How to combine AI with SEO in the organic positioning strategy of brands to achieve the best results. 

SEO and Artificial Intelligence

 Constant updates to the Google algorithm aim to offer increasingly precise, rich and valuable answers to user queries. A good SEO strategy has to know how to deal with these changes, optimizing both technical and content aspects to ensure good organic positioning of the website within the results pages.

In this work, AI is presented as an ideal assistant to perfect tactics, correctly identify the target audience, detect search trends, find ways to improve the user experience and even generate highly personalized content in an automated manner.  

All of this allows us to react quickly and efficiently, matching communication with the interests of the digital community and potential clients.

But could Google penalize using these Artificial Intelligence tools for content creation? As usual, the answer to this question is putting the user at the centre. According to Google sources, if AI content creation does not aim to manipulate positioning, this practice will not be punished within the rankings. Reliable, valuable and people-oriented content will be rewarded by Google with greater organic visibility, whether AI has been involved in its creation or not.

5 Ideas for Using Artificial Intelligence to Improve SEO Positioning

1. Refine the keyword search: To recognize the determining keywords for the business, various AI tools can study the behaviour of potential customers. In addition, they allow drawing comparisons with competitors and tweaking the content generated around these keywords so that it has a better chance of climbing to the top positions in the SERP.
2. Predict trends: Artificial Intelligence and machine learning applied to historical data make it possible to anticipate user behaviour. This knowledge is precious for planning digital marketing actions, anticipating the emergence of trends and the competition itself.
3. Optimize meta-tagging: Meta tags help search engines interpret a website’s content while promoting SEO positioning and providing information to users. Titles, descriptions and other metatags can be optimized in bulk with AI tools, saving time and resources within SEO Content tasks.
4. Respond to visual and voice searches: The new ways users search by voice, through Siri or Alexa, and by images, through Google Lens, are gaining ground over written language. Generative AI can support the development of multi-format content aimed at these queries.
5. Diagnose failures and propose solutions: AI makes locating weak points within a strategy, content or website easier. Once the diagnosis is made, AI also assists in proposing solutions and evaluating them.

AI is presented as a handy instrument to extract ideas, improve strategy and save time on the most tedious tasks. However, in this new scenario, the role of the SEO specialist becomes even more essential. 

Some decisions have to do with the business objectives, the identity of the brand, and the digital path it wants to take that can never be left in the hands of AI. Furthermore, at the level of content generation, you must review the texts created by ChatGPT or any other AI application. 

It is essential to verify that the data and statements made are truthful, that the language is appropriate and that the content is not subject to bias (for example, gender, nationality or social class).

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It all depends on how the tools are used and what is expected. If you expect a code generation wizard-like AWS CodeWhisperer to produce perfect code that you can accept and use wholesale without changes, you will be disappointed. But if you’re using these solutions to complement a developer’s skills, you may be in for a very positive surprise.

The problem is that too many companies expect ‘generative’ to be a magic solution to their problems. As Gartner analyst Stan Aronow highlights, a recent survey highlights that nearly 70% of business leaders believe the benefits outweigh the risks, despite a limited understanding of the precise applicability and risks of generative AI.

Speaking of large language models (LLMs), Willison argues: “It seems like three years ago, aliens showed up on Earth, handed us a USB stick with this thing on, and then left. And since then, we’ve been trying to figure out what it can do.” We know it’s important, and we can feel some of the limits of what LLMs can do, but we’re still in trial-and-error mode.

The problem (and opportunity) with LLMs, Wilkinson continues, is that “you rarely get what you really asked for.” Hence, the advent of rapid engineering as we tinker with ways to make LLMs produce more of what we want and less of what we don’t. “Occasionally, someone will discover that if you use this little trick, suddenly this whole new avenue of skills opens up,” he notes. Nowadays, we are all looking for that “little trick” that leads to programming.

