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How artificial intelligence is helping scientists find a coronavirus treatment – Brandeis University

Photo/Getty Images

An illustration of COVID-19

By Julian Cardillo '14April 27, 2020

More than 50,000 academic articles have been written about COVID-19 since the virus appeared in November.

The volume of new information isnt necessarily a good thing.

Not all of the recent coronavirus literature has been peer reviewed, while the sheer number of articles makes it challenging for accurate and promising research to stand out or be further studied.

Computer science and linguistics professor James Pustejovsky is leading a Brandeis team in creating an artificial intelligence platform called Semantic Visualization of Scientific Data or SemViz that can sort through the growing mass of published work on coronavirus and help biologists who study the disease gain insights and notice patterns and trends across research that could lead to a treatment or cure.

Pustejovsky, an expert in theoretical and computational modeling and language, is partnering with colleagues at Tufts University, Harvard University, the University of Illinois, and Vassar College. He discussed his work with BrandeisNOW.

Can you provide a birds-eye view of the way youve applied your background as a computational linguist to current coronavirus research?

Im a researcher who focuses on language and extracting information from large amounts of text, like the COVID-19 dataset, which now includes more than 50,000 academic articles. Biologists on the front lines of coronavirus are trying to find connections between genes, proteins and drugs, and how they interact with the virus in the cells of the human body.

SemViz combs through the existing papers and manuscripts and enables scientists to make connections and generalizations that are not obvious from reading one paper at a time.

So how might a biologist studying coronavirus actually use SemViz?

This tool gives a rapid way for biologists studying coronavirus to see a global overview of inhibitors, regulators, and activators of genes and proteins involved in the disease.

For example, what are the drugs and proteins regulating the receptor for the COVID-19 virus? This could help discover therapies that decrease the expression of the receptor for the virus in patients lungs. This is important because millions of people currently take blood pressure medicines that can alter this receptor and possibly increase their risk of contracting the disease.

SemViz creates a visualization landscape that helps biologists make both global and specific connections between human genes, drugs, proteins and viruses. The overall program Im working on contains three components: two semantic visualization outputs based on the entire coronavirus research dataset, as well as a natural language-based question-answering application.

Whats the language application grid and how does it work?

It is essentially a computer-based reading machine that interprets tens of thousands of research articles on coronavirus and presents the results of this process to biologists in a form that is visually accessible and easily analyzed and interpreted.

It is more informative than a search engine, because it utilizes a host of language understanding tools and AI that can be applied to different domains (economics, news, science, literature) and text types (tweets, articles, books, email).

What are the implications of SemViz?

I think its hard to overstate the challenge brought about by information overload, particularly now with the coronavirus literature.

Biologists are interested in the mechanisms and functions of specific chemicals and proteins. SemViz can be the roadmap that scientists use to sort through large amounts of research to find these kinds of functions and relationships.

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Artificial Intelligence in the Indian Manufacturing Industry, 2020: Trends, Insights, Use Cases, Case Studies – ResearchAndMarkets.com – Business Wire

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Indian Manufacturing Industry 2020" report has been added to ResearchAndMarkets.com's offering.

Manufacturing companies in India have been digitizing their plants with advanced process controls, analytics, and AI-based decision support solutions.

Several Indian manufacturing companies have been investing in artificial intelligence AI-based factory automation solutions to improve product quality and design, reduce labor costs, minimize manufacturing cycles, and monitor real-time condition of machines.

To ensure efficiency and productivity, companies such as Blue Star Ltd., TVS Motor Company Ltd., JK Tyre & Industries Ltd., and Asian Paints Ltd. have deployed AI-based solutions and analytics platforms across their manufacturing units in India.

Market Trends

The automated manufacturing machines use AI solutions to identify faults in the manufacturing process and notify the production team to eliminate product quality issues. Manufacturing companies are also installing AI-enabled predictive maintenance systems that are capable of self-monitoring and reporting malfunctions in real time.

IBM has developed cognitive assistants using its cognitive computing platform Watson, for manufacturers to reduce errors and improve product quality. Similarly, Qualitas Technologies has developed the Eagle Eye Inspection System, which uses an AI-based vision controller to check the quality of products.

Indian manufacturers are deploying collaborative robots powered by analytics and AI. These robots are capable of handling additional cognitive tasks and making independent decisions based on real-time data. Blue Star Ltd. is using AI-enabled collaborative robots (offered by Universal Robots A/S) to optimize the task of copper tube expansion and minimize stress risks associated with it.

