The difference between artificial intelligence, machine learning and deep learning

 The difference between artificial intelligence, machine learning and deep learning

Nowadays, many terms related to artificial intelligence, machine learning, and deep learning are widely used in the business context, especially when it comes to making correct predictions and analyzing data.

Today's production processes require efficiency and automation. The market has grown, so companies are increasingly focusing on the use of chatbots and other programs and systems to improve logistics, productivity and customer service, with a significant impact also on the presence and visibility of brands. In this regard, artificial intelligence, machine learning and deep learning are becoming increasingly important.

First of all, it is worth noting that, despite the importance of Artificial Intelligence, the quality of the workforce should never be overlooked. Highly qualified people are responsible for making strategic business decisions , based on the analysis provided by intelligent tools.

What is artificial intelligence or AI?

This term is used a lot and refers to the process or ability that machines can have to solve problems and learn efficiently.

It is important to note that machines designed with artificial intelligence have the ability to imitate some human cognitive functions. They can also predict certain financial and business events in order to offer viable solutions for freelancers and companies in different economic sectors.

Some IT professionals determine that by using Artificial Intelligence, machines can interpret a variety of data to achieve objectives with greater flexibility, accuracy and efficiency.

Within Artificial Intelligence, there are systems capable of thinking like human beings, which allow decision-making and help improve learning. Some systems have the ability to think rationally and are useful for calculations, to have an adequate perception of the reality of a company.

Systems capable of acting like humans on their own imitate a variety of human behaviors. For example, we can highlight innovative robotic devices.

Artificial Intelligence Applications

Artificial intelligence is one of the most amazing advancements, as it allows machines to learn to predict certain types of behavior, based on data analysis. For this reason, it finds application in a variety of businesses, exploring the machine learning capability on several fronts, such as:

• natural language processing
• Training electronic devices to detect specific situations, such as diseases in patients, for example
• Using chatbots for efficient customer service
• development of robotic products that stand out for their functionality.

What is machine learning?

Machine Learning is a term that refers to the development of programs with the ability to identify complex patterns in millions of data, build models and generate predictions of future behavior, based on examples of information.

Machine learning is a part of artificial intelligence and is very useful for developing computer systems that can learn from input examples. In this context, it is important to mention that this type of machine can identify patterns in a large amount of data used for business analysis.

This type of learning is widely used in companies that have adopted Artificial Intelligence, as it improves processes, provides productive efficiency, increases revenues and reduces costs.

Machines powered by Artificial Intelligence work with an algorithm to review the amount of data in a company. Therefore, they can predict the behaviors that they will have in the future. It is important to note that this type of computer system can be improved day by day and does not need human intervention to function and be efficient.

Machine learning applications

Machine learning is mainly used to enable machines to project behaviors through learning. For example,  there are machines that recognize faces,  learn a variety of languages,  perform  medical diagnoses with great efficiency,  among  others .

Businesses look at many important aspects, such as customer satisfaction. With Machine  Learning , you can determine which customers  will turn  to another provider because the company  may be providing inadequate service .

In addition,   a variety of customer data  is analyzed, such as preferences in  product consumption, age, products or plans that were contracted, etc. In other words, one of the great advantages of Machine  Learning  is that it allows companies to be more competitive. This is because algorithms detect a variety of patterns to predict or determine certain future events.

Today, most businesses use relevant data to gain important insights that enable a competitive advantage in the market. With machine learning, you can detect fraud, make predictions about industrial equipment failures, analyze employee efficiency, have access to profitable customers, analyze consumer behavior, have better control over social media to  make timely posts, diagnose  patients ,  provide  better customer service through personalized and timely calls,  among countless other possibilities.

Typically, machine learning and artificial intelligence are primarily used to make business decisions, through the correct analysis of the data generated by a company .

Manual processes are a thing of the past. With machine learning, machines can analyze data through algorithms, which are improved every day to make businesses more efficient.

Machine learning can be incorporated by different segments of the economy, such as health, insurance, telecommunications, energy, finance and others.

What is deep learning?

It is a  recent term, part of  Artificial Intelligence  . It is mainly used to create modern and efficient systems. With Deep Learning, it is possible to develop programs that can perform human-like behaviors.

Deep learning improves machine learning by giving machines the ability to choose from a set of algorithms that offer a variety of answers and act on the conclusions determined by a variety of combinations.

Deep learning allows machine learning to be performed through an artificial neural network. This type of learning works through levels, i.e. at the first level, the network can learn simple things and then transmit all the learned information to the other level. This information is then efficiently combined to improve machine learning.

Deep learning is currently widely used in the industrial sector as it enables the analysis of large amounts of data to discover important patterns and make accurate predictions.

Artificial intelligence, machine learning and deep learning in the business context

Artificial intelligence improves production efficiency. Thanks to it, there are many systems capable of reproducing a variety of processes that are part of human thinking, for example:

• Voice recognition
• Image Identification
• Facial recognition
• Autonomous vehicles
• Medical diagnosis

With the performance of Deep Learning processes, you can delve deeper into previous  applications  , like this:

• Voice recognition: recognition of accents, dialects, etc.
• Image identification: recognition of specific colors and materials, etc.
• Facial recognition: identifies the user's emotions of happiness or sadness, etc.
• Autonomous vehicles: route recognition, taking into account the environment around you, etc.
• Medical diagnosis: symptom recognition for more accurate disease detection, etc.

Machines, through machine learning, can also provide a clearer view of the business. This helps managers make the best decisions.

Furthermore, thanks to Deep Learning, robotics has evolved, and it is now possible to create machines that reproduce human behavior and are capable of improving business productivity.

