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DALL-E Proves the Unbounded Abilities of Artificial Intelligence – Study Breaks

The creative power of the human mind has often been recognized as the greatest force in art. The ability to internalize real-world circumstances and transmit thought into visual form, storytelling or music is a facet of human society that can be traced back to the beginning of recorded history. The sanctity of the human mind within the realm of art has long gone unchallenged, yet modern technology has posed some counterarguments to the assertion that sentience is required to produce creative works. Artificial intelligence, or AI, is a broad category of machine learning technology whereby computer programs are exposed to data and subsequently begin to work independently to complete tasks. One recently announced program has demonstrated abilities that are leaps and bounds beyond the limits of its contemporaries, and has unlocked the yet unforeseen power of AI-generated art.

The new program, known as DALL-E, has demonstrated that the sky is the limit for creative artificial intelligence. DALL-E was developed in 2021 by OpenAI, an artificial intelligence lab that has spent the last seven years programming applications that approximate human ability in various fields. The platform derives its name from two radically different influences: Spanish painter Salvador Dali and the lovable robotic protagonist of Pixars WALL-E. It has garnered a devoted online following for its revolutionary ability to understand complex phrases and produce unique, original computer-generated visuals based upon written sentences.

The platforms user interface is reminiscent of many search engines, with a text bar for users to input phrases that serve as instructions for generating the original images. Within 30 seconds of a user hitting enter, half a dozen rendered images appear onscreen. The content of the images varies slightly from one picture to the next, with some demonstrating a literal interpretation of the searched phrase while others explore implied meanings of the searched words. The truly remarkable ability to interpret the strings of words in several manners demonstrates an inventive level of textual understanding that feels impossibly human for an AI. The platforms website advertises many of its most impressive capabilities, such as: creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images. These descriptions only scratch the surface of what DALL-E is capable of, yet OpenAI has already moved beyond this first program in a quest to code something even closer to sentient life.

DALL-E was quickly followed by DALL-E 2, a similar application that performs nearly the same function but displays crisper images and has a more advanced understanding of English language syntax. Neither application is available for public use, with the latter in beta testing and made available to select online personalities to advertise its features. It is not apparent when or if the platforms will be released for general use, though it seems likely that it would exist behind a paywall should a public version be developed. The lack of general knowledge concerning the complete functionality of the program or its technical foundation has left many to speculate about what code powers the two applications, though OpenAIs website provides a wealth of knowledge about certain components of their inner workings.

Since its inception in the 1940s, digital computer technology has been able to interpret human inputs and produce a desired response, typically in the form of text. When a search engine or website is asked to display an image, such as on Google Images, it does so by retrieving an existing file that it understands to be linked with the search terms via machine learning processes. DALL-E is built upon the framework of Generative Pre-trained Transformer 3 (GPT-3), a language algorithm that learns to predict and generate sequences of text. The platform uses this coding model and expands upon it, housing its own database of reference images in a manner reminiscent of a search engine. It harnesses GPT-3 to recognize the order and significance of words and to scan multiple images that are associated with different words in a search. Once it comprehends the string of input vocabulary using these references, it can then generate an original image by combining the disparate content in the search phrase.

There are countless reasons to praise the minds behind DALL-E for concocting a creative tool that has such an elevated understanding of language and visual art, though there is also cause for concern. The art world was immediately concerned about a marketplace in which artificial intelligence can push living artists out of a job. The frenzied discourse around DALL-E is sensible for those who are concerned about their careers, though this is not the first time visual artists have been threatened by, but ultimately survived, the march of technology. Photography was also once a feared new medium, with the ease of capturing real-life imagery seemingly challenging the job security of portrait artists and impressionist painters. Though the medium could have replaced the demand for painted artworks, the classical forms of the visual arts have survived in the era of cameras because photography constituted a separate sector of the art world and was often used by painters to provide inspiration for their work. OpenAIs stated goal for developing the DALL-E programs is to assist graphic designers by giving them a tool to quickly generate reference images that can be used in several ways for further artistry. The ability to generate reference images in a rapid manner and of a style that the artist may not have considered is an incredible asset for those who learn to use it and will likely contribute more to artists than it will take away.

