With the rise in the wholesale adoption of Deep Learning (DL) models in nearly all aspects of society, a unique set of challenges is imposed. Primarily centered around the architectures of these models, these risks pose a significant challenge, and addressing these challenges is key to their successful implementation and usage in the future. In this research, we present the security challenges associated with the current DL models deployed into production, as well as anticipate the challenges of future DL technologies based on the advancements in computing, AI, and hardware technologies. In addition, we propose risk mitigation techniques to inhibit these challenges and provide metrical evaluations to measure the effectiveness of these metrics.

Fox News correspondent Matt Finn has the latest on the impact of AI technology that some say could outpace humans on'Special Report.' Former Google CEO Eric Schmidt said the tech industry will face a "reckoning" over artificial intelligence, comparing the potential dangers of the technology to the risks associated with social media when the platforms were first rolled out years ago. "What happened with social media is we, including myself, just offered social media because we had a simple model of how humans would use social media. But, instead, look at how social media was used to interfere in elections, to cause harm. People have died over social media," Schmidt told ABC News on Sunday.

Artificial intelligence (AI) has become a hot topic for countries worldwide, and both public- and private-sector organizations have already started leveraging it as a response to continuous digital disruption. According to IDC's 2022 Artificial Intelligence Spending Guide, global AI spending reached $88.6 billion in 2021, and it is forecast to grow at a compound annual growth rate (CAGR) of 25.6% over the 2021–2025 period. Canada, China, and the United States are among the countries in which many organizations began their AI journeys early, supported by government initiatives. Saudi Arabia is no different in terms of its commitment to becoming an AI powerhouse. As an extension of the country's Vision 2030, the Saudi Data and AI Authority (SDAIA) was established in 2019, followed by the release of the National Strategy for Data and AI in 2020.

Imagine Elon Musk getting dragged to trial every time a Tesla car runs a red light? Well, watchdogs around the globe are proposing legislation to hold the manufacturers accountable, and not the human behind the wheel, in the event of offences involving self-driving cars. According to Annual Global Road Crash Statistics, around 1.35 million people die in road crashes each year globally. Around 3,700 people lose their lives daily on the roads, the research said. In India, around 375,000 accidental deaths were registered in 2020, of which 35% were in road crashes, data from National Crime Records Bureau showed.

One of the biggest issues with current "AI" is that it is very narrow. It's a programme to interpret data, or to drive a car, or to play chess, or to act as a carer, or to draw a picture. But almost any human can make a stab at doing all of those, and with a bit of training or learning can get better at them all. If we want to get closer to the SF ideal of AI, and also to make it a lot easier to use AI in the world around us, then what we really need is a "general purpose AI" -- or what is commonly called Artificial General Intelligence (AGI). There is a lot of research going into AGI at the moment in academic institutions and elsewhere (for examples/comment see KORTELING2021, Stanford AI100 Report), but it is really early days.

While the computational processing of Kurdish has experienced a relative increase, the machine translation of this language seems to be lacking a considerable body of scientific work. This is in part due to the lack of resources especially curated for this task. In this paper, we present the first large scale parallel corpus of Central Kurdish-English, Awta, containing 229,222 pairs of manually aligned translations. Our corpus is collected from different text genres and domains in an attempt to build more robust and real-world applications of machine translation. We make a portion of this corpus publicly available in order to foster research in this area. Further, we build several neural machine translation models in order to benchmark the task of Kurdish machine translation. Additionally, we perform extensive experimental analysis of results in order to identify the major challenges that Central Kurdish machine translation faces. These challenges include language-dependent and-independent ones as categorized in this paper, the first group of which are aware of Central Kurdish linguistic properties on different morphological, syntactic and semantic levels. Our best performing systems achieve 22.72 and 16.81 in BLEU score for Ku$\rightarrow$EN and En$\rightarrow$Ku, respectively.

Last year proved to be one of the most difficult periods of time for companies and governments all around the world due to COVID-19, and the challenges persist as we move further into 2021. Some industries were hit harder than others, like the industrial sector, where firms employ nearly 25 million people worldwide and generate $9.3 trillion in annual revenue. These challenges impact the AI domain as well, especially as consumer behavior continues to change. While some may believe everything will return to normal in due time, many of these changes are likely here to stay, and companies will focus on them when developing their AI and data strategies in 2021. According to Algorithmia's '2021 Enterprise Trends in Machine Learning,' the overarching theme this year is that organizations will place greater importance on artificial intelligence (AI) and machine learning (ML) initiatives.

Many tasks that we do on a regular basis, such as navigating a city, cooking a meal, or loading a dishwasher, require planning over extended periods of time. Accomplishing these tasks may seem simple to us; however, reasoning over long time horizons remains a major challenge for today's Reinforcement Learning (RL) algorithms. While unable to plan over long horizons, deep RL algorithms excel at learning policies for short horizon tasks, such as robotic grasping, directly from pixels. At the same time, classical planning methods such as Dijkstra's algorithm and A search can plan over long time horizons, but they require hand-specified or task-specific abstract representations of the environment as input. To achieve the best of both worlds, state-of-the-art visual navigation methods have applied classical search methods to learned graphs.

For more than four decades, University of California, San Diego, Professor of Physics Patrick H. Diamond and his research group have been advancing our understanding of fundamental concepts in plasma physics. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Extreme Science and Engineering Discovery Environment (XSEDE)-allocated Comet supercomputer at the San Diego Supercomputer Center at UC San Diego to showcase how machine learning produced a new model for plasma turbulence. Plasmas have many applications, including fusion energy. When light nuclei fuse together, the mass of the products is less than that of the reactants, and the missing mass becomes energy – hence Albert Einstein's famous E mc2 equation. In order for this to occur, temperatures must literally reach astronomical levels, such as those found in the Sun's core.

We see news about Machine learning everywhere. Indeed, there is a lot of potential in Machine learning. According to Gartner's predictions, "Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization" and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production. Why is it like that? Why do so many projects fail?