Everything is in the right place

Some suggest that coding assistants will be a great asset for unskilled developers. That could eventually be true, but not today. Because? There is no way to adequately trust the output of an LLM without having enough experience to measure its results. Willison believes that “getting the best results from them actually requires a lot of knowledge and experience. “A lot of this comes down to intuition.”

There are coding hacks that developers will discover through experimentation. Still, other areas are simply not a good fit for GenAI right now. Mike Loukides of O’Reilly Media writes: “We can’t be so tied to automatic code generation that we forget to control complexity.” Humans, while imperfect at limiting complexity in their code, are better positioned to do so than machines. For example, a developer can’t really ask an LLM to reduce the complexity of their code because it’s not clear what that would mean. Reduce lines of code? “Minimizing lines of code sometimes leads to simplicity, but just as often leads to complex incantations that pack multiple ideas into the same line,

This is all good. We are incredibly early in the evolution of AI despite the fact that it has been around for decades. We in technology like to get ahead. We act as if cloud is an established norm when it’s still only 10% or so of all IT spending. Despite the avalanche of investment in AI, it is not even 0.1%.

For Willison, now is the time to try out the different LLMs and their associated coding tools. Our goal should not be to see which one will do all our work for us but rather to discover their strengths and weaknesses and probe and push them until we know how to use their strengths and failures to our advantage.

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Whether you’re new to these tools or looking for new ways to implement them, here’s how AI can help solve common customer service problems.

Prioritize customer needs

The top challenge for customer service leaders in 2022 was prioritizing customer requests. As businesses are faced with increasing support requests, it can be more challenging to analyze incoming queries to identify the ones that require the most immediate attention.

How Artificial Intelligence Solves This Problem

Many AI tools analyze keywords and sentiment in incoming requests to identify those of high importance and then prioritize requests based on urgency level. Once requests are triaged, reps can immediately address high-priority issues.

87% of reps say AI tools that help them prioritize customer service requests in urgency are practical.

Time management

Another challenge that service leaders and agent-level representatives also report needing more time in the day. With reps handling multiple tasks of varying complexity, things add up, and tackling them can be challenging.

How Artificial Intelligence Solves This Problem

Artificial Intelligence can take over manual and routine tasks and automate processes so they happen instantly, without the need for rep involvement. Some tools, like chatbots, can handle entire tasks independently, while others take on smaller tasks to ensure reps aren’t so spread out.

On average, reps reported saving 2 hours and 11 minutes per day using Generative AI to respond to customer requests and 2 hours and 20 minutes per day using chatbots to respond to customer requests; that’s almost 5 hours for reps leveraging both technologies.

Long waiting times

One of the main frustrations of consumers who responded to the survey was the long wait times when seeking help. They expect quick answers from the service staff, and their anger increases the lengthier they wait.

How Artificial Intelligence Solves This Problem

Artificial Intelligence can help service teams significantly reduce the time customers spend waiting for help because tools can be programmed to answer questions.

For example, a machine-learning chatbot can quickly share return information with a customer instead of waiting in a lengthy phone line.

Plus, tools like chatbots make it easy to offer 24/7 support so consumers can look for solutions right when they need them. For more urgent issues, a nightly chatbot can schedule a callback when a human agent is available to reduce wait time the next day.

Lack of customization

54% of consumers expect personalized experiences, but tracking unique customer information and shopper behavior can be time-consuming.

How Artificial Intelligence Solves This Problem

Artificial Intelligence makes personalization possible through automated consumer data tracking and record keeping, helping teams create comprehensive profiles of their customers. For example, a CRM automatically stores customer details, interaction history, and all relevant information in one place to provide context and personalize experiences.

For example, you can enter relevant information about your customer into your preferred Generative AI tool and create personalized service content relevant to their needs, such as product recommendations.

Service professionals say that personalization is one of the most significant benefits of using Artificial Intelligence in their functions.

Collection and analysis of customer feedback

Customer feedback is the best way to learn about your business and the usefulness of your service processes directly from those who use what you sell. But gathering feedback can be challenging, whether you need help figuring out what to ask or spending too much time poring over responses and calculating different metrics.

