Devices, Clothing, Jewelry, Investments, Subscriptions, Collections, Social Relations.
Activities: Keystrokes, Gestures, Eye Tracking, Part of the day, Location, IP address, Social posts, Departures to eat, Watch TV, Heart rate over time.
Personality: Religion, Values, Donations, Political Party, Skepticism/Altruism, Introverted/Extrovert, Generous/Greedy, Adaptable/Inflexible, Aggressive/Passive, Opinion, Mood.

Data hygiene

To feed AI with data it becomes necessary to know how it was collected, cleaned, sampled, added, segmented and what transformation is required before combining it with other data streams.1 This process is paramount to ensure that the outcome of the analysis serves the desired goal and can influence the outside world: for example find the perfect owner to induce a group of people towards shopping. For this reason a data expert is recommended to decide which bits (information) should be included and which are rectified.

See also Data Mining

Data features

For a marketing expert it is necessary to know the typology of the data with which we work in artificial intelligence. At this point there are two key concepts: cardinality and dimensionality. The first refers to the uniqueness of the items in a database column. For example, an e-mail has high cardinality because it should be unique while living in "Paris" has low cardinality because more than one shares that characteristic. As far as dimensionality is concerned, it is recognized as the number of attributes obtained about an individual; when you have information from more than one individual, a database is generated where each attribute becomes a dimension. AI is key in the treatment of multidimensional database since through its artificial neural network it is possible to find connections and patterns with statistical basis. This multidimensional data can be mapped and studied using support vector machines that use algorithms to predict the category of a new data.

See also Entity-Relationship Model

Types of learning in Artificial Intelligence

In general there are 3 types or levels of learning in Artificial Intelligence: supervised, unsupervised and by reinforcement; depending on the specific need of the company, some may be applied.

Supervised learning

Main article Supervised learning

It's about teaching the machine certain rules (training data) to create a profile and recognize the results that those entries meet. For example, if you are taught to identify cats through a group of images, in the future you should be able to identify them on their own. Or, if a brand already has its best user type defined, those features can be used to locate them all.

Unsupervised learning

Main article Unsupervised learning

The machine makes associations and draws new conclusions from the information it already contains: If it already identifies cats then it can study its context and recognize that these are found in chairs and sofas as a trend. Or, for example, you may find that the person who searched for the camera "Sony DSC W830 20.1 Megapixels digital camera" after having searched for "digital camera", "digital camera reviews" and "wi-fi cameras" is 50% more likely to buy than the one that only sought "digital camera," "digital camera reviews" and "digital cameras for sale."

Associations

By rules of association machines can infer for example if a person is prone to buy something with the logic of "those who bought this also bought that". Data analysis by association has two key concepts and are: support and trust (confidence). The first would refer to the number of times an item has appeared in the shopping bag and the second relates the number of times two items have been purchased together. For example, if a person bought toothpaste 400 times and floss 300, and 300 times they bought the products together it means that the trust is 3/4 or 75%, but the association between the two is 100%.

Anomalies

Contrary to the patterns are the anomalies, which should be paid special attention because these are unexpected changes that must be explained to take action. For example, a fraud can be detected if a purchase is made at a location that does not match the person's actual location. However, there may also be beneficial anomalies to make marketing decisions such as being a trend on Twitter and being able to take advantage of fame to induce shopping.

Learning by reinforcement

The machine, based on its own learning process, generates outputs and conclusions that it tests to learn and improve. Reinforcement learning differs from supervised because in supervised man must indicate when the machine is wrong while "by reinforcement" the machine creates its own mental model of the world in which, for example, it decides which poster is most impactful for a certain group of people.

How unsupervised learning works

This learning system is composed of neural networks that function like a brain where each neuron transmits information to others to generate a result. Each artificial neuron has its limits because at the individual level it has certain inputs and outputs, however, if there is a situation with high support and high confidence (confidence) it sends the message to others since given the situation makes them prone to spread the signal. Considering the one-way situation the tickets would be the factors that may influence whether or not it occurs: weather, effort, cost. These inputs are not binary but operate on a grayscale (since sentiment about the weather or effort does not have an answer to or b; for that reason the outputs are a percentage (for example, 65% chance of going to the cinema).

This number of layers and factors create different decision layers that become deep learning or deep learning. This learning combines many layers of information (e.g. level of education, the likelihood of buying pasta and dental floss etc.) to enrich each neural unit and thus provide new outputs and conclusions. In this way, it is no longer the human who establishes these relationships, but from the data the machine creates a learning process. The strengthening and robustness of this learning becomes what was previously called "reinforcement learning".