Market Insights

Over the past few years, venture capital firms and global companies like GVFL Ltd., Boschand Hitachi High-Tech Solutions Corporationhave invested in Indian AI start-ups serving the manufacturing industry. These investment activities have supported the development of AI-based solutions, as a result, propelling the growth of AI in the Indian manufacturing industry.

Indian manufacturers are continuously harnessing the power of Industrial Internet of Things (IIoT) by incorporating intelligent controls, sensors and smart switches in their production units. Use of IIoT, together with AI and data analytics, is expected to benefit the manufacturing industry by providing real-time insights for faster decision making.

Key Topics Covered

Chapter 1: Executive summary

Chapter 2: Socio-economic indicators

Chapter 3: Introduction

3.1. Market definition and structure

3.2. Major global players using AI-based manufacturing process

Chapter 4: India artificial intelligence market overview

4.1. India artificial intelligence market overview

4.1.1. India artificial intelligence market size and growth forecast

4.1.2. Funding in the Indian AI start-ups

Chapter 5: India manufacturing industry overview

5.1. India manufacturing industry overview

5.1.1. India - gross value added (GVA) of manufacturing

Chapter 6: Artificial intelligence in the Indian manufacturing industry

6.1. Impact of artificial intelligence in the Indian manufacturing industry

Chapter 7: Use cases of AI in the manufacturing industry

7.1. Use cases of AI in the manufacturing industry

Chapter 8: Case studies

8.1. Case study - Blue Star Ltd.

8.2. Case study - TVS Motor Company Ltd.

8.3. Case study - JK Tyre & Industries Ltd.

8.4. Case study - Bajaj Auto Ltd.

8.5. Case study - Asian Paints Ltd.

Chapter 9: Market influencers

9.1. Market drivers

9.2. Market challenges

Chapter 10: Competitive landscape

10.1. Abee Research Labs Pvt. Ltd.

10.1.1. Company information

10.1.2. Business description

10.1.3. Products/services

10.1.4. Key people

10.2. EroNkan Technologies Pvt. Ltd.

10.3. Flutura Business Solutions Pvt. Ltd.

10.4. LivNSense Technologies Pvt. Ltd.

10.5. Universal Robots (India) Pvt. Ltd.

10.6. Altizon Systems Pvt. Ltd.

10.7. IBM India Pvt. Ltd.

For more information about this report visit https://www.researchandmarkets.com/r/rityut

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Reducing the carbon footprint of artificial intelligence – MIT News

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues.

Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. Thats equivalent to nearly five times the lifetime emissions of the average U.S. car, including its manufacturing.

This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources.

MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved in some cases, down to low triple digits.

The researchers system, which they call a once-for-all network, trains one large neural network comprising many pretrained subnetworks of different sizes that can be tailored to diverse hardware platforms without retraining. This dramatically reduces the energy usually required to train each specialized neural network for new platforms which can include billions of internet of things (IoT) devices. Using the system to train a computer-vision model, they estimated that the process required roughly 1/1,300 the carbon emissions compared to todays state-of-the-art neural architecture search approaches, while reducing the inference time by 1.5-2.6 times.

The aim is smaller, greener neural networks, says Song Han, an assistant professor in the Department of Electrical Engineering and Computer Science. Searching efficient neural network architectures has until now had a huge carbon footprint. But we reduced that footprint by orders of magnitude with these new methods.

The work was carried out on Satori, an efficient computing cluster donated to MIT by IBM that is capable of performing 2 quadrillion calculations per second. The paper is being presented next week at the International Conference on Learning Representations. Joining Han on the paper are four undergraduate and graduate students from EECS, MIT-IBM Watson AI Lab, and Shanghai Jiao Tong University.

Creating a once-for-all network

The researchers built the system on a recent AI advance called AutoML (for automatic machine learning), which eliminates manual network design. Neural networks automatically search massive design spaces for network architectures tailored, for instance, to specific hardware platforms. But theres still a training efficiency issue: Each model has to be selected then trained from scratch for its platform architecture.

How do we train all those networks efficiently for such a broad spectrum of devices from a $10 IoT device to a $600 smartphone? Given the diversity of IoT devices, the computation cost of neural architecture search will explode, Han says.

The researchers invented an AutoML system that trains only a single, large once-for-all (OFA) network that serves as a mother network, nesting an extremely high number of subnetworks that are sparsely activated from the mother network. OFA shares all its learned weights with all subnetworks meaning they come essentially pretrained. Thus, each subnetwork can operate independently at inference time without retraining.

