HMP Wandsworth gets green light to use AI after team sent in to find'quick fixes' after spate of mistakes Artificial intelligence chatbots could be used to stop prisoners from being mistakenly released from jail, a justice minister told the House of Lords on Monday. James Timpson said HMP Wandsworth had been given the green light to use AI after a specialised team was sent in to find "some quick fixes". A double manhunt was launched last week after the incorrect release of a sex offender and a fraudster from the prison in south-west London. Release errors over the past fortnight have been seized upon by opposition MPs as evidence of the helplessness of ministers in the face of chaos within the criminal justice system. David Lammy, the justice secretary, is expected to address parliament about the number of missing prisoners when MPs return on Tuesday. It is understood that AI could be used to read and process paper documents; help staff cross-reference names to ensure that inmates are no longer hiding their past crimes behind aliases; merge different datasets; and calculate release dates and sentences.

An Arizona state bill would allow local and state authorities to shoot down drones used by drug cartels. House Bill 2733, sponsored by Republican state Rep. David Marshall, would provide qualified immunity to authorities for injuries that may be caused by taking out an "unmanned" drone within 30 miles of the southern border. Cartels regularly use drones as a tool to monitor law enforcement activity around the border in hopes of evading them in their own smuggling operations, even using the technology to send their drugs into the country. Although the situation at the border is calming down, there are still plenty of issues to tackle when it comes to crime, according to one sheriff. A drone reportedly flew into a crowd of Boston Celtics fans Tuesday night at an outdoor party.

United States President Joe Biden has reportedly lifted restrictions on Kyiv on the use of long-range missiles, which means Ukrainian forces may fire American-made missiles inside Russian territory for the first time. The move, which comes weeks before Biden leaves office and hours after massive Russian missile and drone attacks, has angered the Kremlin, which accused Washington of "throwing oil on the fire". Kremlin spokesman Dmitry Peskov said the decision would mean Washington's direct involvement in the conflict, echoing a similar sentiment expressed by President Vladimir Putin in September. The White House and President-elect Donald Trump have not commented yet, but Trump's eldest son, Donald Trump Jr, said: "The military industrial complex seems to want to make sure they get World War III going before my father has a chance to create peace and save lives." The elder Trump, who takes office on January 20, repeatedly pledged during his campaign to negotiate an end to the Ukraine war.

Elon Musk's Neuralink has been given a green light to implant its brain chip in a second patient after fixing issues that struck during the first human trial. The US Food and Drug Administration (FDA) approved the next person on Monday, signing off on the company's planned updates that included embedding some of the device's ultrathin wires deeper into the brain. Neuralink revealed this month that some of 64 threads detached from the first patient's brain, causing the chip to malfunction - nearly ending the trial that began in January. A report by Reuters cited'five people familiar with the matter' had claimed that this issue had been'known about for years' from animal testing. This is a developing story... more updates to come.

It's been two years since Google first debuted Project Green Light, a novel means of addressing the street-level pollution caused by vehicles idling at stop lights. At its Sustainability '23 event on Tuesday, the company discussed some of the early findings from that program and announced another wave of expansions for it. Green Light uses machine learning systems to comb through Maps data to calculate the amount of traffic congestion present at a given light, as well as the average wait times of vehicles stopped there. That information is then used to train AI models that can autonomously optimize the traffic timing at that intersection, reducing idle times as well as the amount of braking and accelerating vehicles have to do there. It's all part of Google's goal to help its partners collectively reduce their carbon emissions by a gigaton by 2030.

Each time a driver in Seattle meets a red light, they wait about 20 seconds on average before it turns green again, according to vehicle and smartphone data collected by analytics company Inrix. The delays cause annoyance and expel in Seattle alone an estimated 1,000 metric tons or more of carbon dioxide into the atmosphere each day. With a little help from new Google AI software, the toll on both the environment and drivers is beginning to drop significantly. Seattle is among a dozen cities across four continents, including Jakarta, Rio de Janeiro, and Hamburg, optimizing some traffic signals based on insights from driving data from Google Maps, aiming to reduce emissions from idling vehicles. The project analyzes data from Maps users using AI algorithms and has initially led to timing tweaks at 70 intersections.

Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooperative multi-objective architecture called Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG), which estimates multiple reward terms for traffic signal control optimization using age-decaying weights. Our approach involves two types of agents: one focuses on optimizing local traffic at each intersection, while the other aims to optimize global traffic throughput. We evaluate our method using real-world traffic data collected from an Asian country's traffic cameras. Despite the inclusion of a global agent, our solution remains decentralized as this agent is no longer necessary during the inference stage. Our results demonstrate the effectiveness of MOMA-DDPG, outperforming state-of-the-art methods across all performance metrics. Additionally, our proposed system minimizes both waiting time and carbon emissions. Notably, this paper is the first to link carbon emissions and global agents in traffic signal control.

FOX Business senior correspondent Charlie Gasparino joined'MediaBuzz' to discuss growing concerns surrounding the future of artificial intelligence. In a remarkable leap forward for neuroscience and technological innovation, Elon Musk's brain implant company Neuralink has officially received approval from the FDA to begin the first human trials of its groundbreaking brain-computer interface. The announcement was made via Twitter. However, no details were given at the time of publishing about when the clinical trials or recruitment would begin. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Neuralink's overall goal is to have the human nervous system be able to communicate with computers.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections. This same approach of HIL testing followed by testing in real intersections using our experimental vehicle is later extended to the GLOSA application. The GLOSA application that is tested in this paper has both an optimal speed advisory for passing at the green light and also includes a red light violation warning system. The paper presents the HIL and experimental vehicle evaluation systems, information about RLVW and GLOSA and HIL simulation and road testing results and their interpretations.

Traffic signal control is a challenging real-world problem aiming to minimize overall travel time by coordinating vehicle movements at road intersections. Existing traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods. Specifically, the periodicity of green/red light alternations can be considered as a prior for better planning of each agent in policy optimization. To better learn such adaptive and predictive priors, traditional RL-based methods can only return a fixed length from predefined action pool with only local agents. If there is no cooperation between these agents, some agents often make conflicts to other agents and thus decrease the whole throughput. This paper proposes a cooperative, multi-objective architecture with age-decaying weights to better estimate multiple reward terms for traffic signal control optimization, which termed COoperative Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (COMMA-DDPG). Two types of agents running to maximize rewards of different goals - one for local traffic optimization at each intersection and the other for global traffic waiting time optimization. The global agent is used to guide the local agents as a means for aiding faster learning but not used in the inference phase. We also provide an analysis of solution existence together with convergence proof for the proposed RL optimization. Evaluation is performed using real-world traffic data collected using traffic cameras from an Asian country. Our method can effectively reduce the total delayed time by 60\%. Results demonstrate its superiority when compared to SoTA methods.