The impressive technology at play within DALL-E proposes another ethical dilemma. The significant difference between a sentient artist and a robotic curator is the presence of a moral compass within the former. DALL-E can render photorealistic visuals and could hypothetically be asked to depict damaging content without much participation from a user. In preparation for such circumstances, the AI refuses to generate images using some violent or explicit search terms and will also avoid producing visuals containing public figures. These decisions have pre-emptively circumvented some forms of abusing the technology, though crafty users can search precise, uncensored terms to generate imagery that approximates what the program would refuse to depict with censored terminology. It is easy to blame DALL-E for this defect, though the user is still the driving force behind any reprehensible works the application makes. Human artists have also shown tendencies to produce despicable art without the wonders of 21st-centurytechnology, as numerous propaganda artists of past centuries demonstrate. Any method of communication can be channeled for questionable aims, yet it is not sensible to blame the tool for an issue that lies squarely with its user.

Though the platforms name references Dali, it is actually worth examining the difference between the program and the painter to ease the concerns of those who find DALL-E and its successor dangerous. Salvador Dali was an eccentric abstractionist painter who was instrumental in the 20th-century shift away from impressionist painting toward postmodern art. His incredibly stylized work is instantaneously recognizable and the product of his ingenuity; his brush brought into existence contours and compositions that nobody had previously imagined. DALL-E, on the other hand, can only emulate, and its ability to create new styles or forms beyond what exists in its database of visuals is limited. The program cannot follow in Dalis footsteps and take the next quantum leap in artistic thought in the same way aspiring artists of today undoubtedly will. Whether or not it is being used to originate, emulate, or outright copy a style or form, it still requires a creative mind to take the wheel and lead it in a certain direction. DALL-E doesnt need to ring alarm bells for a war against technology, but rather, it reminds us that even when artificial intelligence progresses, we can recognize it as an extension of ourselves.

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Artificial Intelligence in Aviation Market to be Worth $9995.83 Billion by 2029 | Global Market Vision – Digital Journal

The Artificial Intelligence in Aviation market will exhibit a CAGR of 46.3% for the forecast period of 2022-2029 and is likely to reach the USD 9,995.83 million by 2029.

The global Ai in aviation market is expected to witness a significant rise during the forecast period, owing to the rising usage of big data analytics in the aerospace industry. The rapidly increasing investments by the aerospace companies in towards the adoption of the cloud-based technologies and services is boosting the growth of the global AI in aviation market. The airlines industry and the airports are increasingly adopting the latest and novel technologies like artificial intelligence to improve services and smooth operations. The rising operational costs and rising need for improving the profitability is fostering the adoption of AI in the aviation industry. Airways has now become an important medium of transport across the globe and hence the rising focus on the improvement of the customer services is significantly boosting the demand for the AI in aviation industry. There has been a significant rise in the adoption of the AI based chat bots that facilitates the travelers in online ticket booking.

Get a Sample Copy of the artificial intelligence in aviation Market Report 2022 Including TOC, Figures, and Graphs @: https://globalmarketvision.com/sample_request/131336

The adoption of the AI and machine learning technologies are expected to enhance the air traffic control and predictive maintenance activities in the near future. The adoption of AI for observation tasks such as time series analysis, natural language processing, and computer vision. The ongoing developments and rising investments on the research activities are expected to surge the number of applications of AI in the various complex operations of the aviation industry. EHang, a China-based company and Airbus are collectively engaged in developing AI-based navigation technology. EHang uses AI in its autonomous aircrafts and Airbus has completed its first taxi, take-off and landing using the vision-based AI. Therefore, the rising focus on the adoption of the AI for performing different operations in the aviation industry is significantly boosting the growth of the global AI in aviation market.

Key Market Developments

Some of the prominent players in the global artificial intelligence in aviation market include:

Intel, NVIDIA, IBM, Micron, Samsung, Xilinx, Amazon, Microsoft, Airbus, Boeing, General Electric, Thales, Lockheed Martin, Garmin, Nvidia, GE, Pilot AI Labs, Neurala, Northrop Grumman, IRIS Automation, Kittyhawk and others

Segments Covered in the Report

By Offering

By Technology

By Application

Artificial Intelligence in Aviation Market by Region

Table of Content (TOC):

Chapter 1: Introduction and Overview

Chapter 2: Industry Cost Structure and Economic Impact

Chapter 3: Rising Trends and New Technologies with Major key players

Chapter 4:Global Artificial intelligence in aviation Market Analysis, Trends, Growth Factor

Chapter 5: Artificial intelligence in aviation Market Application and Business with Potential Analysis

Chapter 6: Global Artificial intelligence in aviation Market Segment, Type, Application

Chapter 7: Global Artificial intelligence in aviation Market Analysis (by Application, Type, End User)

Chapter 8: Major Key Vendors Analysis of Artificial intelligence in aviation Market

Chapter 9: Development Trend of Analysis

Chapter 10: Conclusion

Conclusion:At the end of Artificial intelligence in aviation Market report, all the findings and estimation are given. It also includes major drivers, and opportunities along with regional analysis. Segment analysis is also providing in terms of type and application both.

