Conoce las diferencias entre deep
learning y machine learning

La inteligencia artificial avanza constantemente y es abrumador mantenerse actualizado. Sin embargo, si deseas simplificar los conceptos de inteligencia artificial, puedes reducirlo todo en dos principales: machine learning y deep learning. ¿Acaso no son lo mismo? A veces pensamos eso, pero es un error y es fundamental saber las diferencias.

Comprender las diferencias entre ellos puede ayudarte a comprender cómo Facebook reconoce rostros en fotos, cómo funcionan los vehículos autónomos y cómo Netflix determina qué programas podrían gustarte. Acá te explicaremos las principales diferencias entre machine learning y deep learning.

Primero que todo, ¿qué son?

En realidad, el deep learning es solo una forma de machine learning y ambos son formas de inteligencia artificial. Eso significa que todo deep learning es machine learning pero no todo machine learning es deep learning. Ambos comienzan con entrenamiento, prueba de datos y un modelo. Ambos pasan por un proceso de optimización para que el modelo se adecúe a la data. Ambos manejan problemas de regresión y clasificación.

Ahora que ya explicamos sus similitudes fundamentales, podremos explicar mejor qué son. Los algoritmos de machine learning se dividen en supervisados y no-supervisados. Los problemas supervisados se dividen en problemas de clasificación, que predicen respuestas no-numéricas y de regresión, que predicen respuestas numéricas.

Los algoritmos no-supervisados se dividen en agrupamiento (identificar grupos de objetos similares), asociación (identificar secuencias de objetos) y reducción de dimensiones (como proyección y selección y extracción de características).

Por otra parte, deep learning es una forma particular de machine learning donde el modelo tiene más de una capa entre el problema y la solución. Esto significa que un algoritmo de un modelo de deep learning puede determinar si una predicción es acertada o no, lo cual significa que es más avanzado que los algoritmos de machine learning básicos. Están diseñados de forma que analizan la data a través de una estructura lógica similar a la de un ser humano.

Un maravilloso ejemplo de deep learning es AlphaGo, de Google. La compañía creó un programa que incluía una red neuronal y aprendió a jugar el juego de mesa Go, que requiere profundas habilidades de intuición. AlphaGo jugó contra jugadores profesionales y el algoritmo llegó a jugar a un nivel sin comparación sin que le dijesen cuándo y cómo hacer una movida específica.

Las diferencias

Primero que todo, como dijimos anteriormente, el deep learning es una forma más avanzada de machine learning. El machine learning típico usa algoritmos para analizar la data, aprender de ella y tomar decisiones basadas en lo que aprende. El deep learning, en cambio, organiza algoritmos en distintas capas. Esta estructura se llama “red neuronal” que aprende y toma decisiones de forma autónoma. Eso significa que, aunque todo machine learning es inteligencia artificial, el deep learning es la base de casi toda la inteligencia artificial similar a la lógica humana.

Eso significa que el deep learning puede descubrir elementos que clasificar, mientras que el machine learning requiere que dichos elementos se le provean manualmente. En consecuencia, el deep learning requiere maquinaria de punta y grandes cantidades de datos para entregar resultados que se asemejen a la lógica humana.

5G is the network that will revolutionize medicine

Technological advances are not a secret society, the intervals between one and the other are getting smaller and today you can already find the 5G network knocking on doors of sectors as important as medicine.

How does 5G promise to revolutionize medicine?

When we talk about 5G networks we mean the new wave spectrum that will facilitate the release of the now partially collapsed 4G network. We know that the custom is to associate these networks with mobile devices or, failing that, computers. However, the Internet of Things (IoT) is a reality and in the medical field it now becomes a priority to have good connectivity and speed to optimize processes in the digital age. The 5G network seeks to decongest the 4G network by presenting a greater storage capacity, with a speed that promises to reach up to 10 GB per second, which is more than enough to work as the new “smart cities” should work with smartphones, cars smart, smart homes and, among those things, medicine with smart devices and equipment.

