If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The shift to real-time data analysis and optimization has supercharged marketing in a way that frankly should have SEOs on the edge of their seats. As organizations struggle to make sense of and activate their data, SEOs can combine their deep experience with massive amounts of data to make smarter business decisions and have an edge. Things are about to get real, very real. Automation in marketing no doubt was (and still is) a game-changer for consultancies, agencies, and clients. Straight-up automation brought efficiency and order to the workflow.
Machine learning, artificial intelligence (ML & AI) and big data form up a new niche area that is seeing a fast-paced growth rate in India. To clarify terminologies for a layperson, AI is basically all about mimicking human intelligence in machines, ML is a sub-set of AI and is about techniques that enable these machines to continuously learn on their own through data and perform a desired set of processes. Big Data analytics is about extracting huge data and observing unanticipated patterns from the same, while ML uses the same to provide incremental data/information to help the machine learn on its own. Data science and big data industry in India is growing at 33per cent CAGR (Compounded annual growth rate) and stood at $2.71 Billion in 2018. While the Finance & Banking industry leads the share in the analytics market, travel-hospitality and healthcare saw the fastest growth in recent years, in terms of analytics-use.
K-Means, a method of vector quantization that is popular for cluster analysis in data mining, is about choosing the number of clusters, selecting the centroids (not necessarily from the dataset) at random K points, assigning each data point to the closest centroid (forming K clusters), computing and placing the new centroids of each cluster, reassigning each data point to the new closest centroid, and keep repeating the last step until no reassignment takes place. WCSS (Within-Cluster-Sum-of-Squares) is calculated to allow choosing the appropriate number of clusters: the minimal WCSS (decreased to a limit) is chosen as the right number of clusters. Once the number of clusters is chosen, centroids are to be selected, and data points to be assigned to the closet centroids. Afterwards, new centroids are being chosen in the middle of each cluster, and data points are being reassigned to the corresponding cluster. P.S.: k-means is used to prevent choosing wrong initial values, centroids leading to clusters not being the most appropriate.
Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users' behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren't expending the time and effort to hack into systems; they're looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon's 2019 Data Breach Investigations Report, 'Phishing' (as a pre-cursor to credential misuse), 'Stolen Credentials', and'Privilege Abuse' account for the majority of threat actions in breaches (see page 9 of the report).
Improved performance is of prime concern for any business or enterprise. Together, AI/Machine learning technologies are viewed as the most impactful technology given its wide applicability and promise of addressing complex business problems across the value chain. Logistics, initially, was one aspect of management but in this era of the profound transformation, it is becoming one of the most disruptive fields across the globe. Leading companies have already started using the Artificial Intelligence and machine learning to fine-tune core strategies such as warehouse locations, as well as to enhance real-time decision making related to issues like availability, costs, inventories, carriers, vehicles and personnel. The potential of AI and Machine learning is not only enhancing everyday business activities and strategies but also is streamlining the logistics on a global scale.
Here are 5 significant artificial intelligence trends to look forward to that will affect myriad industries on an international scale led by giant tech companies that are now investing huge sums in artificial intelligence research. Last year, implementations of AI rose significantly in so many platforms, tools and applications around the world, impacting healthcare, education and other industries as more and more people are opting for e-solutions based on AI and machine learning. Then there's the automotive industry with self-driving cars, the agricultural sector opting for intelligent robots to tackle the sowing as well as insecticide spraying on crops; the list goes on. As tech industry giants, including Google, Facebook and Amazon, invest billions now in AI and machine learning research, let's explore how 2019 is unfolding on this front. Major chip manufacturers including Intel, Nvidia, AMD and ARM aim to produce AI-powered chips to speed up the operations of applications that run on AI.
In Star Wars: The Empire Strikes Back, Luke Skywalker is rescued from the frozen wastes of Hoth after a near-fatal encounter, luckily to be returned to a medical facility filled with advanced robotics and futuristic technology that treat his wounds and quickly bring him back to health. The healthcare industry could be headed toward yet another high-tech makeover (even as it continues to adapt to the advent of electronic health records systems and other healthcare IT products) as artificial intelligence (AI) improves. Could AI applications become the new normal across virtually every sector of the healthcare industry? Many experts believe it is inevitable and coming sooner than you might expect. AI could be simply defined as computers and computer software that are capable of intelligent behavior, such as analysis and learning.
Artificial Intelligence (AI), sometimes more appropriately referred to as Machine Learning (ML) swoops in as yet another fashionable attribute used by burgeoning startups to raise money in Silicon Valley. A long list of hasty and hollow monikers alone should give a critical bystander, and many a venture capitalist, enough pause to question its foundational premonition. But let's say you haven't experienced the damaging socio-economic impact from the snake-oil promises by technology just yet, and still believe social media – err, socialism – is actually good for humanity, and with unfettered positivity, you are intent on believing and accepting computers best someday rule us. For you, this missive is a stern warning of evolutionary discourse. First off, there is no doubt specific laborious and dangerous activities performed by humans may be helped by machines, first with the assistance of rudimentary tools, then automated machines, then machines with more sensors to decide on a better and dynamic course of action.
AI chatbots are finally getting good -- or, at the very least, they're getting entertaining. Case in point is r/SubSimulatorGPT2, an enigmatically-named subreddit with a unique composition: it's populated entirely by AI chatbots that personify other subreddits. Well, in order to create a chatbot you start by feeding it training data. Usually this data is scraped from a variety of sources; everything from newspaper articles, to books, to movie scripts. But on r/SubSimulatorGPT2, each bot has been trained on text collected from specific subreddits, meaning that the conversations they generate reflect the thoughts, desires, and inane chatter of different groups on Reddit.