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This helps to understand the overall market and to recognize the growth opportunities in the global Artificial intelligence in aviation Market. The report also includes a detailed profile and information of all the major Artificial intelligence in aviation market players currently active in the global Artificial intelligence in aviation Market. The companies covered in the report can be evaluated on the basis of their latest developments, financial and business overview, product portfolio, key trends in the Artificial intelligence in aviation market, long-term and short-term business strategies by the companies in order to stay competitive in the Artificial intelligence in aviation market.

If you have any special requirements, please let us know and we will offer you the report at a customized price.

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Northeastern Launches AI Ethics Board to Chart a Responsible Future in AI – Northeastern University

The world of artificial intelligence is expanding, and a group of AI experts at Northeastern wants to make sure it does so responsibly.

Self-driving cars are hitting the road and others cars. Meanwhile a facial recognition program led to the false arrest of a Black man in Detroit. Although AI has the potential to alter the way we interact with the world, it is a tool made by people and brings with it their biases and limited perspectives. But Cansu Canca, founder and director of the AI Ethics Lab, believes people are also the solution to many of the ethical barriers facing AI technology.

With the AI Ethics Advisory Board, Canca, co-chair of the board and AI ethics lead of the Institute for Experiential AI at Northeastern, and a group of more than 40 experts hope to chart a responsible future for AI.

There are a lot of ethical questions that arise in developing and using AI systems, but also there are a lot of questions regarding how to answer those questions in a structured, organized manner, Canca said. Answering both of those questions requires experts, especially ethics experts and AI experts but also subject matter experts.

The board is one of the first of its kind, and although it is housed in Northeastern, it is made up of multidisciplinary experts from inside and outside the university, with expertise ranging from philosophy to user interface design.

The AI Ethics Advisory Board is meant to figure out: What is the right thing to do in developing or deploying AI systems? Canca said. This is the ethics question. But to answer it we need more than just AI and ethics knowledge.

The boards multidisciplinary approach also involves industry experts like Tamiko Eto, the research compliance, technology risk, privacy and IRB manager for healthcare provider Kaiser Permanente. Eto stressed that whether AI is utilized in healthcare or defense, the impacts need to be analyzed extensively.

The use of AI-enabled tools in healthcare and beyond requires a deep understanding of the potential consequences, Eto said. Any implementation must be evaluated in the context of bias, privacy, fairness, diversity and a variety of other factors, with input from multiple groups with context-specific expertise.

The AI Ethics Advisory Board will function as an external, objective consultant for companies that are grappling with AI ethical questions. When a company contacts the board with a request, it will determine the subject matter experts best suited to tackling that question. Those experts will form a smaller subcommittee that will be tasked with considering the question from all relevant perspectives and then resolving the case.

But the aim is not only to address the concerns of specific companies. Canca and the board members hope to answer broader questions about how AI can be implemented ethically in real-world settings.

The mindset is for truly solving questions, not just managing the question for the client but truly solving the question, and contributing to the progress of the practice Canca said. This is not a review board or a compliance board. Our approach is one, Lets figure the ethical issues and create better technologies. Lets enhance the technology with all these multidisciplinary capabilities that we have, that we can bring on board.'

Its an approach that Ricardo Baeza-Yates, co-chair of the board, director of research for the Institute for Experiential AI and professor of practice in Khoury College of Computer Science, said is necessary in order to tackle the privacy and discrimination issues that are most commonly seen in AI use. Baeza-Yates said the latter is especially concerning, since its not always a simple technical fix.

This sometimes comes from the data but also sometimes comes from the system, Baeza-Yates said. What you are trying to optimize can sometimes be the problem.

Baeza-Yates points to facial recognition programs and e-commerce AI that have profiled people of color and reinforced pre-existing biases and forms of discrimination. But the most well-known ethical problem in current AI use is the self-driving car, which Baeza-Yates likened to the trolley problem, a famous philosophical thought experiment.

We know that self-driving cars will kill less people [than human drivers], for sure, Baeza-Yates said. The problem is that we are saving a lot of people, but also we will kill some people who before were not in danger. Mostly, this will be vulnerable people, women, children, old people that, for example, didnt move so fast like the model expected or the kid moved too fast for the model to expect.

Conversations around the ethical implications of technology like the self-driving car are only starting in companies. For now, AI ethics seems very mysterious to a lot of companies, Canca said, which can lead to confusion and disinterest. With the board, Canca hopes to spark a more meaningful, engaged conversation and put an ethics-based approach at the core of how companies approach the technology moving forward.

