Are nueral networks or blockchain harder to understand

Although the system was introduced in , its actual use began to grow only from Therefore, Bitcoin is a new entry in currency markets, though it is officially considered as a commodity rather than a currency, and its price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial currencies, also in view of its very different nature with respect to more traditional currencies or commodities. Hence, forecasting Bitcoin price has also great implications both for investors and traders. In this work, we approach the forecast of daily closing price series of the Bitcoin cryptocurrency using data on prices and volumes of prior days.

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Content:

• What is TensorFlow? The machine learning library explained
• Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning
• Are blockchains immune to all malicious attacks?
• DEEP NEURAL NETWORK: HOW FAR CAN ARTIFICIAL INTELLIGENCE GO?
• Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network
• Solana, a blockchain platform followed by top crypto investors, says it’s far faster than Ethereum

WATCH RELATED VIDEO: If You Understand This Video About Blockchain You Are in The Top 0.0001% of People That Get it

What is TensorFlow? The machine learning library explained

Artificial intelligence is already an integral and easily usable part of our daily life, as evidenced by its constant use in many large Italian companies. It is not difficult, in fact, to realise that every day we deal with intelligent algorithms that are able to self-learn and that help us in our daily life in many ways:.

There are also numerous examples of how AI has had a positive impact on business or public administration procedures by automating, reducing errors or allowing the development of new products and services. In this article we will deepen some aspects of artificial intelligence, in particular those related to the deep neural network, and we will ask the authoritative opinion of Piero Poccianti, the president of the Italian Association for Artificial Intelligence.

Before delving into the concepts of the deep neural network it is necessary to give a definition of artificial intelligence. However, there is no unambiguous definition of IA, and interpretations can be of various kinds, depending on the scope of interest. One can focus on the internal processes of reasoning, for example, or rather on the external behavior of systems, always starting from the similarity of reactions and results with respect to human behavior.

To date, the scientific community defines two different types of artificial intelligence: the so-called weak and the so-called strong. The starting point takes into account how human skills, by innate characteristic, concern the understanding and processing of natural language and images, learning, reasoning and planning skills, but also the interactions with people, machines and the external environment.

At this point we need to make a clarification: unlike traditional software, an AI system is not based simply on the programming made by developers who write their operating code, but on progressive learning techniques, that is, on the definition of algorithms that, by processing a huge amount of data, lead the system itself to reevaluate and advance its own understanding and reasoning skills.

The attempt to simulate human reasoning with artificial intelligence opens up several scenarios of ethical reflection, which have been explored in this article. From a technological and methodological point of view, what we call artificial intelligence is understood as a learning process that generates a task or an action. To date, there are two main learning models: Machine Learning and Deep Learning. The first case is related to functional systems that allow to train the software to correct errors, so that it can learn and then perform an activity independently: think for example of a mechanical hand that performs a very high precision cut using a control algorithm.

Machine learning is progressing, however, on the use of neural networks organised in several levels of depth, for this reason called Deep Learning : we are talking about relatively recent development learning processes after , inspired by the structure and functioning of human neuronal networks.

These systems are already in use, for example in pattern recognition, speech and image recognition, and Natural Language Processing systems. We can think, for example, how the human mind conceives four basic categories: perceiving, seeing, knowing how to listen, knowing how to recognise an object or the world around us.

The fourth capacity is also at an early stage and has been already tested, but the one on which the challenge is played and the fundamental question remains is the ability to abstract thinking, in which man expresses, for example, similarities with similar processes already tested in various fields and which can be replicated also by adopting creativity or modifications, for example. It is on this aspect, combined with the ability to independently build new strategies, that the possibility to answer to the great fundamental question will be concentrated: understanding how, if and how much the so-called machines will somehow be able to cross the categories mentioned, adding the large piece related to the ability to sense that is still missing from the ability to abstract.

The aforementioned considerations are reflections on which ICT research centres dwell every day, in the context of the development of artificial intelligence. For more insights on the state of ICT research in Italy you can take a look at this article. Are you looking for a partner for ICT research projects? Fill in the form below to get in touch with PMF Research.

