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The universe comes off as a vast and immeasurable entity whose depths are imperceptible to Earthlings. But in the pursuit of simplifying all that surrounds us, scientists have made great strides in understanding the space we inhabit.

Now, Japanese astronomers have developed an astounding technique to measure the universe. Using artificial intelligence, scientists were able to remove noise in astronomical data which iscaused by random variations in the shapes of galaxies.

What did the scientists do?

Scientists used supercomputer simulations and tested large mock data before performing the same on real data from space. After extensive testing, scientists used the tool on data derived from Japans Subaru Telescope.

To their surprise, it worked! The results that followed remained largely in sync withthe currently accepted models of the universe. If employed on a bigger scale, the tool could help scientists analyse expansive data from astronomical surveys.

Current methods cannot effectively get rid of the noise which pervades all data from space. To avoid interference from noise data, the team used the worlds most advanced astronomy supercomputer called ATERUI II.

Using real data from the Subaru Telescope, they generated 25,000 mock galaxy catalogues.

Also read:Explosion on Sun equivalent to millions of hydrogen bombs causes biggest solar flare in 4 years

What's causing data distortion?

All data from space can be distorted by the gravity of whats in the foreground eclipsing its background. This is called gravitational lensing. Measurements of such lensing is used to better understand the universe. Essentially, a galaxy directly visible to us could be manipulating data about what lies behind it.

But its difficult to differentiate oddly-looking galaxies from distorting ones that manipulate data. Its called shape noise and regularly gets in the way of understanding the universe.

Based on these understandings, scientists added noise to the artificial data sets and trained AI to recover lensing data from the mock data. The AI was able to highlight previously unobservable details from this data.

Building on this, scientists used the AI model on the real world, covering 21 square degrees of the sky. They found that the details registered about the foreground were actually consistent with existing knowledge about the cosmos.

Also read:'Orphan cloud' bigger than Milky Way found in 'no-galaxy's land' by scientists

The research was published in the April issueof Monthly Notices of the Royal Astronomical Society.

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Astronomers use artificial intelligence to reveal the true shape of universe - WION

The US Government Accountability Office (GAO) released a public report Tuesday stating that most federal agencies that use facial recognition technology systems are unaware of the privacy and accuracy-related risks that such systems pose to federal agencies and the American public.

After holding a forum on AI oversight, the GAO developed an artificial intelligence (AI) accountability framework focused on governance, data, performance, and monitoringto help federal agencies and others use AI responsibly.

Of the 42 federal agencies that the GAO surveyed, 20 reported owning or using facial recognition technology systems. The GAO confirmed that most federal agencies that use facial recognition technology are unaware of which AI systems their employees use; hence, the GEO remarked that these agencies have not fully assessed the potential risks of using these systems, such as risks related to privacy and accuracy. Consequently, the GAO also noted that the use of these AI systems can pose [n]umerous risks to federal agencies and the public.

The GAO, which has provided objective, non-partisan information on government operations for a century, said:

AI is a transformative technology with applications in medicine, agriculture, manufacturing, transportation, defense, and many other areas. It also holds substantial promise for improving government operations. Federal guidance has focused on ensuring AI is responsible, equitable, traceable, reliable, and governable. Third-party assessments and audits are important to achieving these goals. However, AI systems pose unique challenges to such oversight because their inputs and operations are not always visible.

In March, the American Civil Liberties Union (ACLU) requested information on how intelligence agencies use AI for national security. In its request, the ACLU warned that AI systems can be biased against marginalized communities and may pose a risk to civil rights.

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US government watchdog finds federal use of artificial intelligence poses threat to federal agencies and public - JURIST

Is removing subjective human choice from HR decisions going to create more problems than it solves?

We are all very aware of human failings when it comes to people management in the workplace. Everything from unconscious bias through to wholly intentional discrimination. To that extent the handing over of some management decisions to algorithms and AI (a term for which there is no common definition but which can cover a scenario where many algorithms work together with the ability to improve their own function) may seem like a no brainer. The technology is certainly out there and being aggressively marketed.

The rise of the gig economy is tied into the increase in the use of algorithms and AI, as the software began to be used on platforms such as Uber in an attempt to optimise the deployment of workers. It has also been adopted in many other sectors and workplaces - including many global brands such as Amazon and Unilever. Common uses include recruitment, workforce management (eg task or shift allocation) and performance review. The benefits to business include faster decision making, more efficient workforce planning, improved speed of recruitment and the obvious reduction in opportunity for human bias.

However, the very nature of "algorithmic management" means increases in monitoring and collection of data upon which the automated, or semi-automated, decisions are made. This is particularly so for performance monitoring and brings with it the risk of monitoring and processing data without appropriate consent. Removing humans from the decision making process entirely also creates the potential for lack of accountability. Additionally, if bias is embedded into an algorithm this will increase rather than decrease the risk of discrimination.