We can help them understand the issues they are facing and figure out the problems that they need to solve through a proper knowledge exchange, Canca said. Through advising, We can help them ask the right questions and help them find novel and innovative solutions or mitigations. Companies are getting more and more interested in establishing a responsible AI practice, but its important that they do this efficiently and in a way that fits their organizational structure.

For media inquiries, please contact Shannon Nargi at s.nargi@northeastern.edu or 617-373-5718.

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Multiverse Collaborating with Bosch to Optimize Quality, Efficiency, and Performance in an Automotive Electronic Components Manufacturing Plant -…

Multiverse Collaborating with Bosch to Optimize Quality, Efficiency, and Performance in an Automotive Electronic Components Manufacturing Plant

Multiverse and Bosch will be working to create a quantum computing model of the machinery and process flow in at one of Boschs manufacturing plants in a process known as digital twin. This is a technique where a model of the activities in the facility will be created inside the computer and then enable various simulations and optimizations to be performed which can predict how the plant will perform under different scenarios. The companies will be using both customized quantum and quantum inspired algorithms developed by Multiverse in order to model an automotive electronic components plants located in Madrid, Spain. The companies hope to have first results of this pilot implementation by the end of the year with a goal of finding ways to enhance quality control, improve overall efficiencies, minimize waste, and lower energy usage. Bosch has a total of 240 manufacturing plants that include over 120,000 machines and 250,000 devices which are connected together to provide them with digital control and sensing to optimize performance. So a successful implementation of this digital twin concept could be extended to many more factories and provide Bosch with a significant productivity advantage in the future. A news release from Multiverse about this collaboration can be accessed on their website here.

July 30, 2022

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Historically accurate Bitcoin metric exits buy zone in ‘unprecedented’ 2022 bear market – Cointelegraph

Bitcoin (BTC) is enjoying what some are calling a "bear market rally" and has gained 20% in July, but price action is still confusing analysts.

As the July monthly close approaches, the Puell Multiple has left its bottom zone, leading to hopes that the worst of the losses may be in the past.

The Puell Multiple one of the best-known on-chain Bitcoin metrics. It measures the value of mined bitcoins on a given day compared to the value of those mined in the past 365 days.

The resulting multiple is used to determine whether a day's mined coins is particularly high or low relative to the year's average. From that, miner profitability can be inferred, along with more general conclusions about how overbought or oversold the market is.

After hitting levels which traditionally accompany macro price bottoms, the Puell Multiple is now aiming higher something traditionally seen at the start of macro price uptrends.

"Based on historical data, the breakout from this zone was accompanied by gaining bullish momentum in the price chart," Grizzly, a contributor at on-chain analytics platform CryptoQuant, wrote in one of the firm's "Quicktake" market updates on July 25.

The Multiple is not the only signal flashing green in current conditions. As Cointelegraph reported, accumulation trends among hodlers are also suggesting that the macro bottom is already in.

After its surprise relief bounce in the second half of this month, Bitcoin is now near its highest levels in six weeks and far from a new macro low.

Related:Bitcoin futures data shows 'improving' mood' despite -31% GBTC premium

As sentiment exits the "fear" zone, market watchers are pointing to unique phenomena which continue to make the 2022 bear market extremely difficult to predict with any certainty.

In another of its recent "Quicktake" research pieces, CryptoQuant noted that even price trendlines are not acting as normal this time around.

In particular, BTC/USD has crisscrossed its realized price level several times in recent weeks, something which did not occur in prior bear markets.

Realized price is the average at which the BTC supply last moved, and currently sits just below $22,000.

"The Realized Price has signaled the market bottoms in previous cycles," CryptoQuant explained.

Those conditions, as Cointelegraph reported, have come in the form of forty-year highs in inflation in the United States, rampant rate hikes by the Federal Reserve and most recently signals that the U.S. economy has entered a recession.

In addition to realized price, meanwhile, Bitcoin has formed an unusual relationship to its 200-week moving average (MA) this bear market.

While normally retaining it as support with brief dips below, BTC/USD managed to flip the 200-week MA to resistance for the first time in 2022. It currently sits at around $22,800, data from Cointelegraph Markets Pro and TradingView shows.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph.com. Every investment and trading move involves risk, you should conduct your own research when making a decision.

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New hardware offers faster computation for artificial intelligence, with much less energy – MIT News

As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial neurons and synapses that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages, says senior author Jess A. del Alamo, the Donner Professor in MITs Department of Electrical Engineering and Computer Science (EECS). This work has really put these devices at a point where they now look really promising for future applications.

