It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".
On the morning of November 9, 2016, the world woke up to the shocking outcome of the U.S. Presidential election: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots--automated social media accounts mimicking humans--played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory.16,19 In the aftermath of the 2016 U.S. elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects these malicious actors seem to have on our societies.27,29 This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times--during the run-up to the 2020 U.S. elections--the question appears as more crucial than ever. Particularly so, also in light of the recent reported tampering of the electoral debate by thousands of AI-powered accounts.a What struck social, political, and economic analysts after 2016--deception and automation--has been a matter of study for computer scientists since at least 2010. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavors in social bot detection can also inform strategies for detecting and mitigating the effects of other--more recent--forms of online deception, such as strategic information operations and political trolls.
AI algorithms namely machine learning and deep learning algorithms are powerful tools. However, they suffer from some limitations which require that human analytics should work with AI tools collaboratively. In this post, we will look at the most important shortcoming of Artificial Intelligence in the cybersecurity domain. Though Benefits are more, AI also comprises limits . Cybercriminals are creative and come up with new ways to conduct cyberattacks.
To learn the best, you must learn from the finest. Geoffrey Hilton is called the Godfather of Deep Learning in the field of data science. Mr. Hinton is best known for his work on neural networks and artificial intelligence. A Ph.D. in artificial intelligence, he is accredited for his exemplary work on neural nets. The co-founder of the term, "Data Science", Jeff Hammerbacher developed methods and techniques for capturing, storing and analysing a large amount of data.
One study estimated that pharmaceutical companies spent US$2·6 billion in 2015, up from $802 million in 2003, for the development of a new chemical entity approved by the US Food and Drug Administration (FDA). N Engl J Med. 2015; 372: 1877-1879 The increasing cost of drug development is due to the large volume of compounds to be tested in preclinical stages and the high proportion of randomised controlled trials (RCTs) that do not find clinical benefits or with toxicity issues. Given the high attrition rates, substantial costs, and low pace of de-novo drug discovery, exploiting known drugs can help improve their efficacy while minimising side-effects in clinical trials. As Nobel Prize-winning pharmacologist Sir James Black said, "The most fruitful basis for the discovery of a new drug is to start with an old drug". New uses for old drugs.
This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.
Cyber threats continue to escalate in both sophistication and volume. Traditional approaches to threat detection, however, are no longer sufficient to ensure protection. Correspondingly, machine learning (ML) has proven highly effective at identifying and warding off cyber attacks. Machine learning's power is the result of three factors: data, compute power and algorithms. Due to its very nature, the cyber field produces substantial amounts of data.
Deep learning – an advanced form of artificial intelligence – can be more accurate in predicting outcomes, compared with conventional econometric approaches, according to research from Bank of Korea (BoK). The research paper tested predictions of monthly exports from Korea and daily Korean won-US dollar exchange rates. It found that deep learning approaches produced better results even with the sorts of non-granular data sets that are normally used for conventional econometric models.
So you're interested in AI? Then join our online event, TNW2020, where you'll hear how artificial intelligence is transforming industries and businesses. In 2018, a big fan of Nicholas Cage showed us what The Fellowship of the Ring would look like if Cage starred as Frodo, Aragorn, Gimly, and Legolas. The technology he used was deepfake, a type of application that uses artificial intelligence algorithms to manipulate videos. Deepfakes are mostly known for their capability to swap the faces of actors from one video to another. They first appeared in 2018 and quickly rose to fame after they were used to modify adult videos to feature the faces of Hollywood actors and politicians.
In 2018, a big fan of Nicholas Cage showed us what The Fellowship of the Ring would look like if Cage starred as Frodo, Aragorn, Gimly, and Legolas. The technology he used was deepfake, a type of application that uses artificial intelligence algorithms to manipulate videos. Deepfakes are mostly known for their capability to swap the faces of actors from one video to another. They first appeared in 2018 and quickly rose to fame after they were used to modify adult videos to feature the faces of Hollywood actors and politicians. In the past couple of years, deepfakes have caused much concern about the rise of a new wave of AI-doctored videos that can spread fake news and enable forgers and scammers.