In May 2021, the TUC and the AI Consultancy published a report - Technology Managing People - the legal implications - highlighting exactly these sorts of issues and calling for legal reform. One focus of the report is the lack of transparency in decision making that comes with the use of AI - the basis of the decision being made is often an unknown to those that the decisions are being made about. The report points out that where it is difficult to identify when, how and by whom discrimination is introduced, it becomes more difficult for workers and employees to enforce their rights to protection from discrimination.

Other issues identified by the report include a lack of guidance for employers explaining when workers' privacy rights under the ECHR may be infringed by AI and the risks posed by the lack of clarity in the application of the UK GDPR to the use of AI within the employment relationship. Although unfair dismissal rights provide some protection from dismissals that are factually inaccurate or opaque, and this could be applied to an AI based decision making processes, the need for qualifying service means this protection is not universal. The UK GDPR also provides protection for employees via the requirement, amongst other things, for all personal data that is processed by AI to be accurate but a complaint arising from such a breach cannot, in itself, be brought within the employment tribunal system.

The TUC report makes a number of recommendations on how theses issues can be overcome. The provision of statutory guidance on how to avoid discrimination in the use of AI and on the interplay between AI and workers' rights to privacy; the introduction of a statutory right not to subjected to detrimental treatment (including dismissal) due to the processing of inaccurate data; the right to "explainability" in relation to high risk AI systems; and a change to the UK's data protection regime to state that discriminatory data processing is always unlawful are amongst those recommendations. However, even if any of these proposals are acted upon by UK Government, they will take time to implement.

For employers looking for ideas on good practice in this area, the policy paper published by ACAS - My boss the algorithm: an ethical look at algorithms in the workplace - is a good starting point, although it should be noted this is not ACAS guidance. The recommendations look at what can be done at a human level within a business. Key to those recommendations is the need for human input - algorithms being used alongside human management rather than replacing it. This is something that the TUC report also picks up on, albeit more formally suggesting that there should be a comprehensive and universal right to human review of AI decisions made in the workplace that are "high risk". Both reports also highlight the need for good communication between employers and employees (or their representatives) to ensure technology is effectively used to improve workplace outcomes.

Given the growth in this area, further regulation to manage the use of algorithms and AI in the workplace seems inevitable. In the meantime, businesses making use of this technology need to fully understand exactly what it does, where there are risks to its use and the importance of transparency in its use.

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Artificial intelligence and algorithms in the workplace - Lexology

I have an unusually good memory, especially for symbols, words, and text, but since I dont use regular expressions (ahem) regularly, theyre one of those parts of computer programming and HTML/EPUB editing that I find myself relearning over and over each time I need it. How did something this arcane but powerful even get started? Naturally, its creators were trying to discover (or model) artificial intelligence.

Thats the crux of this short history of regex by Buzz Andersen over at Why is this interesting?

The term itself originated with mathematician Stephen Kleene. In 1943, neuroscientist Warren McCulloch and logician Walter Pitts had just described the first mathematical model of an artificial neuron, and Kleene, who specialized in theories of computation, wanted to investigate what networks of these artificial neurons could, well, theoretically compute.

In a 1951 paper for the RAND Corporation, Kleene reasoned about the types of patterns neural networks were able to detect by applying them to very simple toy languagesso-called regular languages. For example: given a language whose grammar allows only the letters A and B, is there a neural network that can detect whether an arbitrary string of letters is valid within the A/B grammar or not? Kleene developed an algebraic notation for encapsulating these regular grammars (for example, a*b* in the case of our A/B language), and the regular expression was born.

Kleenes work was later expanded upon by such luminaries as linguist Noam Chomsky and AI researcher Marvin Minsky, who formally established the relationship between regular expressions, neural networks, and a class of theoretical computing abstraction called finite state machines.

This whole line of inquiry soon falls apart, for reasons both structural and interpersonal: Pitts, McCullough, and Jerome Lettvin (another early AI researcher) have a big falling out with Norbert Wiener (of cybernetics fame), Minsky writes a book (Perceptrons) that throws cold water on the whole simple neural network as model of the human mind thing, and Pitts drinks himself to death. Minsky later gets mixed up with Jeffrey Epsteins philanthropy/sex trafficking ring. The world of early theoretical AI is just weird.

But! Ken Thompson, one of the creators of UNIX at Bell Labs comes along and starts using regexes for text editor searches in 1968. And renewed takes on neural networks come along in the 21st century that give some of that older research new life for machine learning and other algorithms. So, until Skynet/global warming kills us all, it all kind of works out? At least, intellectually speaking.