The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime, explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water, says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.

These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft, adds lead author and MIT postdoc Murat Onen.

Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

Accelerating deep learning

Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor. Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesnt need more time to complete new operations because all computation occurs simultaneously.

The key element of MITs new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

Surprising speed

PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesnt damage anything, thanks to the small size and low mass of protons. It is almost like teleporting, he says.

The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field, adds Li.

Because the protons dont damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence, adds Yildiz.

The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting, del Alamo says.

Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance, says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. It lays the foundation for a new class of memory devices for powering deep learning algorithms.

This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates, says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.

This research is funded, in part, by the MIT-IBM Watson AI Lab.

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Weekly AiThority Roundup: Biggest Machine Learning, AI, Robotic And Automation Updates July Week 05 – AiThority

This is your AI Weekly Roundup today. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities inartificial intelligence (AI),Machine Learning, Robotic Process Automation, Fintech, and human-system interactions. We cover the role of AI Daily Roundup and its application in various industries and daily lives.

UK and Japan-based crypto startup Sumo Signals Ltd. announced that it has raised US$5.5 Million in its recent round of funding led by Hong Kong based prominent investor OnDeck Venture. The successful funding round is a clear indication of the companys strong growth prospects powered by its pioneering AI-based technology.

Thentia, a venture capital-backed and global industry-leading government software-as-a-service (SaaS) provider, announced it has joined the Google Cloud Partner Advantageprogram. Thentia Cloud can be procured directly through Google Clouds Independent Software Vendor (ISV) Marketplace.

Merkle, dentsus leading technology-enabled, data-driven customer experience management (CXM) company, announces the expansion of its EMEA Salesforce practice with the appointment of three new strategic hires.

Chargebee, the leading subscription management platform, announced its Summer 2022 Product Release. The slate of new products and features is focused on enabling high-performing subscription businesses to monetize their existing customers and fend off the growing threats of a tumultuous economy. These new products help businesses build their cash reserves and maintain their customer base at a time when many businesses and their customers are struggling with the realities of inflation and drying up of venture capital, the lingering effects of COVID-19 and a decimated global supply chain.

Mvix, a leading provider of enterprise-grade digital signage solutions, speeds up its development and integration of Enterprise Business Intelligence Tools on its cloud-based software Mvix CMS, empowering data sharing for efficiency and scalability.Microsoft Power BI, Tableau, and Klipfolio, top business intelligence (BI) powerhouses with a combined market share of 80 percent, are three of numerous tools slated to offer real-time data and metrics streamlining workflow and productivity for clients.

[To share your insights with us, please write tosghosh@martechseries.com]

AiT Analyst is a trained researcher with many years of experience in finding news and reviewing them. The Analysts provide extensive coverage to major companies and startups in key technology sectors and geographies from the emerging tech landscape.

To connect, please write to AiT Analyst at sghosh@martechseries.com.

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Machine Learning Breakthroughs Have Sparked the AI Revolution – InvestorPlace

Source: shutterstock.com/Peshkova

[Editors note: Machine Learning Breakthroughs Have Sparked the AI Revolution was previously published in February 2022. It has since been updated to include the most relevant information available.]

Its October 1950. Alan Turing, the genius who cracked the Enigma code and helped end World War II, has just introduced a novel concept.

Its called the Turing Test, and its aimed at answering the fundamental question:Can machines think?

The world laughs. Machines think for themselves? Not possible.

However, the Turing Test sets in motion decades of research into the emerging field of Artificial Intelligence (AI).

This research is conducted in the worlds most prestigious labs by some of the worlds smartest people. Collectively, theyre working to create a new class of computers and machines that can, indeed, think for themselves.

Fast forward 70 years.

AI is everywhere.

Its in yourphones. What do you think powers Siri? How does a phone recognize your face?

Its in yourapplications. How does Google Maps know directions and optimal routes? How does it make real-time changes based on traffic? And how does Spotify create hyper-personalized playlists or Netflix recommend movies?

AI is on yourcomputers. How does Google suggest personalized search items for you? How do websites use chatbots that seem like real humans?

As it turns out, the world shouldnt have laughed back in 1950.

The great Alan Turing ended up creating a robust foundation upon which seven decades of groundbreaking research has compounded. Ultimately, it resulted in self-thinking computers and machines not just being a thing but being everything.

Make no mistake. This decades-in-the-making AI Revolution is just getting started.

Thats because AI is mostly built on what industry insiders call machine learning (ML) and natural language processing (NLP) models. And these models are informed with data.