Let’s imagine a situation

You are in a rural area, far away from the city, and casually you get sick. What is your option? Right! Taking your car or any means of transportation to travel to the nearest doctor or hospital, although that can often mean more than an hour’s journey that involves an investment in time, money and, most importantly, energy (which is the resource that is most appreciated when faced with a disease). 5G networks, within telehealth, become the salvation by giving the user the convenience of receiving attention directly from your home, with the help of your smartphone or any device with an internet connection capable of sending and receiving data.

Here is the following question: With a 4G network, you cannot use telehealth as well? Well, the answer is: Yes. However, when this remote monitoring and all the files that these require are added to the networks of the health companies, they generate overhead in the network, which decreases the speed and, with this, the productivity. Taking into account that these are hundreds of patients a day.

Among the things in which 5G promises to be a clear improvement in the health sector are the rapid transmission of large multimedia data, such as those corresponding to MRIs and other machines of the type. When the network is slow, these files may not be sent (or sent damaged), thus delaying the processes and causing specialists to see fewer patients in the same amount of time. Another thing is the improvement in telemedicine, as well as improvement in augmented reality, virtual reality and in space computing (which is now limited in use). Finally, real-time monitoring will be more reliable for both doctors and patients.

Learn to calculate the profitability of
your marketing campaigns

The return on investment is essential when evaluating your marketing movements and here we explain how to calculate it as a professional.

First of all: What do we mean when we talk about ROI? It is nothing more and nothing less about the return on investment (Return On Investment in English), that is, the profits that are generated with a certain marketing strategy, can be online or offline.

To make it easy we explain it this way:

For every dollar you invest in promoting a product, how many dollars are you earning? What has been the development and effectiveness of your marketing strategies?

Learning to calculate the ROI represents a fundamental step that will make a difference when making decisions to advertise and promote the products or services of a company because the main objective of an investment in marketing campaigns is to obtain benefits that can be translated into sales or brand positioning.

ROI can be a positive percentage, if effective strategies and campaigns are being applied, or negative, which means that your company is losing money and you need to rethink your marketing decisions.

Calculating the ROI in digital marketing campaigns, whether in Facebook Ads and Google Adwords, content campaigns or SEO can be a simple and automated process, although it requires the investment of time and attention in the design and execution of strategies. However, what happens when you want to measure campaigns that are not automated? If you don’t have the answer, ask!

An effective strategy to obtain the information is to conduct surveys on your website or via personalized (such as phone calls) to discover what motivated your customer to make the purchase, so you can verify what percentage of sales is due to your marketing strategy and through from which channels your customers received the information.

Another automated way to calculate ROI is used by many companies when working with influencers or allied web pages and they are personalized discount codes, such as:

“Enter the Atlantis University website and receive a discount of XX%

in the Master in Business Administration using the code ATLANTIS2020 ”

With this mechanism of codes, it is possible to calculate the return on investment for each individual that participates in the campaign and disseminates the information with their personalized code. If any of the promoters that the company is using does not reach the public, a strategic modification is made and a diffuser is selected that meets requirements such as: having a consistent image with the values ​​that the company manages, reliability and a common target.

To calculate the return on investment, use the following equation: Subtract the investment from the benefit and divide the result by the investment.

(BENEFIT – INVESTMENT) / INVESTMENT

If your investment in a Google Adwords campaign is $ 10 and you get a profit of $ 20, it means your ROI is 1%. That is, you invest a dollar, recover your dollar and get another profit. Now that you know how to calculate the ROI of your campaigns. Are you ready to evaluate your campaigns intelligently?

Challenges in Healthcare Management

Health and management are areas that go hand in hand. Globalization and the digital age impose new challenges when performing both and in this article we explain why.