The team trained an OFA convolutional neural network (CNN) commonly used for image-processing tasks with versatile architectural configurations, including different numbers of layers and neurons, diverse filter sizes, and diverse input image resolutions. Given a specific platform, the system uses the OFA as the search space to find the best subnetwork based on the accuracy and latency tradeoffs that correlate to the platforms power and speed limits. For an IoT device, for instance, the system will find a smaller subnetwork. For smartphones, it will select larger subnetworks, but with different structures depending on individual battery lifetimes and computation resources. OFA decouples model training and architecture search, and spreads the one-time training cost across many inference hardware platforms and resource constraints.

This relies on a progressive shrinking algorithm that efficiently trains the OFA network to support all of the subnetworks simultaneously. It starts with training the full network with the maximum size, then progressively shrinks the sizes of the network to include smaller subnetworks. Smaller subnetworks are trained with the help of large subnetworks to grow together. In the end, all of the subnetworks with different sizes are supported, allowing fast specialization based on the platforms power and speed limits. It supports many hardware devices with zero training cost when adding a new device.In total, one OFA, the researchers found, can comprise more than 10 quintillion thats a 1 followed by 19 zeroes architectural settings, covering probably all platforms ever needed. But training the OFA and searching it ends up being far more efficient than spending hours training each neural network per platform. Moreover, OFA does not compromise accuracy or inference efficiency. Instead, it provides state-of-the-art ImageNet accuracy on mobile devices. And, compared with state-of-the-art industry-leading CNN models , the researchers say OFA provides 1.5-2.6 times speedup, with superior accuracy. Thats a breakthrough technology, Han says. If we want to run powerful AI on consumer devices, we have to figure out how to shrink AI down to size.

The model is really compact. I am very excited to see OFA can keep pushing the boundary of efficient deep learning on edge devices, says Chuang Gan, a researcher at the MIT-IBM Watson AI Lab and co-author of the paper.

If rapid progress in AI is to continue, we need to reduce its environmental impact, says John Cohn, an IBM fellow and member of the MIT-IBM Watson AI Lab. The upside of developing methods to make AI models smaller and more efficient is that the models may also perform better.

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April 2020: The Increasing Importance of Trade Secret Protection for Artificial Intelligence – JD Supra

Artificial intelligence (AI) has quickly become one of the pillars of the modern economy. According to one widely cited study from 2017, AI could contribute up to $15.7 trillion dollars to the global economy by 2030. See PwCs Global Artificial Intelligence Study: Exploiting the AI Revolution at 3, https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html. That prediction is already coming to fruition. According to a White House report on AI from February 2020, AI is already having a substantial economic impact, not only for companies whose core business is AI, but also for nearly all other companies as they discover the need to adopt AI technologies to stay globally competitive. American Artificial Intelligence Initiative: Year One Annual Report (Feb. 2020) at 1, https://www.whitehouse.gov/wp-content/uploads/2020/02/American-AI-Initiative-One-Year-Annual-Report.pdf. The recognition of the importance of AI is both broad and worldwide. Russias Vladimir Putin has gone as far as to state that whoever becomes the leader in [AI] will become the ruler of the world. See The Verge (Sept. 4, 2017), https://www.theverge.com/2017/9/4/16251226/russia-ai-putin-rule-the-world.

It is thus no surprise that companies are heavily investing to protect the intellectual property generated from their investments in AI technology. See PwC MoneyTree Report (Q4 2018), https://www.pwc.com/us/en/moneytree-report/moneytree-report-q4-2018.pdf. The question becomes how best to protect those investments in this critical space. For example, an autonomous driving company may be looking at its AI training data (i.e., records of previous test drives), the artificial neural network implementations generated from that training data (i.e., the software that helps the car drive itself), and assortments of other data necessary to operate an autonomous car. For each of these elements, the company must examine what aspects are patentable, subject to trade secret protectionor both. A misstep could result in the company being left with no meaningful intellectual property protection for its most important research and development.

But patenting AI technology today can be difficult. Due to the prohibition on patenting abstract ideas, acquiring meaningful patents on artificial intelligence systems is not straightforward. Thus, companies are increasingly turning to trade secret protection to protect their AI-related intellectual property. This article explores the tradeoffs between patents and trade secrets in the AI sector. It then describes how trade secrets have become essential tools for companies to protect their AI-related intellectual property. Finally, it concludes with practical guidance on how to leverage both patents and trade secrets to best protect valuable intellectual property regarding AI.

What is Artificial Intelligence?