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Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning

Artificial neural networks ANN , more commonly referred to as neural networks NN , are computing systems inspired by the biological neural networks that constitute human brains. Neural networks may seem new and exciting, but the field itself is not new at all. Frank Rosenblatt, an American psychologist, conceptualized and tried to build a machine that responds like the human mind in For all practical purposes, artificial neural networks learn by example, in a manner similar to their biological counterparts. External inputs are received, processed, and actioned in the same way the human brain does. We know that different sections of the human brain are wired to process various kinds of information. These parts of the brain are arranged hierarchically in levels.

Are blockchains immune to all malicious attacks?

Artificial general intelligence AGI is the hypothetical ability of an intelligent agent to understand or learn any intellectual task that a human being can. AGI can also be referred to as strong AI , [2] [3] [4] full AI , [5] or general intelligent action [6] Although academic sources reserve the term "strong AI" for computer programs that experience sentience or consciousness. In contrast to strong AI, weak AI [7] or "narrow AI" [3] is not intended to have general cognitive abilities; rather, weak AI is any program that is designed to solve exactly one problem. Academic sources reserve "weak AI" for programs that do not experience consciousness or do not have a mind in the same sense people do. Various criteria for intelligence have been proposed most famously the Turing test but to date, there is no definition that satisfies everyone. However, there is wide agreement among artificial intelligence researchers that intelligence is required to do the following: [10]. Other important capabilities include:. Computer based systems that exhibit many of these capabilities do exist e. The following tests to confirm human-level AGI have been considered: [15] [16].

DEEP NEURAL NETWORK: HOW FAR CAN ARTIFICIAL INTELLIGENCE GO?

Artificial intelligence is already an integral and easily usable part of our daily life, as evidenced by its constant use in many large Italian companies. It is not difficult, in fact, to realise that every day we deal with intelligent algorithms that are able to self-learn and that help us in our daily life in many ways:. There are also numerous examples of how AI has had a positive impact on business or public administration procedures by automating, reducing errors or allowing the development of new products and services. In this article we will deepen some aspects of artificial intelligence, in particular those related to the deep neural network, and we will ask the authoritative opinion of Piero Poccianti, the president of the Italian Association for Artificial Intelligence. Before delving into the concepts of the deep neural network it is necessary to give a definition of artificial intelligence.

Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network

Deep learning was one of the great breakthroughs in the field of artificial intelligence. Generally, there is a belief that deep learning may be all that is needed to replicate human intelligence. However, the reality is that challenges remain, as exposing a neural network to an unknown dataset will reveal it in a fragile way. For example, in the case of autonomous cars they are apparently effective, but AI systems can easily get it wrong. If the system has only been trained to identify objects from side perspectives, it is unlikely to recognise them from a higher perspective. Taking advantage of the impact and the large investment involved in AI development today, new proposals are emerging every day to develop Deep Learning, Machine Learning, new algorithms, and so on.

Solana, a blockchain platform followed by top crypto investors, says it’s far faster than Ethereum

In , people benefit from artificial intelligence every day: music recommender systems, Google maps, Uber, and many more applications are powered with AI. However, the confusion between the terms artificial intelligence, machine learning, and deep learning remains. This is how it looks on an Euler diagram:. The term artificial intelligence was first used in , at a computer science conference in Dartmouth. AI described an attempt to model how the human brain works and, based on this knowledge, create more advanced computers. After all, the conference collected some of the brightest minds of that time for an intensive 2-months brainstorming session.

By Brian Droitcour. The second edition, in June , focused on artificial intelligence. Artists whose work uses generative adversarial networks GANs — algorithms that pit computers against each other to produce original machine-made output approximating the human-made training data—have turned to crypto platforms not only to sell their work, but also to explore ways of critically and creatively engaging the blockchain.

Skip to search form Skip to main content Skip to account menu You are currently offline. Some features of the site may not work correctly. In this study, we applied the long short-term memory LSTM model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies.

Machine learning is a complex discipline. Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning aka neural networking models and algorithms and makes them useful by way of a common metaphor. TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE partial differential equation based simulations. Best of all, TensorFlow supports production prediction at scale, with the same models used for training. TensorFlow allows developers to create dataflow graphs —structures that describe how data moves through a graph , or a series of processing nodes.

Try out PMC Labs and tell us what you think. Learn More. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks mining and, eventually, the generation of money.