How Artificial Intelligence Solves This Problem

Generative AI and customer feedback software can work together to streamline survey creation and feedback analysis. Gen AI can help you create the perfect questions to include in your surveys, and feedback tools help you submit your surveys and automatically analyze the results, display critical information, and calculate essential metrics like NPS.

87% of customer service professionals say that AI tools that collect and analyze customer feedback help them improve the customer experience.

Varied communication with the client

How you communicate with customers is essential, whether by email, over the phone, or in person. A problem can arise without a consistent communication style between support representatives. Regular support users need clarification on the different tones of reps, and different communication styles make it difficult to compare post-support feedback.

How Artificial Intelligence Solves This Problem

86% of reps say generative AI tools that help them write responses to customer service requests are somewhat or very effective.

Your go-to tool will help you stay consistent across all aspects of customer communication, from call scripts to social media response templates. You’ll have a cohesive and consistent strategy, and reps will also have all the templates they need to communicate with customers across all channels.

Plus, each template you create can be further customized for each client you share it with, helping you keep personalization a priority.

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The sentence has been published by the San Francisco-based nonprofit Center for AI Safety. Among the approximately 350 signatories, including researchers, executives, and other personalities, there are also Geoffrey Hinton and Yoshua Bengio, two of the great pioneers in the development of artificial intelligence.

This is not the first time that the scientific community has warned about the dangers brought about by the development of artificial intelligence, which is currently at its peak thanks to the enormous success achieved by the talking machine ChatGPT.

A couple of months ago, important executives, scientists, and humanists signed a more extensive letter requesting a stoppage in developing new technologies to develop security measures that prevent their harmful effects, ranging from the destruction of jobs to the generation of misinformation.

Sam Altman, CEO of the company behind the creation of ChatGPT, has been warning for days that artificial intelligence should be treated similarly to autonomous weapons. This was made clear last week during a fleeting visit to Madrid, in which he advocated the creation of an international organization similar to the IAEA (UN International Atomic Energy Agency), which controls the development of the AI.

Nor is it the first time the scientific community has pointed out that artificial intelligence can put humans at risk of extinction. A study published by researchers at the University of Oxford and Google shared a study late last year stating that, eventually, when machines reach a level of sophistication that they are nowhere near, they could end up competing with humans to meet their energy needs.

“In a world with infinite resources, what would happen would be extremely uncertain. In a world with finite resources (like ours), there is inevitable competition for these resources,” said Michael Cohen, a researcher at the University of Oxford, in an interview with Vice conducted in this regard. “If you compete with something capable of surpassing you at all times, you should not expect to win,” the expert settled.

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In the context of digital transformation, particular importance is given to artificial intelligence. Last but not least, the interface between banks and their customers will change massively due to the technological possibilities associated with it.

Researchers from the famous Stanford University have launched a long-term study to examine the opportunities and possible threats. The project aims to investigate the effects of artificial intelligence over 100 years and develop scenarios of how people will live and work in the future. The first results are now presented in a report on living and working in 2030.

Understand artificial intelligence as an opportunity

Although many expectations of artificial intelligence (AI) have been disappointing in the past, there is steady progress to report. However, only some of them are so spectacular that they catch the eye of the public.

High-profile success stories include IBM’s Watson winning a US TV quiz or, more recently, Google’s AlphaGo software winning the Go board game against the 18-time world champion from Korea.

A professor recently said there wouldn’t be a global AI combining everything humans can do for the foreseeable future. However, AI already significantly impacts society and businesses; revolutionary changes are expected shortly.

The Stanford University report states that machines with independent long-term goals or intentions have yet to be developed and will remain so for the foreseeable future.

However, many challenges remain. For example, how AI will change the labour market and how to reap the economic benefits of new technologies for society.

Eight central research areas of artificial intelligence

The researchers presented their results divided into eight main artificial intelligence research areas. They examine the opportunities and risks for each area.