Management in the area of ​​health carries an especially important responsibility since it is a dynamic that, beyond uniting the challenges of leadership and management of a work team, encompasses everything that involves decision-making in an area as decisive for human life as health and medicine is. In this world, a simple action can affect the lives of hundreds, thousands or millions of people and is something to take into account when forming in order to hold certain positions in the industry. An ethical and responsible management is attainable with the appropriate tools and information to develop the specific competencies necessary to develop this role.

Health management presents clear functions at all levels, among them we find strategic planning, setting organizational goals and the steps to follow to achieve them, taking into account resources and deadlines; also, at the organizational level, it is the responsibility of the management to define the departments and competences of the staff, as well as direct their actions towards the achievement of the previously stated objectives.

Other functions related to human resources is the ability to select the most suitable personnel for the positions, in a sector so sensitive to health, the ability to respond and professionalism between one and the other can make a difference when solving A situation under pressure. In turn, he must be in charge of wisely investing the organization’s resources, both financial, as well as temporary, material and human.

A manager must be a leader, the area of ​​health is constantly evolving and, with the digital age and globalization, must be responsible for motivating staff to research and train periodically in new discoveries and trends to seek to couple the practices of health centers towards more ecological and efficient dynamics adapted to the second decade of the 21st century, as well as, should focus on the resolution of the millennium goals and mandates raised by institutions such as WHO and the UN, depending on build a world where health is as accessible and human as possible.

For this, in our degrees, we seek to reinforce and stimulate the analytical, technical and interpersonal skills necessary to carry out such positions with the energy, ethics and responsibility involved in leading in such a determinative field in the history of humanity and collective well-being. . At the same time, although it is well known that these skills are fully developed over time and the practice applied at the labor level, studying the key and cutting-edge knowledge in the sector is a step in favor to acquire the base that gives rise to expected performance by a Health Manager.

Don’t Say I: How to be a true leader starting
from how you speak

“Team” and “together” don’t have an “I” in them. That’s one of the best ways to remind leaders about the importance of teamwork. This cannot be stretched enough. Successful leaders know they are not working alone. They are a part of the team and they must inspire others with their leadership.

That’s why a true organizational leader in the 21^st Century doesn’t use the word “I.” You don’t need it to express issues you may see or for saying what you think. You don’t even need it to enforce your authority as a leader. Heads up: If you must remind people that YOU are in charge… You’re doing something wrong.

Avoid the word

Instead of “I,” use other terms such as “we” or “us.” Also, in certain circumstances, you can just remove the pronoun. Instead of saying “I see an issue here” you can say “Isn’t it better if…?” If people see you’re a “me, myself, and I” type of person, your leadership will suffer. It will only be recognized as long as you recognize others.

Leadership is not only about the leader but about the leader and the followers working together. Using the term “I” indirectly imposes a top-down view that people may not like. Using other expressions says that you are leading for your team and not using them as mere pawns.

Avoid it when it undermines you

In general, saying “I” may feel like emphasizing your authority. However, on certain occasions, it weakens you. When you say “I think” or “I believe” it may show you don’t trust what you’re saying. Saying “I think” may suggest confidence issues. Self-assured individuals just say things. They don’t need to mark down that what they say is what they think. It’s redundant, self-doubting, and unnecessary. Throw it away.

Other moments to avoid it

“I can’t” is possibly the worst words that can come out of the mouth of a leader. It limits your actions, and it generates distrust in your leadership. If you don’t believe in yourself, how can others believe in you? How will they believe in the vision you have for the team? Don’t say “I can’t”, say “I will.”

“I don’t care” is another term to avoid at all costs. It means you don’t have empathy or that you don’t have an opinion. It shows a lack of regard and interest which is always negative for a leader. A leader must show interest in the ideas of his team to foster innovation and showing he is passionate about the plans he has for the organization.

Last but not least: “I’m in charge.” We mentioned it before: if you have to say to your team that you’re the leader, you’re not. Yes, there can be a moment or two when saying those words can be necessary, but avoid them as much as you can. Saying them to get people to do what you want is not the way to go. That means your crew is working from fear and not from motivation, and that’s not the best idea.