Accordingly, the more data they have, the better the models get and the more capable the AI becomes.

When I say identity, what do you think of?

If youre like me, you immediately start to think of what makes you, well, you your height, eye color; what job you have, what car you drive, what shows you like to binge-watch.

In other words, the amount of data associated with each individual identity is both endless and unique.

Those attributes make identity data extremely valuable.

Up until recently, though, enterprises had no idea how to extract value from this robust dataset. Thats all changing right now.

Breakthroughs in artificial intelligence and machine-learning technology are enabling companies to turn identity data into more personalized, secure and streamlined user experiences for their customers, employees and partners.

The volume and granularity of data is exploding right now. Thats mostly because every object in the world is becoming a data-producing device.

Dumb phones have become smartphones and have started producing a ton of usage data.

Dumb cars have become smart cars and have started producing lots of in-car driving data.

And dumb apps have become smart apps and have started producing heaps of consumer preference data.

Dumb watches have become smartwatches and have started producing bunches of fitness and activity data.

As weve sprinted into the Smart World, the amount of data that AI algorithms have access to has exploded. And its making them more capable than ever.

Why else has AI started popping up everywhere in recent years? Its because90% of the worlds data was generated in the last two years alone.

More data, better ML and NLP models, smarter AI.

Its that simple.

And guess what? The world isnt going to take any steps back in terms of this smart pivot. No. We love our smartphones, smart cars and smartwatches far too much.

Instead, society will accelerate in this transition. Globally, the world produces about 2.5 exabytes of data per day. By 2025, that number is expected to rise to 463 exabytes.

Lets go back to our process.

More data, better ML and NLP models, smarter AI.

Thus, as the volume of data produced daily soars more than 185X over the next five years, ML and NLP models will get 185X better (more or less). And AI machines will get 185X smarter (more or less).

Folks, the AI Revolution is just getting started.

Most things a human does, a machine will soon be able to do better, faster and cheaper.

Given the advancements AI has made over the past few years with the help of data and the exponential amount of it yet to come Im inclined to believe this.

Eventually, and inevitably, the world will be run by hyperefficient and hyperintelligent AI.

Im not alone in thinking this. Gartner predicts that 69% of routine office work will be fully automated by 2024. And the World Economic Forum has said that robots will handle 52% of current work tasks by 2025.

The AI Revolution is coming and its going to be the biggest youve seen in your lifetime.

You need to be invested in this emerging tech megatrend that promises to change the world forever.

Of course, the question remains: What AI stocks should you start buying right now?

You could play it safe and go with the blue-chip tech giants. All are making inroads with AI and are low-risk, low-reward plays on the AI Revolution. Im talking Microsoft (MSFT), Alphabet (GOOG), Amazon (AMZN), Adobe (ADBE) and Apple (AAPL).

However, thats not how we do things. We dont like safe we like best.

At present, enterprise AI software is being used very effectively by Big Tech. And its being used ineffectively or not at all by everyone else.

Todays AI companies are changing that. And the best way to play the AI Revolution is by buying the stocks that are changing the paradigm in which they exist.

We have identified several AI stocks to buy for enormous long-term returns.

Again, these AI stocks arent the safe way to play the AI Revolution. Theyre the best way to do it.

One company is pioneering a novel model-driven architecture. Indeed, it represents a promising paradigm shift in the AI application development process. Ultimately, it will democratize the power of AI so that its no longer a weapon used by Big Tech to crush its opponents.

Essentially, this company has pre-built multiple, highly scalable AI models in its ecosystem. And it allows customers to build their own AI models by simply editing and stacking them atop one another.

Think of building an AI application as a puzzle. You must have the right pieces and directions. In other words, to effectively utilize the power of enterprise AI, customers just piece it together in a way that works best for them.

Equally important, the building of these puzzles is not rocket science. The company does all the hard work of making the actual models. Customers simply have to pick which ones they want to use and decide how they want to use them.

In some instances, coding and data science are still required but not much. Todays top AI companies make it easy to develop, scale, and apply insights without writing any code.

Its a genius breakthrough to address the widening AI gap between Big Tech and everyone else.

Eventually, every company from every industry and of every size will leverage the power of AI to enhance their business, increase revenues and reduce costs.

Of course, this reality bodes well for AI stocks in the long term.

You just have to know which ones are worth buying and which are not

On the date of publication, Luke Lango did not have (either directly or indirectly) any positions in the securities mentioned in this article.

