Smart Software Learns to Run And Play Football

The idea of a dystopia in which intelligent machines learn human tasks and resolve to put an end to our existence, so common in literature and science fiction cinema, seems to be only a matter of time.

More proof of this comes from DeepLoco, a computer program created at the University of British Columbia in the United States, which has learned a number of activities by itself.

The intelligent software uses machine learning and was created just to develop skills on its own. In this way, it discovered how to walk and run in the open or between obstacles, just as it learned to play football – or at least carries it with the feet. The video below shows some of the capabilities of DeepLoco.

<iframe src="https://www.youtube.com/embed/G4lT9CLyCNw&quot; width="700" height="394" frameborder="0" allowfullscreen="allow

The idea of a dystopia in which intelligent machines learn human tasks and resolve to put an end to our existence, so common in literature and science fiction cinema, seems to be only a matter of time.

More proof of this comes from DeepLoco, a computer program created at the University of British Columbia in the United States, which has learned a number of activities by itself.

The intelligent software uses machine learning and was created just to develop skills on its own. In this way, it discovered how to walk and run in the open or between obstacles, just as it learned to play football – or at least carries it with the feet. The video below shows some of the capabilities of DeepLoco.

In the future, DeepLoco can make a very impressive contribution to the development of games in the future. If the evolving characters of Mordor Shadows are already attractive, imagine characters not controlled by the player with the ability to learn new skills as the game unfolds?

Data Analytics in Clear Language

You have allocated a budget to innovate; you’re on the verge to work with the new “oil” Data. You decide to take data scientists. A good start, and now? Where to begin? What tools they should use and what data sources are appropriate? What is the duration of a data science process if the result is acceptable and how you bring a beautiful model in practice?

Big Data, Artificial Intelligence, Machine Learning, Deep Learning; Nowadays you can not ignore. But … what do these terms actually is and how it is used in the business?

What is Data Science really?

In recent years, the amount of collected and available data grew exponentially. Until 2020, the data will grow by approximately 42% per year. Here we not only talk about data and numbers in tables (structured data) but also documents, chats, posts, photos, videos, audio clips, etc. (<a href="https://learningsimp

You have allocated a budget to innovate; you’re on the verge to work with the new “oil” Data. You decide to take data scientists. A good start, and now? Where to begin? What tools they should use and what data sources are appropriate? What is the duration of a data science process if the result is acceptable and how you bring a beautiful model in practice?

Big Data, Artificial Intelligence, Machine Learning, Deep Learning; Nowadays you can not ignore. But … what do these terms actually is and how it is used in the business?

What is Data Science really?

In recent years, the amount of collected and available data grew exponentially. Until 2020, the data will grow by approximately 42% per year. Here we not only talk about data and numbers in tables (structured data) but also documents, chats, posts, photos, videos, audio clips, etc. (unstructured data).

To take advantage of these data, it is important to translate these data into useful information. The transformation of the quantity of data to information is done through data analytics. This newly acquired knowledge can then be used to make-driven systems, processes, and decisions.

Data Analytics Process

An average data analytics process can be divided into three phases: data processing, transformation, and visualization.

The process starts with the business itself, where a set of data is available where information can be removed. You can consider data from your customer base, from financial systems or, for example, logistics administration. It is also possible to integrate external data sources into your analysis, such as weather data and social media.

Processing

In this phase, there is scooped outline in a large amount of data is present, so that it can be used for the transformation and the visualization step. This processing phase is the most time-consuming, it takes on average 70% of the time. During this period, the data is put in the correct format.

Transformation

During the transformation phase, different data are combined and transformed into analytics models. In this way, new information extracted from the data. The analytics models search for hidden trends and relationships so that you understand about your business.

Visualization

In the visualization phase, the results of the transformed data will be made transparent to the user by means of visualization. By using the appropriate visualization methods and tools, it is possible to understand and use the information hidden in your data.

Artificial Intelligence: Friend or Enemy of Cybersecurity?

Security strategies must undergo a radical revolution. Tomorrow’s security devices will need to see and operate internally among them to recognize changes in the interconnected environments and thus automatically be able to anticipate risks, update and enforce policies.

Devices must have the ability to monitor and share critical information and synchronize their responses to detect threats.

Sounds very futuristic? Not really. A new technology that has recently grabbed attention lays the foundation for such an automation approach. This has been called Intent-Based Network Security (IBNS).

This technology provides extended visibility across the entire distributed network and enables integrated security solutions to automatically adapt to changes in network configurations a

Security strategies must undergo a radical revolution. Tomorrow’s security devices will need to see and operate internally among them to recognize changes in the interconnected environments and thus automatically be able to anticipate risks, update and enforce policies.

Devices must have the ability to monitor and share critical information and synchronize their responses to detect threats.

Sounds very futuristic? Not really. A new technology that has recently grabbed attention lays the foundation for such an automation approach. This has been called Intent-Based Network Security (IBNS).

This technology provides extended visibility across the entire distributed network and enables integrated security solutions to automatically adapt to changes in network configurations and change needs with a synchronized response against threats.

These solutions can also dynamically divide network segments, isolate affected devices, and get rid of malware. Similarly, new security measures and countermeasures can be automatically upgraded as new devices, services, and workloads are moved or deployed to and from anywhere in the network and from devices to the cloud.

The tightly integrated automated security allows for a general response against threats far greater than the total of all individual security solutions that protect the network.

Artificial intelligence and machine learning have become significant allies for cybersecurity. Mechanical learning will be reinforced by devices packed with information from the Internet of Things and by predictive applications that help to safeguard the network. But securing those “things” and information, which are ready targets or entry points for cybercriminals, is a challenge in itself.