In conclusion

A good leader works with his team. Leadership is not about yourself but all your team. The perception your team will have from you is built based on what you say and what you do. If what your words indicate you only care about yourself, you’re going in the wrong direction. A good leader is a good communicator. To inspire your organization, start by changing the way you talk to your team.

Understanding the differences between Deep
Learning and Machine Learning

AI advances constantly and it’s overwhelming to stay up to date. However, if you want to keep things simple you can reduce AI innovations in pretty much two notions: machine learning and deep learning. Aren’t they the same? Sometimes we have that impression but that’s a mistake, and is vital to know the differences.

Understanding the differences between them can help you understand how Facebook recognizes faces in pictures, what drives self-driving cars (pun intended) and how Netflix determines which shows you may like. Here we’ll let you know the main differences between machine learning and deep learning.

First things first, what are they?

Actually, deep learning is a particular type of machine learning. Both are forms of AI. That means that all deep learning is machine learning but not all machine learning is deep learning. They both begin with training, test data and a model. They both pass through an optimization procedure to lead the model to fit the data. They both handle regression and classification problems.

Now that we went through their fundamental similarities, we can explain better what they are. Machine learning algorithms are divided between supervised and unsupervised. Supervised problems are also divided into classification and regression problems. Classification problems predict non-numeric answers and regression predict numeric answers.

Unsupervised learning is divided into clustering (identifying groups of similar objects), association (identifying sequences of objects), and dimensionality reduction (such as projection, and characteristics selection and extraction).

On the other hand, deep learning is a particular form of machine learning where the model has more than one hidden layer between the problem and the solution. That means that a deep learning model algorithm can determine if a prediction if accurate or not, which means it’s more advanced that basic machine learning algorithms. They are designed in such a way they analyze data through a logical structure that resembles the one of a human being.

An awesome example of deep learning is AlphaGo, from Google. The company created a program that had an engrained neural network that learned to play Go, the board game, which requires deep intuition. AlphaGo played against pro players, and the deep learning algorithm played at an unparallel level without being told how and when to make a specific move.

The differences

First of all, as we said before, deep learning is a more advanced subset of machine learning. Typical machine learning uses algorithms to analyze the data, learn from it, and make decisions based on what it learns. Deep learning, instead, organizes algorithms in different layers, a structure called “neural network” that learns and makes decisions independently. That means that even as all machine learning is AI, deep learning is the one that sustains almost all human-like AI.

That means deep learning can discover features that it needs to classify, while machine learning needs to be provided with these elements manually. In consequence, deep learning requires high-end technology and large amounts of data to deliver exact, human-resembling, results.

How to migrate into the cloud and not die trying?

Not so long ago, using the cloud was pretty much a secondary option in the IT industry. However, in the last couple of years, cloud technology became the first option for many companies, considering the flexibility, cost-efficiency, and scalability it provides.

All that means that basically, every company is migrating to the cloud sooner than later. However, cloud migration is not quick nor straightforward. Mistakes in the process can endanger the whole IT infrastructure of an organization. That shouldn’t stop any organization from migrating to the cloud, but it means it’s necessary to have a rational, well-planned approach towards the process. Here we’ll discuss some of that.

Some challenges to consider

Cloud technology can replace on-site servers but also causes a widely different IT approach to growing the technology in the company. This is the most basic distinction to understand cloud migration. It’s not just moving servers and information from one place to another. It’s a holistic process of simplification and improvement to cut costs, speed up processes, and boost your business capabilities.

That’s why cloud migration is sometimes said to be a “digital transformation.” Using the cloud is way more than changing your database; it’s more of a new mindset that affects every part of your IT infrastructure. That’s why acquiring a holistic mindset is one of the greatest challenges ahead.

How to migrate into the cloud rightly?

To conduct a successful cloud migration, you need to follow a few simple guidelines.

First, make a thorough inventory of all of your assets. If you exclude one of them, you are compromising your entire IT infrastructure and your migration. That’s the main reason why you need to make a complete inventory of everything you need to make the transition.

