1. Machine Intelligence Is Increasing Due to Big Data
Machine learning (ML) and artificial intelligence (AI) are rapidly gaining traction as dominant problem-solving approaches in a wide variety of fields of research and industry, not least because to deep learning’s recent triumphs (DL). However, as lately suggested in news, blogs, and the media, the equation AI=ML=DL falls short. These topics are founded on the same underlying premise: computation is an effective method for simulating intelligent behaviour in machines. What type of computation should be performed and how should it be programmed? This is the incorrect question to ask. Computation does not preclude the use of search, logical, and probabilistic techniques, as well as (deep) (un)supervised and reinforcement learning methods, to name a few, because computational models incorporate all of these. They compliment one another, and the next breakthrough will come from integrating them, not just pushing each of them.
This is not a passing fad. The world is expanding exponentially, and with it, the volume of data collected globally. Data is becoming more meaningful and contextually relevant, paving the way for machine learning (ML), namely deep learning (DL) and artificial intelligence (AI), to move from research laboratories to production (Jordan and Mitchell, 2015). The challenge has evolved away from accumulating vast volumes of data and toward comprehending it—converting it into information, conclusions, and actions.
Numerous scientific disciplines, ranging from cognitive sciences to biology, finance, physics, and social sciences, as well as a large number of businesses, think that data-driven and “intelligent” solutions are required to address many of their most pressing issues. Personalized therapy can be enabled by using high-throughput genomic and proteomic research. To improve information retrieval, large datasets of search queries can be used. Climate data from the past can be utilised to gain a better understanding of global warming and improve weather forecasting. Numerous sensor readings and hyperspectral images of plants can be utilised to detect drought conditions and obtain knowledge into when and how stress affects plant growth and development, and thus how to combat the global hunger crisis. Within video games, game data may convert pixels to actions, but observational data can assist robots in comprehending complicated and unstructured surroundings and developing manipulation abilities.
However, as lately stated in the press, blogs, and media, are AI, machine learning, and deep learning synonymous? For instance, when AlphaGo (Silver et al., 2016) defeated South Korean Master Lee Se-dol in the board game Go in 2016, the media referred to AlphaGo’s victory as an AI, machine learning, and deep learning victory. Additionally, Gartner’s (Panetta, 2017) list of the top ten Strategic Trends for 2018 places (narrow) AI at the top, defining it as “a collection of highly scoped machine-learning systems that focus on a specific task.”
1. Machine Intelligence Is Increasing Due to Big Data
Machine learning (ML) and artificial intelligence (AI) are rapidly gaining traction as dominant problem-solving approaches in a wide variety of fields of research and industry, not least because to deep learning’s recent triumphs (DL). However, as lately suggested in news, blogs, and the media, the equation AI=ML=DL falls short. These topics are founded on the same underlying premise: computation is an effective method for simulating intelligent behaviour in machines. What type of computation should be performed and how should it be programmed? This is the incorrect question to ask. Computation does not preclude the use of search, logical, and probabilistic techniques, as well as (deep) (un)supervised and reinforcement learning methods, to name a few, because computational models incorporate all of these. They compliment one another, and the next breakthrough will come from integrating them, not just pushing each of them.
This is not a passing fad. The world is expanding exponentially, and with it, the volume of data collected globally. Data is becoming more meaningful and contextually relevant, paving the way for machine learning (ML), namely deep learning (DL) and artificial intelligence malaysia (AI), to move from research laboratories to production (Jordan and Mitchell, 2015). The challenge has evolved away from accumulating vast volumes of data and toward comprehending it—converting it into information, conclusions, and actions.
Numerous scientific disciplines, ranging from cognitive sciences to biology, finance, physics, and social sciences, as well as a large number of businesses, think that data-driven and “intelligent” solutions are required to address many of their most pressing issues. Personalized therapy can be enabled by using high-throughput genomic and proteomic research. To improve information retrieval, large datasets of search queries can be used. Climate data from the past can be utilised to gain a better understanding of global warming and improve weather forecasting. Numerous sensor readings and hyperspectral images of plants can be utilised to detect drought conditions and obtain knowledge into when and how stress affects plant growth and development, and thus how to combat the global hunger crisis. Within video games, game data may convert pixels to actions, but observational data can assist robots in comprehending complicated and unstructured surroundings and developing manipulation abilities.
However, as lately stated in the press, blogs, and media, are AI, machine learning, and deep learning synonymous? For instance, when AlphaGo (Silver et al., 2016) defeated South Korean Master Lee Se-dol in the board game Go in 2016, the media referred to AlphaGo’s victory as an AI, machine learning, and deep learning victory. Additionally, Gartner’s (Panetta, 2017) list of the top ten Strategic Trends for 2018 places (narrow) AI at the top, defining it as “a collection of highly scoped machine-learning systems that focus on a specific task.”
Source: mobius.co