Transport is the first area that will be permanently modified by AI. The most well-known keyword in this context is “autonomous driving.” With e-mobility and carpooling, there will be a wide range of effects on the automotive industry and urban and transport planning.

Smart home technologies and robots are entering the household and office sector, which will communicate with each other and people through natural speech and a wide range of sensors. Mail delivery, house and office cleaning, and security services will be part of their standard services.

Researchers believe that significant changes are possible in the healthcare industry. Knowledge-based algorithms could take care of the patient’s medical history, i.e., the comparison of symptoms with the characteristics of the disease, and thus help doctors to establish a diagnosis. Other approaches, some already in use or undergoing clinical trials, include health monitoring using mobile apps or surgical robots.

Improved cameras and drones can be used for surveillance to establish public security. Unique algorithms could detect financial fraud early, and predictive policing tools could use various data to estimate the likelihood of crimes in certain areas.

The world of work will also change massively. However, it currently seems complicated to predict whether the consequences will be mainly positive or negative. It seems evident that machines will take over various tasks and that the existence of entire professional groups is threatened. They range from truck drivers to radiologists and lawyers.

It is feared that many people will no longer be able to live from their work. This raises many questions whose answers are less economic than political, such as a primary income granted by the state.

The following report will be published this year

As with all projections, whether the researchers’ predictions will come remains open. The past of artificial intelligence has shown more than once those changes are somewhat erratic than continually predictable. Therefore, a new report on the current state of research will be published this year.

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Since “Snapchat,” the app that went viral for its 24-hour format before Instagram launched “Instagram Stories.” At that time, Snapchat launched its face swap filter. Do you remember? It’s scary how everything evolves.

The evolution of fake faces: What are “deep fakes”?

Recently, a video of actress Jennifer Lawrence receiving an award and giving a speech went viral on networks. Well, what went viral was the actress giving the speech but with the face of actor Steve Buscemi. What madness! This video became a clear example of how fake news is no longer just text but fake faces, better known as Deepfakes. 

Deepfake technology allows you to create completely fake photos from scratch. But, there have been cases like “Katie Jones,” a profile that claims to work at the US Center for Strategic and International Studies but was actually created to carry out espionage operations abroad.

How are deepfakes created?

The “deep fakes” are created through artificial intelligence, which works through an encoder that takes thousands of shots of the face of the people who want to fake it. 

The function of this algorithm is to identify and learn the similarities between faces and reduce them to shared common characteristics through an image compression process. Then a second algorithm called a decoder comes into operation, responsible for recovering the compressed images.

To carry out face swapping, the encoded images are entered into the opposite decoder so that it reconstructs the other person’s face. That is, an encoded image of face A is fed to the decoder trained on face B. 

The decoder is in charge of reconstructing face B with the expressions and orientation of face A to obtain a clear and super realistic image.

Fake But Realistic Faces: The Artificial Intelligence Revolution

But, obviously, human beings were not going to stop there. Artificial intelligence must evolve much more. Artificial intelligence can create an entirely false face portrait of someone who does not exist.

We are talking about the “This person does not exist “website; it is a new demonstration of how easy it can be to create “personas” that are not real. Its creator, Phillip Wang, is a software engineer fascinated with GANs. His main objective was to set up a site where he could show the capabilities of the GAN: fake faces.

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At the Mobile World Congress (MWC), in his inaugural speech, the president of the important SoftBank Group, Masayoshi Son, stated that, in thirty years, artificial intelligence would be more intelligent than the human brain. On the same occasion, Peggy Johnson, executive vice president of business development at Microsoft, stated that artificial intelligence would become the transformative technology of our time.

Although not everything is congratulations on the future of AI, the architect of the World Wide Web, Sir Tim Berners-Lee, in a talk he gave in April at the Innovate Finance Global Summit, offered a nightmare scenario where artificial intelligence could in the coming years become the new Masters of the Universe, as in the novel. 

The Bonfire of the Vanities, creating and managing many companies better and faster than human managers. Berners-Lee described a world where artificial intelligence-based systems were beginning to develop their decision-making capabilities and explained the impact this could have on the functioning of our economic and financial system.