Browser marketing

Google

Google through its "Google display network" composed of different websites (also called publishers) supports its service/program "Google ads". In it Google receives ads from advertisers to then select the websites (publishers) associated with the ad depending on criteria such as the relevance of the content, the offer price and the revenue it would get. Thus, in Google's targeted advertising model, publishers are used to track users while browsing the Internet (via the DoubleClick cookie whose domain belongs to google) and at the same time to profile users when they visit their pages. For example, if a user who frequently visits a football website will be tainted in the category of "sport" and in the "football" subcategory. This crosses the demographic information that Google owns (such as age, gender, location) creates a user profile that will be used to show you advertising (the behavior targeting method). According to research, 88% of the tags/categories with which an individual is profiled (such as "football") receive targeted ads that are directly associated with the keywords that define them. These tags/categories that define individuals are updated in a 1- and 2-minute range and can be seen on the Google Ads preferences page.

Social media marketing

Facebook

Facebook, like other platforms such as Amazon, use the 1-1 marketing method where it is used: the history of pages visited, information collected by data brokerage firms (such as Experian, Acxiom and Epsilon digital data profiling) and user data and interactions on the platform to profile and advertise them according to their interests.4 Specifically Facebook had an evolution in its targeted ad technology when on May 6, 2015 it partnered with IBM in order to give its users a more personalized and relevant experience. In practice, Facebook's personalized ads have made the platform an indispensable tool for advertisers (92% of marketing companies use it) as it is less expensive than other media and also has wide reach (at least 1.39 billion active users per month).

Instagram

Concerns about the use of AI in Marketing

Business models such as Google's where personal information acquires monetary value raises major concerns about users' privacy. Sensitive categories such as sexual orientation, health, religion and political ideology are being used to display targeted advertising even though in many places it is forbidden to use that information. According to research between 10% and 40% of ads shown to people profiled with these sensitive conditions, they correspond to ads that appealed to those characteristics.

Mary Coombs was the first woman to work at the Leo Computer

Mary Coombs was the first woman to work at the Leo Computer

Mary Coombs (brought into the world 4 February 1929) was the main lady to deal with the LEO Computer.1 Her father, William Blood, believed in women's education and her sister worked in microbiology and bacteriology. Unlike her sister, and unlike others in computing, she had no experience in mathematics or science. The National Museum of Computing documents her contribution. He graduated with a French degree from Queen Mary University in London. He later moved to surrey, when his father became a medical officer of the J. Lyons and Co. catering company. He was clear that women should have their own careers and interests.

Education

In her early years, Coombs attended Putney High School and St Paul's Girls' School. She went on to earn a BA degree with honors in French, with history from Queen Mary University of London.

I work at J. Lyons and Co.

After earning his degree, Coombs began working on J. Lyons and Co. in 1951 temporarily clerical worker—a job he reluctantly accepted while looking for a better alternative. Coombs' math skills soon allowed him to move from the ice cream sales department to the bureau de estadísticas, where he heard that the division working at LEO computers had been looking to hire additional programmers.

The selection process, devised by Raymond Thompson, was conducted as a "computer appreciation course", which consisted of a grueling week of daytime conferences and afternoon writings designed to assess the suitability of candidates for the work on the computer.

Coombs' performance in the computer's appreciation course was stellar, and as a result, she was one of two candidates who were offered a position in the computer division, along with Frank Land. According to Coombs, she was one of the few women who took the appraisal course on the computer, and was the only one, who was offered a job as a result.

Once Coombs officially began working with LEO in 1952, John Grover, one of LEO's first programmers, taught him to program. At first, she was the main lady in the group and worked nearby Leo Fantl, John Grover and Derrick Hemy, utilizing LEO to naturally compute representative finance at J. Lyons and Co. The team then went on to make the payroll for Ford Motor Company using LEO.9 Coombs is recognized as the first woman to work on a commercial computer.

Coombs continued to work for J. Lyons and Co while LEO II and LEO III were being built. She spent most of her time as a supervisor, looking for logical and syntactic errors in the programs that other people had written. He developed programs for the internal use of the company and for external clients as another part of the commercial computer service offered by the company.11 He was also in charge of rewriting LEO II programs to work with LEO III, as LEO III used a different programming language.

J. Lyons and Co. provided a good working environment for Coombs. The company had several sports clubs in which Coombs was involved and even an Amateur Drama Society. However, the company paid her very little, which was a difficulty for her as she financially supported her mother.

after J. Lyons and Co.
Coombs was transferred to English Electric Leo Computers, a joint venture created by the merger of J. Lyons and Co. and English Electric. It was later transferred to International Computers Limited (ICL) when they purchased English Leo Computers. There, in 1964, due to family commitments, he went from full-time to part-time work. He continued to work in the computer business, mainly editing manuals. She briefly taught a computer programming course at Princess Marina Center in Seer Green for disabled residents.

In 1969, when she realized she would not be able to return to full-time work, Coombs left the LEO team and worked briefly for Freelance Programmers, a company started by Dame Stephanie Shirley. After three years of not working, she became a teacher elementary school at a private school. After leaving teaching, he has taught piano, and led the church choir, as well as being up to date with other hobbies.

LEO Computers
Coombs was the first woman to work in the LEO computer business. She and her husband were co-workers there. They finally had a son together. The girl was disabled, which made Mary think she should either quit her job or work part-time. Her daughter died at the age of 6.