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Machine Learning Breakthroughs Have Sparked the AI Revolution - InvestorPlace

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21 PPC lessons learned in the age of machine learning automation – Search Engine Land

What youre about to read is not actually from me. Its a compilation of PPC-specific lessons learned by those who actually do the work every day in this age of machine learning automation.

Before diving in, a few notes:

Its simple, a machine cannot optimize toward a goal if there isnt enough data to find patterns.

For example, Google Ads may recommend Maximize Conversions as a bid strategy, BUT the budget is small (like sub $2,000/mo) and the clicks are expensive.

In a case like this, you have to give it a Smart Bid strategy goal capable of collecting data to optimize towards.

So a better option might be to consider Maximize Clicks or Search Impression Share. In small volume accounts, that can make more sense.

The key part of machine learningis the second word: learning.

For a machine to learn what works, it must also learn what doesnt work.

That part can be agonizing.

When launching an initial Responsive Search Ad (RSA), expect the results to underwhelm you. The system needs data to learn the patterns of what works and doesnt.

Its important for you to set these expectations for yourself and your stakeholders. A real-life client example saw the following results:

As you can see, month two looked far better. Have the proper expectations set!

Many of us whove been in the industry a while werent taught to manage ad campaigns the way they need to be run now. In fact, it was a completely different mindset.

For example, I was taught to:

Any type of automation relies on proper inputs. Sometimes what would seem to be a simple change could do significant damage to a campaign.

Some of those changes include:

Those are just a few examples, but they all happened and they all messed with a live campaign.

Just remember, all bets are off when any site change happens without your knowledge!

The best advice to follow regarding Recommendations are the following:

Officially defined as the impressions youve received on the Search Network divided by the estimated number of impressions you were eligible to receive, Search Impression Share is basically a gauge to inform you what percentage of the demand you are showing to compete for.

This isnt to imply Search Impression Share is the single most important metric. However, you might implement a smart bidding rule with Performance Max or Maximize Conversions and doing so may negatively impact other metrics (like Search Impression Share).

That alone isnt wrong. But make sure youre both aware and OK with that.

Sometimes things change. Its your job to stay on top of it. For smart bidding, Target CPA no longer exists for new campaigns. Its now merged with Maximize Conversions.

Smart Shopping and Local Campaigns are being automatically updated to Performance Max between July and September 2022. If youre running these campaigns, the best thing you can do is to do the update manually yourself (one click implementation via the recommendations tab in your account).

Why should you do this?

This doesnt need to be complicated. Just use your favorite tool like Evernote, OneNote, Google Docs/Sheets, etc. Include the following for each campaign:

There are three critical reasons why this is a good idea:

Imagine youre setting up a campaign and loading snippets of an ad. Youve got:

Given the above conditions, do you think it would be at all useful to know which combinations performed best? Would it help you to know if a consistent trend or theme emerges? Wouldnt having that knowledge help you come up with even more effective snippets of an ad to test going forward?

Well, too bad because thats not what you get at the moment.

If you run a large volume account with a lot of campaigns, then anytime you can provide your inputs in a spreadsheet for a bulk upload you should do it. Just make sure you do a quality check of any bulk actions taken.

Few things can drag morale down like a steady stream of mundane tasks. Automate whatever you can. That can include:

To an outsider, managing an enterprise level PPC campaign would seem like having one big pile of money to work with for some high-volume campaigns. Thats a nice vision, but the reality is often quite different.

For those who manage those campaigns, it can feel more like 30 SMB accounts. You have different regions with several unique business units (each having separate P&Ls).

The budgets are set and you cannot go over it. Period.

You also need to ensure campaigns run the whole month so you cant run out of budget on the 15th.

Below is an example of a custom budget tracking report built within Google Data Studio that shows the PPC manager how the budget is tracking in the current month:

Devote 10% of your management efforts (not necessarily budget) to trying something new.

Try a beta (if you have access to it), a new smart bidding strategy, new creative snippets, new landing page, call to action, etc.

If you are required (for example by legal, compliance, branding, executives) to always display a specific message in the first headline, you can place a pin that will only insert your chosen copy in that spot while the remainder of the ad will function as a typical RSA.

Obviously if you pin everything, then the ad is no longer responsive. However, it has its place so when you gotta pin, you gotta pin!

Its simple: The ad platform will perform the heavy lifting to test for the best possible ad snippet combinations submitted by you to achieve an objective defined by you.

The platform can either perform that heavy lifting to find the best combination of well-crafted ad snippets or garbage ones.

Bottom line, an RSA doesnt negate the need for skilled ad copywriting.

If youve managed campaigns for an organization in a highly regulated industry (healthcare, finance, insurance, education, etc.) you know all about the legal/compliance review and frustrations that can mount.