 

Artificial Intelligence Transforms War

(San Francisco) China and the United States have stopped short of committing to banning lethal autonomous weapons, as some experts had hoped, after media reports of the issue emerged at Wednesday's presidential summit in California.

Presidents Joe Biden and Xi Jinping have nevertheless agreed that their respective experts will discuss the risks associated with rapid advances in artificial intelligence (AI), which are disrupting many sectors.

In the field of military equipment, this technology could constitute the third major revolution, after the invention of gunpowder and the atomic bomb.

Non-exhaustive review of AI applications in military equipment.

Autonomous weapons

Robots, drones, torpedoes… thanks to technologies ranging from computer vision to sophisticated sensors, all kinds of weapons can be transformed into autonomous systems, governed by AI algorithms.

Autonomy does not mean that a weapon "wakes up in the morning and decides to go to war," says Stuart Russell, a professor of computer science at the University of California, Berkeley.

"This means they have the ability to locate, select and attack human targets, without human intervention."

These lethal autonomous weapons systems are also nicknamed "killer robots," a phrase that evokes androids straight out of science fiction.  

"This is one of the options being explored, but in my opinion it is the least useful of all," the specialist notes.

Most of these weapons are still ideas or prototypes, but Russia's war in Ukraine offers a glimpse of their potential.

Due to telecommunications issues, armies have been pushed to make their drones more autonomous.

As a result, "people are going underground," Russell notes, and this foreshadows a major change in the nature of war, "where being visible anywhere on the battlefield will be a death sentence."

Autonomous weapons have several potential advantages: efficiency, low-cost mass production, no human emotions such as fear or anger, no radioactive crater in their wake, etc.

But they raise major ethical questions in terms of evaluation and commitment.

And above all, "since it does not require human supervision, you can launch as many as you want," stresses Stuart Russell, "and therefore potentially destroy an entire city or an entire ethnic group in one go."

Autonomous vehicles

Autonomous submarines, boats and aircraft are intended to provide reconnaissance, surveillance or logistical support in dangerous or remote areas.

These vehicles, like drones, are at the heart of the "Replicator" program launched by the Pentagon to counter China in terms of personnel and military equipment, particularly in the Asia-Pacific region where the United States is trying to regain power.

The goal is to deploy several thousand "low-cost, easily replaceable autonomous systems in many areas within the next 18 to 24 months," Deputy Defense Secretary Kathleen Hicks said in late August.

She cited the example of space, where such devices "will be launched by the dozens, to the point that it will be impossible to eliminate them all."

Many companies are developing and testing autonomous vehicles, including California-based Anduril, which touts its human-free submarines as “optimized for a variety of defense and commercial missions such as long-range oceanographic sensing, underwater battlespace awareness, mine countermeasures, anti-submarine warfare,” and more.

Tactical software

Powered by AI and capable of synthesizing mountains of data collected by satellites, radars, sensors and intelligence services, tactical software serves as powerful assistants for general staffs.

“The Pentagon needs to understand that in an AI war, data is the ammunition,” Scale AI CEO Alexandr Wang said at a congressional hearing in July.

“We have the largest fleet of military hardware in the world. It generates 22 terabytes of data per day. If we can organize that data properly to analyze it with AI, we will have a pretty insurmountable advantage in terms of using this technology for military purposes.”

Scale AI has won a contract to deploy a language model on a classified network of a major U.S. military unit. Its chatbot, “Donovan,” is designed to enable commanders to “plan and act in minutes instead of weeks.”

 

The impact of AI in business

Generative AI will impact all departments of the enterprise, but four departments could account for about 75% of the total annual value from generative AI use cases . These are marketing and sales, product R&D, software engineering, and customer operations. The opportunities for using AI in these departments are numerous and have very positive effects, even if other challenges remain.

 

The positive effects of AI in business

AI can automate repetitive and tedious tasks and free up time for more creative or higher value-added tasks. For example, it is possible to quickly exploit a large amount of data to make sales, supply, logistics, finance forecasts, create a summary overview of a customer, summarize email exchanges, prepare a customer meeting, create content, etc. AI can also identify or avoid errors and optimize processes, which reduces costs and delays.

The challenges and risks of AI in business

The biggest fear and the biggest unknown concerns the impact of AI on certain jobs that risk being replaced or reduced, particularly those that are routine, low-skilled or low-paid. This leads to risks of unemployment, precariousness or social inequality.

AI also raises ethical and legal questions about user rights, data integrity, and the potential for discrimination, violations of privacy, security, or human rights. Other possibilities include harm caused by autonomous or uncontrolled AI systems.

In the workplace, AI can potentially cause a feeling of dehumanization or loss of meaning. It can also create a feeling of stress, surveillance and call into question the autonomy or creativity of workers.

The requirement to make investments to access AI-specific infrastructure and skills risks creating inequalities depending on the size of companies and can create a technology gap and unfair competition.

There is also concern about the environmental impact as AI is a big consumer of energy and resources.

These negative effects can be avoided or mitigated by appropriate measures , such as training, support, protection, regulation or worker participation, in order to ensure that AI serves humans and not the other way around.

If employees can free themselves from tedious tasks to devote themselves to more interesting tasks, they will be recognized for their high value-added work. They will be able to free themselves from certain routines, certain hardships, certain pressures , to access greater responsibilities and have more perspectives. In all cases, they will feel more satisfaction, self-esteem and well-being at work.

Process improvement promotes development, R&D and innovation, increases competitiveness and economic growth, opening up new markets, new sectors and new business opportunities .

AI also opens up new job prospects and gives rise to new career paths requiring new skills: engineers, developers, analysts, trainers. It will also be an opportunity for professional retraining towards these professions. New professions will also be dedicated to the maintenance, development, and upkeep of AI and its environment.