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Cyber Attacks Have Led to Focus More on Data Security

The recent change in working pattern has led to Work from Home and since then it has been difficult for accountancy firms to keep a check on the data. Thus, it has been hard for firms and employees to deal with security. “That being said, the chief officer for Calligo, Adam Ryans has suggested that […]

The post Cyber Attacks Have Led to Focus More on Data Security appeared first on Aiiot Talk – Artificial Intelligence | Internet of Things | Technology.

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The recent change in working pattern has led to Work from Home and since then it has been difficult for accountancy firms to keep a check on the data. Thus, it has been hard for firms and employees to deal with security.

“That being said, the chief officer for Calligo, Adam Ryans has suggested that the change has been difficult and by default, the change itself can be at a state of vulnerability to cyber attacks.” 

In fact, during a live conversation at Accountancy Age, he also mentioned that the change in human behaviour is codependent on cultural change. That being said, he also mentions that when a person goes to work in the office, the alertness and the sense of being alarmed all the time are relatively higher. That is not the same when the work process continues from home.

However, the point is, it is natural to change the environment that becomes comfortable after a point of time. While work from home is convenient, it can also reduce the sense of awareness along with added distractions in the house. That way, the sense to detect any potential threat can easily get reduced. Let’s find out how you are prone to data theft.

How These Cyber Attacks Take Place?

Ever since work from home has been called out globally, the rate of phishing attacks has gone higher. This gives all the attackers the vantage point knowingly that work from home comes with a reduced sense of security and having distractions- for example, daily household chores, parenting, or more. As per the reports, what has also become common are the fake emails from higher authorities and failed Data recovery

Bearing that in mind, Chris Knowles, a Digital officer of RSM in the United Kingdom had agreed with Ryan Adams. However, he also added that the current situation calls in for immediate exercising to control any breaching of data. He also suggests that action should be taken instantly which is involved with technical solutions. 

With regard to that, Chris also mentions that working remotely does not necessarily mean that every employee will work from home. Instead, it means that employees may be at a different location, maybe running errands whilst working or visiting a public place.

This itself implies that security levels should be taken into long consideration if employees are going to work from different locations other than their house. But before doing that, what is a necessity is to find out what or which organization can be targeted by breachers. And, how that can affect a certain type of cyber attack. If your data is in the wrong hands, contact Data recovery services. 

What are the Security Implications?

As work from home is the new approach, there has been a shift in the change of using other platforms. For example, Zoom or any other application that is in use for video conferencing. Ryan and Chris have been concerned about this shift. The concern derived from a previous isolated incident that involved the application, Zoom. 

The US Federal Bureau of Investigation made an investigation approach towards Zoom when hacking incidents took place on the platform. Even after that incident, many have still resorted to using Zoom as a channel of video communication. 

Bearing that in mind, Knowles suggests that private messages and emailing needs to have proper and levelled security, because it forms a channel of communication & collaboration. Thus, to keep the profession going, companies and organizations must also consider regulations while finding a safer way to communicate. If your informational data is at stake, find a safe Data recovery from professionals.

How Accountancy Education will Reflect on the Current Situation?

For many years, decades, and centuries, education systems keep teaching about the role in society, for generations after another. However, the recent events have been challenging to an extent where many have decided to simplify it. Meanwhile, such challenges, over time, can reflect upon how education systems have been teaching everyone, and the current situation can be helpful to learn from. 

On the other hand, IFAC (International Federation of Accountants) has an association with accountants worth 3 million, shares wise advice. The association is popular in many countries, such as France, Canada, Mexico, and more. During the discussion with all these people, two notable points had come up.

These notable points suggest that the approach in higher education needs to move way forward. Simultaneously, the curriculum of accountancy needs to be available in such a way that it can help in succeeding long term goals and challenges. And this is in relation to accountancy. 

Why?

Given the current situation, accountancy has been extremely important as an essential medium among business organizations. To keep the essentiality alive, a good amount of training is necessary for the upcoming generation to learn. Alongside this, it is needless to mention that accountancy is the backbone of every business organization. 

“However, accountancy education has not been of much importance before the pandemic hit. Thus, it is an absolute necessity to exercise upon modern models.” 

That way, one can understand how it plays an important role in society and how it’s agility can cause adverse effects. The interaction of various practitioners around the world can share their wisdom which will be helpful in clearing out doubts and approaching new ideas.

Thus, discussing long term goals can help everyone in grasping the concepts and working on it in the future. This way, it prepares the next generation to be ready when these challenges arise. 

Cyber attacks will continue to exist even a decade later. More so, the attacks could even become more advanced or aggressive. However, educational discussions about such problems related to accountancy, Data recovery, can make the future accountants more aware of the situation. In other words, it can turn them into dynamic decision-makers. 

Finally…

The upcoming era will come face to face with new challenges of cyber-attacks and cybercrimes. But to anticipate the cause, through inferred knowledge, the future generation can overcome such shortcomings. Thus, stay updated on accountancy and Data recovery and keep track of what’s going around the globe!

Also Read Avoid Cyberattacks During Remote Work

Source: https://www.aiiottalk.com/data-and-security/cyberattacks-led-to-focus-data-security/

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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