(Via Jim Ray)

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A History of Regular Expressions and Artificial Intelligence - kottke.org

July 6, 2021

Paris and Amsterdam Nexyad, the embedded, real-time platform for aggregating on-board data, and HERE Technologies, the leading location data and technology platform, are now working together to apply cognitive AI to road safety.

On-board data

Nexyad uses cognitive AI to aggregate extensive data sources in a vehicle in real time and interprets them to assess whether a certain driving behaviour is appropriate given the surrounding context. Nexyads assessment, that can easily be delivered to a driver via a mobile phone, can be calculated from four sets of data only: HERE map, Global Navigation Satellite System, electronic horizon and acceleration. Nexyads platform is also scalable and can aggregate data from Advanced Driving Assistant Systems (ADAS) sensors to include camera, radar and lidar, weather (visibility and temperature), and traffic data.

Maximum speed recommended for a specific vehicle at a specific time

Nexyads real-time data aggregation platform provides two output values 20 times every second: the lack of caution of the driver and the maximum speed recommended given the road conditions legal speed limit, road roughness, topography of the road, weather, and traffic. Nexyad bases its analysis on several thousand road accident reports, using a set of rules from modern hybrid AI which includes knowledge-based systems, deep learning, neural gas, PAC (Possibly Approximatively Correct) learning, game theory, reinforcement learning, possibility theory and fuzzy logic.

By recommending a maximum cautious speed based on real-time data and context-specific to every single vehicle, driver and driving environment, Nexyads approach goes much further than the European requirement for vehicles to be aware of the legal speed limit on each road segment (Intelligent Speed Assist). Nexyads safety coach called SafetyNex acts as a true co-pilot for the driver as it provides real-time guidance so as to anticipate possible emergency situations ahead that may lead to an accident. This proactive coaching activates while driving and has been demonstrated to reduce accident rates by at least 25%1.

A risk score for drivers and autonomous shuttles

Nexyad provides drivers with a score that reflects the risk associated with their driving behavior. Nexyads platform is being used by insurers to provide recommendations to drivers and generate a risk profile. For example, Brightmile, a start-up incubated by Kamet, AXAs insurer tech studio, is using Nexyads SafetyNex software as one of the parameters of their smartphone-based telematics solutions for fleets. Indias Montbleu also relies on Nexyads SafetyNex for its smartphone-based app ROAD-Drive it Safe. Milla, the French autonomous electric shuttle, uses SafetyNex to adapt vehicle speeds according to driving conditions and alerts the service operator (on-board and/or off-board) to take appropriate action when the level of risk is estimated too high.

Nexyad has started to integrate the HERE HD Live Map to provide OEMs with Predictive Automotive Cruise Controlservices whereby appropriate speeds are not only being recommended but automatically implemented. Moving forward, connected vehicles will use SafetyNex to assess the level of caution of their own driving and will be able to adopt the appropriate speed even in unknown road conditions.

We found that the maps from HERE are accurate to the centimetre and constantly updated to the second. Every detail counts for us - the topography of the road, the exact positioning of the crossing, the location of a school. With our mission being to save lives, we cannot settle for anything less than the best, says Grard Yahiaoui, CEO of Nexyad.

Nexyads SafetyNex software is one of a kind not only does it provide a score for the lack of caution of the driver, based on the environment in real time, it also recommends an appropriate driving speed. This is the future for Predictive Automotive Cruise Control systems, insurers and autonomous vehicles, says Gilles Martinelli, Director of Automotive at HERE Technologies.

Demos of Nexyads safety coach SafetyNext can be found here and here.

Media Contacts

Adrianne Montgobert +49 151 72 11 67 81 adrianne.montgobert@here.com

Gerard Yahiaoui gyahiaoui@nexyad.net

About HERE Technologies

HERE, a location data and technology platform, moves people, businesses and cities forward by harnessing the power of location. By leveraging our open platform, we empower our customers to achieve better outcomes - from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely. To learn more about HERE, please visit here.com and 360.here.com

About Nexyad

Nexyad is a Paris-based AI start-up founded by former professors and researchers of AI and applied maths, specialized in road safety. We propose a unique next generation hybrid cognitive AI that improves road safety, avoids emergency situations and road accidents, and saves lives. We help our customers integrate our technology into their valuable products for insurance & fleets, for automotive Safety Coach or Predictive ACC, and for Autonomous Vehicles aware of their level of caution in driving regarding context and able to adapt to unknown situations to keep caution level high enough.

1 Impact assessment on road accident rate reduction of NEXYAD cognitive AI SafetyNex, available on demand.

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Nexyad and HERE improve vehicle safety with next generation, cognitive artificial intelligence - GlobeNewswire