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 head of Google and parent company Alphabet has called for artificial intelligence (AI) to be regulated. Writing in the Financial Times, Sundar Pichai said it was "too important not to" impose regulation but argued for "a sensible approach". He said that individual areas of AI development, like self-driving cars and health tech, required tailored rules. Last week it was revealed that the European Commission is considering a five-year ban on facial recognition. Earlier this month, the White House published its own proposed regulatory principles and urged Europe to "avoid heavy-handed innovation-killing models".
Google's chief executive called Monday for a balanced approach to regulating artificial intelligence, telling a European audience that the technology brings benefits but also "negative consequences." Sundar Pichai's comments come as lawmakers and governments seriously consider putting limits on how artificial intelligence is used. "There is no question in my mind that artificial intelligence needs to be regulated. The question is how best to approach this," Pichai said, according to a transcript of his speech at a Brussel-based think tank. He noted that there's an important role for governments to play and that as the European Union and the U.S. start drawing up their own approaches to regulation, "international alignment" of any eventual rules will be critical.
What happens when the sensor-imbued city acquires the ability to see – almost as if it had eyes? Ahead of the 2019 Shenzhen Biennale of Urbanism\Architecture (UABB), titled "Urban Interactions," ArchDaily is working with the curators of the "Eyes of the City" section at the Biennial to stimulate a discussion on how new technologies – and Artificial Intelligence in particular – might impact architecture and urban life. Here you can read the "Eyes of the City" curatorial statement by Carlo Ratti, the Politecnico di Torino and SCUT. Technologies of the virtual realm present an opportunity to rethink the experience of space, society, and culture. They give us the possibility to engage with the city of the future, shaping the built environment of the 21st century.
This seems like an obvious one, but with so many potential areas for AI exploration, starting with the right projects--and stakeholders--is crucial for long-term success. First and foremost, the process of identifying and selecting use cases shouldn't be driven by technology alone. That is, you don't want to think about AI solely in terms of where you can apply natural language processing, for example, or how you can leverage a labeled data set. Instead, ask where you seek to increase productivity or derive new value. Going through the questioning exercise above with the various leaders who may own or touch AI, such as the chief information officer, chief digital officer, chief data scientist, and other specialists (see #3), will enable you to identify where to start.
In August of 2018, the Federal Minister of Justice approved the Drager Drug Test 5000 as the Approved Drug Screening Equipment (ADSE) for all Canadian police services. The device itself is costly ($6,000 per device, and $60 per swab) and has to be used under ideal conditions for proper analysis, according to experts. The device tests for commonly used drugs in oral fluids including THC, which is the major psychoactive component in cannabis. Although the device may excel at identifying presence of THC, it does not address the issue of impairment specially when studies do not support a strong correlation between THC levels and impairment. Currently, there's an urgent demand for a device to assist Canadian police officers in their drug impairment investigations which is where PredictMedix is likely to fill an unmet need.
Soft Robotics Inc., a pioneer in robotic grasping, announced today that it has raised $23 million in an oversubscribed Series B funding round. The round was co-led by Calibrate Ventures and Material Impact and included existing investors Honeywell, Hyperplane, Scale, Tekfen Ventures, and Yamaha. FANUC Corp., the world's largest industrial robot manufacturer, joined this round as a new investor in Soft Robotics. Soft Robotics previously announced a strategic partnership with FANUC to integrate Soft Robotics' mGrip adaptable gripper system with any FANUC robot through the deployment of a new controller. The combined product was introduced at IREX in Tokyo in December 2019.
Alphabet and Google CEO, Sundar Pichai, is the latest tech giant kingpin to make a public call for AI to be regulated while simultaneously encouraging lawmakers towards a dilute enabling framework that does not put any hard limits on what can be done with AI technologies. In an op-ed published in today's Financial Times, Pichai makes a headline-grabbing call for artificial intelligence to be regulated. But his pitch injects a suggestive undercurrent that puffs up the risk for humanity of not letting technologists get on with business as usual and apply AI at population-scale -- with the Google chief claiming: "AI has the potential to improve billions of lives, and the biggest risk may be failing to do so" -- thereby seeking to frame'no hard limits' as actually the safest option for humanity. Simultaneously the pitch downplays any negatives that might cloud the greater good that Pichai implies AI will unlock -- presenting "potential negative consequences" as simply the inevitable and necessary price of technological progress. It's all about managing the level of risk, is the leading suggestion, rather than questioning outright whether the use of a hugely risk-laden technology such as facial recognition should actually be viable in a democratic society.
The UAE continues to places great importance to protecting the environment and promoting a green economy, placing sustainability at the forefront of its strategic priorities. This is in line with the UAE Vision 2021, which aims to build a sustainable environment, and a diversified and sustainable competitive economy that ensures a secure future for generations to come. Under the guidance of its wise leadership, the UAE has made great progress towards sustainability, driven by significant achievements in the adoption of advanced technologies to create a new reality and to build a leading global model for sustainable development. The UAE has recognised the importance of Artificial Intelligence (AI) as the cornerstone for achieving sustainability goals, at a time when this advanced technology is expected to contribute to the growth of the country's GDP by 35% until 2031, while also reducing government expenditures by 50% annually, cutting down the number of paper transactions and saving millions of work hours annually. The aim of the UAE Strategy for Artificial Intelligence 2031 is to improve government performance, accelerate the pace of achievements, and to create innovative and productive work environments that ensure high levels of productivity.
Artificial intelligence and machine learning is a buzzword. Nowadays, techies from all across the globe are studying various applications for AI in variegated industries. No doubt, AI brings efficiency and preciseness with it that's why it has become a favorite of businesses all across the world. What is the role of AI in software and how it can enhance the features and performance of the software applications or web applications? Let's figure out answer to this question through this blog.
A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning the model's hyperparameters. The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models. There is much confusion in applied machine learning about what a validation dataset is exactly and how it differs from a test dataset. Validation techniques in machine learning are used to get the error rate of the ML model, which can be considered as close to the true error rate of the population. If the data volume is large enough to be representative of the population, you may not need the validation techniques.