Artificial intelligence and its associated technologies, such as machine learning and deep learning, together with robotics, are spreading across all industrial sectors, especially in the technological and financial sectors, but also in the health and healthcare, while transforming the world of media, making IT and communications technologists the most sought-after employees to the detriment of salespeople and strategists.

A New Boss: Artificial Intelligence?

Artificial intelligence is changing how business leaders do their jobs, from who is hired to how they are evaluated and promoted. Above the managers, there is now a new boss: artificial intelligence. 

The same technology that underlies a navigation app to find the most efficient route to our destination or makes an online store recommend products based on previous purchases is about to transform offices, promising new answers to questions following: how do you find the best candidates for a job? How do you get the best performance from your employees? And how do you retain the best employees?

The “automatic” performance of these functions by these systems raises many questions that are still unanswered. Privacy is the number one concern when tracking employees, especially concerning their personal behavior. 

The systems that carry out the selection of personnel also raise some questions. For example, Entelo’s system favors candidates with a large network digital footprint, while SAP’s system might prefer those whose characteristics better match those of previously hired employees.

Fourth Industrial Revolution

The new catalyst for artificial intelligence is deep learning (DL), which has dramatically improved the capabilities of AI systems. In 2017, two crucial phenomena began to crystallize. 

First, the considerable expansion of the field of action of AI systems covering areas such as health care, agriculture, telecommunications, distribution, and finance, as well as its combination with other emerging technologies such as systems for the Internet of Things (IoT) and analytics for big data. 

Second, in 2017 the prototypes of hardware accelerators appeared to improve the performance of the algorithms. Deep learning neural networks for machine learning are the most successful innovation in AI.

We are facing a fourth industrial revolution that will make all companies and businesses revolve around software. AI is the main disruption agent in this new digital transformation phase. 

AI software is becoming more accessible, mainly thanks to the ethics of code sharing and the dissemination of research articles. The entire libraries of software algorithms for machine learning and deep learning are shared openly to stimulate progress in this field and to reach the level of artificial general intelligence; that is, the machine behaves just like a human being.

AI offers great potential for applications in combination with other emerging or existing technologies, such as big data analysis, natural language understanding, speech translation, and system optimization. Marketing departments use advanced analytics tools that focus on customer behavior, customer logic, and optimization of marketing actions. Many of these tools and technologies use artificial intelligence components. 

AI has always been present in these environments. Still, as the volume and diversity of data have grown and cloud storage has become popular to increase computing power and data storage rapidly, there has been a resurgence and integration with analytical marketing.

The Debate is Served

Concepts like superintelligence or smart algorithms make people uncomfortable, but analytic machines with incredibly fast cognitive performance are emerging.

But collaborating with robotics for automation is where artificial intelligence is taking center stage. ICTs are increasingly oriented toward the automation of tasks and intelligent multisectoral solutions, sparking the debate that divides European society and, in particular, on the advantages and disadvantages of the progressive automation of industrial manufacturing processes and the service provision processes, with the consequent disappearance of certain jobs and the creation of new ones with their complementary skills and abilities.

The McKinsey Global Institute estimates that 30% of the tasks of most occupations or jobs are repetitive and can be automated using a robot that, running pre-established software, interacts with the processes, production, and service provision. 

Under this concept, thousands of robots were installed in 2017 to serve customers, the so-called chatbots, and to simplify and optimize many companies’ back-offices.

In this deployment of new solutions, we must not forget that the subjects of the emerging cognitive society are social beings for whom technology is a tool we define, control, and manage within a cultural and social concept. 

As in previous industrial revolutions, digitalization comes with opportunities and risks that we must consider with some perspective since the most optimistic experts suggest that the professions will not be fully automated but only in certain specific tasks. Indeed, a worker generally performs a set of tasks, and a robot will not completely replace his work.

