We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to automation. The effects on wages depend on a race between automation and capital accumulation. If the distribution of task complexity exhibits a sufficiently thick infinite tail, then there is always enough work for humans, and wages may rise forever. By contrast, if the complexity of tasks that humans can perform is bounded and full automation is reached, then wages collapse. But declines may occur even before if large-scale automation outpaces capital accumulation and makes labor too abundant. Automating productivity growth may lead to broad-based gains in the returns to all factors. By contrast, bottlenecks to growth from irreproducible scarce factors may exacerbate the decline in wages.

Remember the Apple car rumors? Project Titan, as it's apparently called, is still progressing, with perhaps, a dose of reality. Bloomberg's Mark Gurman says the company's decade-old project has shifted from creating a fully self-driving car to an EV more like Tesla's. The car's autonomous features have reportedly been downgraded from a Level 5 system (full automation) to a Level 4 system (full automation in some circumstances) -- and now to Level 2 (partial automation). For context, Tesla's Autopilot is Level 2. Level 2 doesn't have a formal description yet.

Apple has reportedly scaled back its automotive aspirations, at least for now. Bloomberg's Mark Gurman says the company's decade-old vehicle project has pivoted from planning a fully self-driving car to an EV more like Tesla's. The so-called "Apple Car" is now projected to launch no earlier than 2028 -- two years after the company's last reported target date. The car's autonomous features have reportedly been downgraded from a Level 5 system (full automation) to a Level 4 system (full automation in some circumstances) -- and now to a Level 2 one (partial automation). That would mean it offers limited self-driving features like lane centering and braking / accelerating support -- while still requiring the driver's full attention. Tesla's Autopilot is categorized as Level 2. Level 2 isn't an official designation, but it's sometimes used informally to describe a more advanced version of Level 2. What Apple once envisioned as a car without a steering wheel or pedals -- and perhaps having a remote command center ready to take over for a driver -- now looks more like a Tesla-like market entrance.

Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. Traffic jams without a clear cause, such as an on-ramp or an accident, are called phantom traffic jams and are said to make up 50% of all traffic jams. They are the result of an unstable traffic flow caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans' limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow, and compared it with manual control and full automation. We also investigated the effect of two control types on participants' behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.

In the run up to Dreamforce 2021 in September, Salesforce announced new capabilities for Einstein Automate as well as new AI-driven workflows and RPA capabilities for Service Cloud . Prior to Dreamforce 2021, I had a chance to talk with Clara Shih, CEO of Service Cloud at Salesforce, about how the cloud-based software company sees automation and AI transforming, and actually humanizing, customer service. The following is a transcript of our interview, edited for readability. So let's talk automation, AI, RPA and how that relates to the Service Cloud and how that's kind of changing how organizations approach their interactions with their customers. Because I know that automation is a large part of many organization's digital transformation processes.

I will teach you to easily add your own text, photos, colors and sound to customize your video and edit it all on a simple drag-and-drop timeline. Once, you've done that we will go through the process of choose a soundtrack from Biteable's huge, high-quality music library to match the look and feel of your video. Let's delve down further into the use of AI technology. Artificial Intelligence (AI) is now involved in the creation of smart video and has also been able to influence businesses. Users can now film and edit videos through Artificial Intelligence.

Multiple lines of evidence suggest that predictive models may benefit from algorithmic triage. Under algorithmic triage, a predictive model does not predict all instances but instead defers some of them to human experts. However, the interplay between the prediction accuracy of the model and the human experts under algorithmic triage is not well understood. In this work, we start by formally characterizing under which circumstances a predictive model may benefit from algorithmic triage. In doing so, we also demonstrate that models trained for full automation may be suboptimal under triage. Then, given any model and desired level of triage, we show that the optimal triage policy is a deterministic threshold rule in which triage decisions are derived deterministically by thresholding the difference between the model and human errors on a per-instance level. Building upon these results, we introduce a practical gradient-based algorithm that is guaranteed to find a sequence of triage policies and predictive models of increasing performance. Experiments on a wide variety of supervised learning tasks using synthetic and real data from two important applications -- content moderation and scientific discovery -- illustrate our theoretical results and show that the models and triage policies provided by our gradient-based algorithm outperform those provided by several competitive baselines.

The conversation around artificial intelligence and automation seems dominated by either doomsayers who fear robots will supplant all humans in the workforce, or optimists who think there's nothing new under the sun. But MIT Sloan professor Erik Brynjolfsson and his colleagues say that debate needs to take a different tone. New research finds that specific tasks within jobs, rather than entire occupations themselves, will be replaced by automation in the near future, with some jobs more heavily impacted than others. "Our findings suggest that a shift is needed in the debate about the effects of AI: away from the common focus on full automation of entire jobs and pervasive occupational replacement toward the redesign of jobs and reengineering of business practices," the researchers write in an article published in May in the American Economic Association Papers and Proceedings. The work is by Brynjolfsson, professor Tom Mitchell of Carnegie Mellon University's machine learning department, and Daniel Rock, a doctoral candidate and researcher at the MIT Initiative on the Digital Economy.

The current article is an interdisciplinary attempt to decipher automatic program repair processes. The review is done by the manner typical to human science known as diffraction. We attempt to spot a gap in the literature of self-healing and self-repair operations and further investigate the approaches that would enable us to tackle the problems we face. As a conclusion, we suggest a shift in the current approach to automatic program repair operations in order to attain our goals. The emphasis of this review is to achieve full automation. Several obstacles are shortly mentioned in the current essay but the main shortage that is covered is the overfitting obstacle, and this particular problem is investigated in the stream that is related to full automation of the repair process.