The quality of intelligence

One of the greatest challenges of using artificial intelligence and machine learning lies in the caliber of intelligence. Today, Intelligence against cyber threats is highly prone to false positives due to the volatile nature of IoT.

Threats can change in a matter of seconds; one device can be flushed out, infect the next and then re-emptied back into a full low latency cycle.

Improving the quality of intelligence against threats is extremely important as IT teams increasingly transfer control to artificial intelligence to perform work that they otherwise should do. This is an exercise in trust, and this is a unique challenge.

As an industry, we can not transfer total control to an automated device, but we need to balance operational control with essential execution that can be performed by the staff. These work relationships will really make artificial intelligence and machine learning applications for cyber defense really effective.

Because there is still a shortage of talent in cybersecurity, products and services must be developed with greater automation in order to correlate intelligence against threats and thus, determine the level of risk to synchronize a coordinated response automatically.

By the time managers try to tackle a problem on their own, it is too late, even causing a major problem or generating more work. This can be handled automatically, using a direct exchange of intelligence between detection and prevention products or with assisted mitigation, which is a combination of people and technology working together.

Automation also allows security teams to allocate more time to the business goals of the company, rather than spending time in the routine administration of cybersecurity.

In the future, artificial intelligence in cybersecurity will constantly adapt to the growth of the attack surface. Today, we are barely connecting points, sharing information and applying that information to systems.

People are making these complex decisions, which require a correlation of intelligence from humans. It is expected that in the coming years, a mature artificial intelligence system may be able to make complex decisions for itself.

What is not feasible is total automation; That is, transfer 100% of the control to the machines so that they make the decisions all the time. People and machines must work together.

The next generation of “conscious” malware will use artificial intelligence to behave like a human, perform reconnaissance activities, identify targets, choose attack methods, and intelligently evade detection systems.

Just as organizations can use artificial intelligence to improve their security posture, cybercriminals can also start using it to develop smarter malware.

It guided by offensive intelligence set and analysis such as the types of devices deployed in the segment of a network, traffic flow, applications being used, transaction details or the time of day in which they occur.

The longer a threat remains within the network, the greater the ability to operate independently, to blend into the environment, to select tools based on the target platform, and eventually to take countermeasures based on the security tools found in the place.

This is precisely the reason why an approach is needed where security solutions for networks, accesses, devices, applications, data centers and cloud work together as an integrated and collaborative system.

With Artificial Intelligence, The Travel Industry Can Better Understand its Customers

If you have heard about artificial intelligence (AI) and are updated on recent developments, you may have heard of these terms: deep learning, algorithms, machine learning, etc. As we progress in the flowering of AI, Deep Learning is implemented in various industries and platforms, and we begin to see tangible applications in our daily lives.

It is a fact that the travel industry has much to improve: business travel can turn into nightmares easily, travelers are even more demanding when choosing their travel option and expect full-time care and support.

Artificial Intelligence is already common to most travelers in the form of computer helpers like Ok Google, Siri and Cortana, and technologies like this can respond to current problems.

The abundance of data that travel organizations have, including traveler profiles, action history, airline preferences, and hotels, make the business of the tourism easily appropriated to the AI. Companies such as <a href="https://www.poder.io/&quot;

If you have heard about artificial intelligence (AI) and are updated on recent developments, you may have heard of these terms: deep learning, algorithms, machine learning, etc. As we progress in the flowering of AI, Deep Learning is implemented in various industries and platforms, and we begin to see tangible applications in our daily lives.

It is a fact that the travel industry has much to improve: business travel can turn into nightmares easily, travelers are even more demanding when choosing their travel option and expect full-time care and support.

Artificial Intelligence is already common to most travelers in the form of computer helpers like Ok Google, Siri and Cortana, and technologies like this can respond to current problems.

The abundance of data that travel organizations have, including traveler profiles, action history, airline preferences, and hotels, make the business of the tourism easily appropriated to the AI. Companies such as Poder.IO, specializing in AI solutions for sectors such as travel, are not only architects but also witnesses of what airlines, hotels, and companies associated with tourism are creating to serve travelers differently before, during and after their travels.

An example is Pana, a specialist on-demand travel company, contacts its customers using messages through applications, SMS or email. It consolidates the regular management of the local dialect, with information on the inclinations of the traveler, and uses the AI to indicate to the operators the decisions that more applications in the middle of the reservation procedure.

The KLM airline allows its travelers to obtain reservations confirmations, registration notices, tickets and flight announcements through a Facebook Messenger bot. They can also contact KLM through Messenger all day, every day.

And the Hilton hotel chain is testing Connie, a robotic assistant, powered by IBM Watson and Wayblazer. It can answer questions from visitors regarding courtesies, management, and nearby attractions. The more visitors you connect with Connie, the more you learn, adjust, and improve your suggestions and responses.

On the other hand, Customer relationship management in the travel industry is always about information and AI also helps in this regard. To build reliable relationships with customers and travelers, tourism managers need to know a lot about their customers; this information covers everything from their age, sexual orientation, gastronomic preferences, and interests.

Most of these data can be used throughout the client’s journey to maintain service at phenomenal levels, and be present to throw carrots at the right time to maintain their loyalty. Artificial intelligence can make this happen with just a few clicks.

The AI brings to the light valuable knowledge that travel managers had never conceived possible. This, in principle, should lead to greater customer benefit, better quality advertising and increased loyalty to brands that use their data in the right way.

That is why obtaining the necessary information and releasing the right messages for the right travelers is the biggest challenge for the tourism industry.