Furthermore, in a broader concept, it is obvious that automation can be an opportunity for Europe. A worker, helped by a robot, can concentrate on tasks that require experience, judgment, intuition, or creativity so that technological progress will create new occupations and make work more interesting and help improve employee productivity. 

Therefore, we should not look exclusively at the most unfavorable scenarios when making decisions on this issue.

Current Applications

In combination with artificial intelligence, robotics solves problems and provides creative solutions in fields as inaccessible as medical rehabilitation.

While personal assistants, such as Siri or Cortana, are showing signs of true artificial intelligence, although their use is very uneven worldwide. Some of these conversational bots have been incorporated into dialog systems as automated virtual assistants, and companies are using them to replace the function of call centers. 

This system can respond to the user and help him in multiple activities, such as learning, searching, remembering, connecting with other systems, or integrating services. The history of this technology dates back to 1966 when a device called Eliza could mimic the responses of a psychotherapist and simulate a human conversation. Nevertheless,

Disturbing applications also emerged in 2017: after the presidential elections in the United States, news appeared about new digital assistants and the use of software agents from chatbots to botnets through Facebook or Twitter to massively shape or alter an opinion. 

The voters in the audience influence political events in the real world and gain political power. Does the software make kings or elect presidents?

The International Data Corporation (IDC) has already forecasted that in 2017 the largest area of ​​technology spending in Western Europe will be cognitive applications ($516 million), including process applications and cognitively enabled applications that automatically learn, discover, and make recommendations or recommendations. 

Predictions. On the other hand, cognitive software and artificial intelligence platforms that provide the tools and technologies to analyze, organize, access, and offer advisory services based on a range of structured and unstructured information, will see investments received reach three hundred and fifty million dollars this year.

Artificial intelligence is currently the focus of many technology companies. It would seem that almost all the main players in the technological world are already investing in their own AI, acquiring companies specialized in this area, or doing both.

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Perhaps, for this reason, Artificial Intelligence projects are still not reaching production phases as much as they should. As Harvard University psychologist and professor Howard Gardner point out, 85% of them fail, leaving teams frustrated. 

In this context, Kepler Data Tech points out eight steps that any company must take to scale the use of AI throughout its organization:

Data Infrastructure

Every company should consider the public cloud as an environment to build their infrastructures. The primary public clouds invest more than 90 billion dollars in research and development, guaranteeing an infrastructure with capabilities to assume future situations and needs.

Dedicated Resources

Dedicating specialized people to this data platform is necessary by hiring or relocating those key employees. Due to their technical and organizational knowledge, they can lead the initiatives.

Organizational Silos

Generate a culture around data that avoids organizational silos since this prevents the reuse of project investments between one business unit and another.

Data Silos

Avoid data silos so that a functional disconnection does not occur since data loses value over time. We believe that the most appropriate approach is to have them as close as possible to where they are produced and to make a more federated model of data access ( data mesh ), which allows the use of data in the context where it is generated and also be reused in other areas for specific use cases. Data mesh is a natural evolution for organizations that are scaling AI use cases.

Value Proposal

It isn’t straightforward to analyze what a use case will contribute to a company, but we must consider that working with data is iterative and experimental. The companies that succeed are the ones that fail quickly and quickly move on to another goal. 

At this point, we always recommend incremental interactive approaches, where you explore the data, identify a limited data pool, perform a proof of concept, extract insights, and make decisions based on the results of your research—this project.

AI Governance Model

The company’s data model will depend on its organizational model. There are highly regulated companies in which decision-making is highly centralized, and others can delegate decision-making to other business areas, which leads them to be very independent. It is an excellent dilemma between speed and control over data. 

Despite this debate, there are a series of minimums at the level of security in design, privacy in design, and management of services. All of this must be automated and centralized but capable of supplying the different areas.

Access to the talent that allows data to be used appropriately is currently very complex and scarce. 46% of Spanish companies experience difficulties finding the digital profiles they need and are looking for. To counteract this, companies are starting to make analytical career plans related to new technologies, but it is still an issue that has to be clearly defined in many companies.