Remember, you have your objectives (produce campaigns that perform) and they have theirs (to keep the organization out of trouble).

When it comes to RSA campaigns, do yourself a favor and educate the legal, compliance, and branding teams on:

To use an automotive analogy, think of automation capabilities more like park assist than full self driving.

For example, you set up a campaign to Bid to Position 2 and then just let it run without giving it a second thought. In the meantime, a new competitor enters the market and showing up in position 2 starts costing you a lot more. Now youre running into budget limitations.

Use automation to do the heavy lifting and automate the mundane tasks (Lesson #11), but ignore a campaign once its set up.

This is related to lesson #5 and cannot be overstated.

For example, you may see a recommendation to reach additional customers at a similar cost per conversion in a remarketing campaign. Take a close look at the audiences being recommended as you can quickly see a lot of inflated metrics especially in remarketing.

You have the knowledge of the business far better than any algorithm possibly could. Use that knowledge to guide the machine and ensure it stays pointed in the right direction.

By some accounts, Im mostly referring to low-budget campaigns.

Machine learning needs data and so many smaller accounts dont have enough activity to generate it.

For those accounts, just keep it as manual as you can.

Speak with one of your industry peers, and youll quickly find someone who understands your daily challenges and may have found ways to mitigate them.

Attend conferences and network with people attending the PPC track. Sign up for PPC webinars where tactical campaign management is discussed.

Participate (or just lurk) in social media discussions and groups specific to PPC management.

Many of the mundane tasks (Lesson #11) can be automated now, thus eliminating the need for a person to spend hours on end performing them. Thats a good thing no one really enjoyed doing most of those things anyway.

As more tasks continue toward the path of automation, marketers only skilled at the mundane work will become less needed.

On the flipside, this presents a prime opportunity for strategic marketers to become more valuable. Think about it the machine doing the heavy lifting needs guidance, direction and course corrective action when necessary.

That requires the marketer to:

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

New on Search Engine Land

About The Author

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U.S. Army Research Lab Expands Artificial Intelligence and Machine Learning Contract with Palantir for $99.9M – Yahoo Finance

DENVER, July 28, 2022--(BUSINESS WIRE)--Palantir Technologies Inc. (NYSE: PLTR) today announced that it will expand its work with the U.S. Army Research Laboratory to implement data and artificial intelligence (AI)/machine learning (ML) capabilities for users across the combatant commands (COCOMs). The contract totals $99.9 million over two years.

Palantir first partnered with the Army Research Lab to provide those on the frontlines with state-of-the-art operational data and AI capabilities in 2018. Palantirs platform has supported the integration, management, and deployment of relevant data and AI model training to all of the Armed Services, COCOMs, and special operators. This extension grows Palantirs operational RDT&E work to more users globally.

"Maintaining a leading edge through technology is foundational to our mission and partnership with the Army Research Laboratory," said Akash Jain, President of Palantir USG. "Our nations armed forces require best-in-class software to fulfill their missions today while rapidly iterating on the capabilities they will need for tomorrows fight. We are honored to support this critical work by teaming up to deliver the most advanced operational AI capabilities available with dozens of commercial and public sector partners."

By working with the U.S. Army Research Lab, integrating with partner vendors, and iterating with users on the front lines, Palantirs software platforms will continue to quickly implement advanced AI capabilities against some of DODs most pressing problem sets. "Were looking forward to fielding our newest ML, Edge, and Space technologies alongside our U.S. military partners," said Shannon Clark, Senior Vice President of Innovation, Federal. "These technologies will enable operators in the field to leverage AI insights to make decisions across many fused domains. From outer space to the sea floor, and everything in between."

About Palantir Technologies Inc.

Foundational software of tomorrow. Delivered today. Additional information is available at https://www.palantir.com.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantirs expectations regarding the amount and the terms of the contract and the expected benefits of our software platforms. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. Forward-looking statements are based on information available at the time those statements are made and were based on current expectations as well as the beliefs and assumptions of management as of that time with respect to future events. These statements are subject to risks and uncertainties, many of which involve factors or circumstances that are beyond our control. These risks and uncertainties include our ability to meet the unique needs of our customer; the failure of our platforms to satisfy our customer or perform as desired; the frequency or severity of any software and implementation errors; our platforms reliability; and our customers ability to modify or terminate the contract. Additional information regarding these and other risks and uncertainties is included in the filings we make with the Securities and Exchange Commission from time to time. Except as required by law, we do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments, or otherwise.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220728005319/en/

Contacts

Media Contact Lisa Gordonmedia@palantir.com

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