Environmental, social and governance (ESG) factors should be key considerations for CTOs and technology leaders scaling next generation companies from day one. Investors are increasingly prioritizing startups that focus on ESG, with the growth of sustainable investing skyrocketing. It's simple: Consumers are no longer willing to support companies that don't prioritize sustainability. According to a survey conducted by IBM, the COVID-19 pandemic has elevated consumers' focus on sustainability and their willingness to pay out of their own pockets for a sustainable future. In tandem, federal action on climate change is increasing, with the U.S. rejoining the Paris Climate Agreement and a recent executive order on climate commitments.

In a landmark decision, an Australian court has set a groundbreaking precedent, deciding artificial intelligence (AI) systems can be legally recognised as an inventor in patent applications. That might not sound like a big deal, but it challenges a fundamental assumption in the law: that only human beings can be inventors. The AI machine called DABUS is an "artificial neural system" and its designs have set off a string of debates and court battles across the globe. On Friday, Australia's Federal Court made the historic finding that "the inventor can be non-human". It came just days after South Africa became the first country to defy the status quo and award a patent recognising DABUS as an inventor. AI pioneer and creator of DABUS, Stephen Thaler, and his legal team have been waging a ferocious global campaign to have DABUS recognised as an inventor for more than two years.

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Artificial Intelligence (AI) has been swiftly infiltrating each aspect of our days and civilization. AI plays a notable role in human society, from hiring employees to the healthcare system and even criminal justice. Shaped by human norms and individual preferences, AI algorithms can lead to biases that are frequently subliminal, flawed. It is the outcome of the inadequate view of the world that individuals possess. Bias in AI is what we encounter when a machine-learning algorithm exhibits a systematically inaccurate result.

Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based communication scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compressed residual vector derived from the reference. In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding. Extensive simulations reveal that the communication cost can be reduced up to 99% with even better learning performance when compared with other baseline methods.

The AI machine called DABUS is an "artificial neural system" and its designs have set off a string of debates and court battles across the globe.

Responsible AI, Ethical AI, AI for social good -- I am sure you must have heard these terms at some point or the other, whether you are a Data Scientist or not. "The development of full artificial intelligence could spell the end of the human race." And there my journey of understanding this critical aspect of the AI foundation started. I used to wonder how to relate ethics with AI which is just a series of algorithms, when, in fact, we have not been able to apply ethical behavior among ourselves. As per the AI index report published by the Stanford University Institute for Human-Centered AI, cybersecurity and regulatory compliance are among the top risks identified by AI/ML-oriented organizations.

SAN FRANCISCO, CA - SEPTEMBER 07: Google AI Research Scientist Timnit Gebru speaks onstage during ... [ ] Day 3 of TechCrunch Disrupt SF 2018 at Moscone Center on September 7, 2018 in San Francisco, California. 'Taking On Tech is an informative series that explores artificial intelligence, data science, algorithms, and mass censorship. In this inaugural report, For(bes) The Culture kicks things off with Dr. Timnit Gebru, a former researcher and co-lead of Google's Ethical AI team. When Gebru was forced out of Google after refusing to retract a research paper that was already cleared by Google's internal review process, a conversation about the tech industry's inherent diversity problem resurfaced. The paper raised concerns on algorithmic bias in machine learning and the latent perils that AI presents for marginalized communities. Around 1,500 Google employees signed a letter in protest, calling for accountability and answers over her unethical firing.

SAN JOSE, Calif., July 29, 2021--(BUSINESS WIRE)--Velodyne Lidar, Inc. (Nasdaq: VLDR, VLDRW) today announced a new software development kit which allows customers to utilize the advanced capabilities of Velodyne's Vella lidar perception software in their autonomous solutions. The Vella Development Kit (VDK) enables companies to accelerate time to market for bringing cutting-edge lidar capabilities to autonomous vehicles, advanced driver assistance systems (ADAS), mobile delivery devices, industrial robotics, drones and more. This press release features multimedia. The Vella Development Kit (VDK) from Velodyne Lidar allows customers to use the advanced capabilities of Vella lidar perception software in autonomous solutions. VDK enables companies to accelerate time to market for bringing cutting-edge lidar capabilities to autonomous vehicles, advanced driver assistance systems (ADAS), mobile delivery devices, industrial robotics, drones and more.

The development of artificial general intelligence is considered by many to be inevitable. What such intelligence does after becoming aware is not so certain. To that end, research suggests that the likelihood of artificial general intelligence becoming hostile to humans is significant enough to warrant inquiry into methods to limit such potential. Thus, containment of artificial general intelligence is a timely and meaningful research topic. While there is limited research exploring possible containment strategies, such work is bounded by the underlying field the strategies draw upon. Accordingly, we set out to construct an ontology to describe necessary elements in any future containment technology. Using existing academic literature, we developed a single domain ontology containing five levels, 32 codes, and 32 associated descriptors. Further, we constructed ontology diagrams to demonstrate intended relationships. We then identified humans, AGI, and the cyber world as novel agent objects necessary for future containment activities.