Effective Implementation

It is necessary to align efforts in AI and automation of processes that help from the business point of view. Small experimental projects that have been proven to work remain in the drawer and are useless if an adequate implementation or start-up is not achieved, which is the added value of this type of project. 

The public cloud multiplies the capabilities of artificial intelligence. Still, a series of essential components are necessary to know and know how to use to prevent these silos from being generated or those experiments with AI from being left in the drawer. It can do many things but must take some components around the basic techniques and must take some parts around the basic techniques into account to make a scaled and actual use in AI.

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Of course, the machines are not perfect; sometimes, they fail. But lately, we are discovering something worrying: sometimes computers fail as humans fail. 

For a woman and a man with the same expression, AI systems may tend to believe that she is happy and he is in a bad mood. We call this type of error bias, including racist, sexist, and ableist tendencies that can end up harming people.

The Face as a Mirror of the Soul

To study these biases, we will focus on a specific application, the automatic recognition of emotions in photographs.

First, we need to clarify what we mean by “emotion.” The most used classification is based on six basic emotions: fear, sadness, happiness, anger, disgust, and surprise. Psychologist Paul Ekman proposed this classification in the 1970s.

These emotions are more or less universal and recognized by everyone. However, it has also been shown that they recognize each other somewhat better between people of the same social group, gender, age… We do not all express ourselves precisely the same, nor do we read the expressions of the rest in the same way. Even without realizing it, we are biased.

These differences are seen in many contexts and sometimes turn into stereotypes and prejudices. For example, we expect women to be more happy than angry, and the opposite is true for men. And this is reflected on the internet, where the photos include, above all, smiling women.

On the other hand, for an artificial intelligence system to learn to distinguish these emotions, we also need to think about how people understand them. 

The face is only one part of a very complex puzzle. Gestures, posture, and our words also contribute… Although work is being done to resolve all these modalities with artificial intelligence, the most popular and versatile form is recognition based on face photos.

How Does an Artificial Intelligence Learn?

Creating bias-free artificial intelligence is quite a challenge. And it all starts with how we make this technology ‘learn.’ We call the field of artificial intelligence dedicated to this learning machine learning. Although there are many different forms of knowledge, the most common is supervised learning.

The idea is simple: we learn from examples. And artificial intelligence needs to know what we want to obtain for each example. To learn to recognize emotions, we need many photos of faces with different emotions: happy, sad, etc. The key is that we must know what emotion appears in each image.

Later, we passed the photos and their associated emotions to artificial intelligence. Through a learning algorithm, the system will learn “only” to relate the images with the emotions that appear. Picture by picture, we ask you to predict an emotion: if it’s right, we move on, and if it’s wrong, we adjust the model to correct this case. 

Little by little, you will learn and fail fewer and fewer examples. If we think about it, it’s not that different from how we humans learn.

As you can see, examples are essential in this process. Although advances allow us to learn with few examples or errors, a large and well-labeled set of examples is vital to achieving good artificial intelligence.

Unfortunately, in practice, it is common to have examples with errors. In our case, they would be from faces labeled with the wrong emotion to photos without faces or animal faces. But there are other problems, sometimes more subtle and worrying: racism, sexism, ableism…

When the Algorithms Go Wrong

As you can imagine, if our examples are biased, the machine will learn and reproduce these biases. Sometimes it will even multiply the effect of the biases. For example, artificial intelligence will likely confuse skin color with mood if we only have angry dark-skinned people and happy light-skinned people in our photos. He tends to predict anger whenever he sees dark-skinned people.

Unfortunately, this is not just a theory. It has already been shown, for example, that facial analysis systems to recognize gender fail more for black women than for white men and that they regularly make mistakes with people who are trans or non-normative in appearance.

One of the most notorious examples was when in 2018, an artificial intelligence system mistakenly identified 28 US members of Congress as criminals. Of the politicians identified, 40% were people of color, even though they only represented 20% of Congress. 

All this was because the system had been trained primarily on white people and confused people of color.