Then, you should know that you need a holistic approach. It is not just moving from one type of storage to another. No. It’s about improving processes. You can reduce the number of server roles, for example, if you use this approach correctly.

Also, consider all the options. There are many transition pathways to choose from. Again, take a holistic approach. Don’t see it as a one-size-fits-all process, but look for every chance you may have to bring new features and adopt better practices. Don’t focus on just reducing costs but on how migration can help you improve your IT infrastructure.

Last but not least, it’s also essential to create a security strategy. Cloud cybersecurity is way different from on-premise cybersecurity. Data breaches in the cloud are way too common, but that’s not the cloud’s fault. It’s mainly because of a lack of planning when migrating, which leaves many parts of the infrastructure vulnerable to attacks.

In the cloud, the assets are not behind a firewall, which means it’s necessary to deploy a cloud firewall to protect them from breaches.

In conclusion

Migrating to the cloud may not be the most straightforward process, but at this age, it’s indispensable. Its benefits are way larger than its dangers if you take a holistic, well-planned approach towards transitioning from on-premise to the cloud. It will let you save costs, improve scalability and flexibility, and have a better overall performance.

Why having a growth mindset is so vital in business?

Having a growth mindset is believing you can continue to learn and acquiring new abilities with experience and effort. It contrasts with a fixed mindset, that occurs when someone believes to have a predetermined set of talents and skills that cannot be improved or expanded.

Every now and then, some keywords circle around the business world and become the thing, the topic everyone wants to discuss and promote in its business.

However, when this happens, a mild temptation may come: throwing the idea away just because everyone seems to be clinging on it.

But think it slowly. What of the two mindsets seems to be more adequate for business? The answer is evident. That’s why we’ll tell you why a growth mindset is so vital and how to build it in business.

It leads to innovation

A fixed mindset hinders growth. If you believe your team cannot learn new capabilities, then, your business cannot evolve. A growth mindset encourages learning, innovation, and development. It’s the base of organizational transformation.

In an ever-changing business world where so many companies have to use new technologies and face constant technological competition and innovation, a growth mindset is not a “plus,” but a necessity. Do or die.

It’s not easy…

However, you may face some difficulties in the way. Workers may have a fear of erring or failing, as they can have more traditional mindsets. Lack of risk-taking reduces innovation and self-confidence. Not wanting to leave the comfort zone is the #1 enemy of a growth mindset.

That means the first to embrace a culture of growth mindset must be the leaders. They must create an environment where workers can lose the fear of being judged or making mistakes to embrace new ideas and acquire new skills.

Moving towards a growth mindset is much more than just launching it and letting it develop naturally. It’s not a workshop, nor a branding campaign. It’s a profound change on the vision you have over your business and transmitting it to your crew.

Four ways to build a growth mindset

Now that we know it’s importance let’s state some easy ways to start creating a growth mindset in your business.

Embrace imperfection and face your challenges and mistakes

We all have our flaws and peculiarities. If we don’t understand that, then we can’t leave our comfort zone to innovate and be creative. That’s why it’s vital to face our challenges and see them as opportunities to grow.

Promote constant learning

The business world changes almost daily thanks to technology, social media, and globalization. You have to pass from a know-all mindset to a learn-all mindset. You cannot know the answers if you don’t ask the right questions.

Listen and encourage real conversations

Learn to listen to your workers. Don’t limit yourself to performance checks nor to mere chit-chat while getting a cup of coffee. When you start listening, you start knowing your team and understanding what may stop them from embracing a growth mindset.

Growth Mindset will get you a step ahead

Knowing how to leverage a growth mindset will give you an environment that promotes creativity and innovation. But the first one to embrace it must be the leader. You need to be willing to go the extra mile and to know everyone makes mistakes. Using a solution-driven approach towards problems and facing challenges seeing them as opportunities will help you walk towards a growth mindset.