Detecting and reducing these biases is a very active field of research with a tremendous social impact. Many of them are subtly related to several demographic factors simultaneously, making it a complex analysis. 

In addition, all phases of learning must be reviewed, from data collection and its measurements to the final application. And usually, it is not the same people who work in each phase.

One Database to Include Them All

Let’s go back to emotion recognition. On the internet, there are many databases of emotions already labeled. Unfortunately, the most extensive databases also have strong sex/gender, race, and age biases.

We must develop diverse and balanced databases on which to work little by little. We need to include all kinds of people in our databases. In addition, all of them must be well represented in each emotion.

Finally, if we want to collect data without bias, we must think about the whole process. All phases, from data collection to the final tests of artificial intelligence, must be carried out in a careful and accessible manner. And it is necessary to involve people who can recognize and point out possible biases in all of them.

And All This For What?

This whole business of recognizing emotions may sound abstract, but it already has critical applications. The most common is assistive technology, such as automatic photo descriptions for the visually impaired. It is also already used in domestic robots. 

I can even apply it in medicine, where it has been possible to automatically recognize pain in newborns who sometimes do not express it through crying.

In any case, the study of biases in artificial intelligence goes beyond emotions. The technologies we develop have a significant impact on people’s lives. We have a moral duty to make sure they are fair and that their effect on the world is positive.

We want to build artificial intelligence that we can trust that makes us smile.

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The growing influence of artificial intelligence in the banking sector reduces operational costs, increases customer support and means automation.

Artificial intelligence is an excellent example of software with multiple uses in almost every industry. This is particularly true about the banking sector, as it happens with lots of utilities that can help optimize unique processes.

How AI is Used in Banking Sector

In the age of technological advancements, Artificial Intelligence has led to taking centre frame. 

With every business getting confused by technological progress in the digital era, customers must get a seamless event for their banking process. 

Since the increase in the effective population and rise in disposable income act as incentives for increasing the need for digital banking. 

AI in banking also encourages users to choose loan products at an attractive interest rate. The AI technology in the banking sector provides banks to update rules automatically and work under being administrative compliance.

Banking Sectors Using AI in Several Departments

Artificial Intelligence has led the banking industry by storm with its contributions and services. Not extended ago, your most adjacent vegetable vendor was receiving only cash as a mode of payment. Now you will see QR scanners for online transactions at nearly every shop you visit, which shows us that technology selection is immediately due to high internet invasion.

Banking Customer Care

Utilizing artificial intelligence to manage customer care responsibilities is commonplace in several different applications. When it happens to the bank, this report is just as valid.

Automated answering systems assist consumers with low-complexity problems, such as monitoring their balance, transferring money, etc. If artificial intelligence can’t feel the request, it will automatically attach them to the relevant consumer service representative.

AI Chatbots

It is one of the essential use examples of AI in banking. AI-powered chatbots are improving the process of how businesses render services to their consumers. Chatbots are serving clients in many forms.

Lending Via AI

For more critical financial institutions, manually reviewing each loan application can take a notable amount of time. Instead, they can use AI systems for banking to better screen applicants who don’t satisfy the required standards for a loan.

From here, those who can provide the basic principles can then have their applications evaluated by a banking professional. 

Consumer Experience

AI banking apps can do miracles. Intelligent mobile apps can follow user management and give valuable personalized recommendations to them. In addition, users can do countless transactions from their smartphones. Secondly, AI-powered banking apps will dispense services, offers, and penetrations based on user search guides.

Fraud Detection

With technological progress, even scammers discover new methods to get their plan within and fool the system. 

Therefore, to assure that we decrease the rate of fraud, banking sectors have begun using ML-Driven Fraud Analytics. Machine Learning operates on the idea of learning from experience. 

Data Collection 

Artificial intelligence-based applications can efficiently perform data gathering and analysis methods. Verified data collections are required for AI machines to interpret the data. 

AI-powered mobile banking apps secure and build a proper learning method for improving the overall user experience. After an appropriate analysis of the data, the user experience can convert further personalized.

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