First, a word on terminology. Some companies invoke the term artificial intelligence to describe their products at the earliest opportunityeven when the underlying technology does not fit within the established definition of artificial intelligence. For the purpose of this article, artificial intelligence generally refers to technology that, in some sense, mimics human intelligence. In particular, AI under this definition permits computers to perform some task without being expressly programmed to do so. To that end, this article will focus on machine learning, neural networks, and related training models, algorithms and data.

Patents Versus Trade Secrets The Tradeoff of Public Disclosure

Patents confer a legal right to exclude others from making, using, selling, and importing into the United States the claimed invention for a number of years. But, in order to take advantage of this government-sanctioned monopoly, the inventor must disclose the invention to the public with enough detail such that the invention can be recreated by others in that field. This quid-pro-quoa disclosure of the invention to the public in return for a limited-in-time monopoly on the inventionis a fundamental underlying policy objective of U.S. patent law.

By contrast, trade secrets, as the name suggests, protect information that is secret. Trade secrets can provide protection for any information where the owner has taken reasonable efforts to keep such information secret and the information derives independent economic value, actual or potential, from not being generally known to other persons. See, e.g., 18 U.S.C. 1839(3) (Federal Defend Trade Secrets Act, definition of trade secret); Cal. Civil Code 3426.1(d) (California Uniform Trade Secrets Act, definition of trade secret). Both federal and state law provide protection for trade secrets. Historically, trade secret protection has been applied to a wide variety of subject matter, including compilations of public data, source code, schematics, diagrams, and customer listsamongst many other pieces of information.

In many ways, trade secret law can be broader or more flexible than patent law. Unlike patents, trade secret protection can be obtained without any application or registrationit arises automatically if the trade secret owner takes appropriate steps to ensure the information is secret, so long as the information provides a competitive benefit. Trade secret protection can also theoretically last as long as the information is kept secret. And trade secret law protects items which would not be proper subjects for consideration for patent protection under 35 U. S. C. 101. Kewanee Oil Co. v. Bicron Corp., 416 U.S. 470, 482-3 (1974). For example, a list of customers could be protected as a trade secret, but certainly not by a patent.

In other ways though, trade secret protection is weaker than patent protection. Importantly, independent development is a defense to trade secret misappropriation but not for patent infringement. As explained by the Supreme Court in 1974:

Trade secret law provides far weaker protection in many respects than the patent law. While trade secret law does not forbid the discovery of the trade secret by fair and honest means, e. g., independent creation or reverse engineering, patent law operates against the world, forbidding any use of the invention for whatever purpose for a significant length of time. The holder of a trade secret also takes a substantial risk that the secret will be passed on to his competitors, by theft or by breach of a confidential relationship, in a manner not easily susceptible of discovery or proof. Where patent law acts as a barrier, trade secret law functions relatively as a sieve.

Kewanee, 416 U.S. at 489-490 (footnote and citation omitted).

This view is not universal. One can ask whether Coca-Cola, the holder of one of the most famous trade secretsthe formula for Coca-Colawould agree with it. More to the point, times have changed since the Kewanee decision in the 1970s. For certain types of innovations related to AI, the pendulum may be swinging away from patent protection and towards trade secret protection.

The Difficulties in Patenting AI Alice and Abstract Ideas

Recent years have seen a rapid acceleration in the number of patent applications directed to inventions in the field of artificial intelligence. More than half of all AI-related patent applications have been published since 2013. See WIPO Technology Trends 2019, Artificial Intelligence, at 13 https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf. Within that time, applications related to machine learning have grown by an average of 28% each year, applications related to computer vision have grown by an average of 46% each year, and applications related to robotics and control methods have grown by an average of 55% each year.

Despite this surge in applications, however, there are potential pitfalls to seeking patent protection over AI-related inventions. In particular, to receive a patent, the patent must claim patent-eligible subject matter under 35 U.S.C. 101. One category that is ineligible for patent protection is abstract ideas. Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014). Over the last 15 years, the general trend in the case law has been to apply the prohibition on patenting abstract ideas more strictly to software-centric inventions.

Given the limitations articulated in Alice and its progeny, it is unclear how many of the AI-related patents that have made their way through the U.S. Patent Office would survive in eventual litigation. See, e.g., Hyper Search, LLC v. Facebook, Inc., No. CV 17-1387-CFC-SRF, 2018 WL 6617143, at *10 (D. Del. Dec. 17, 2018) (invalidating patent with neural network module); see also Purepredictive, Inc. v. H20.AI, Inc., No. 17-CV-03049-WHO, 2017 WL 3721480, at *5 (N.D. Cal. Aug. 29, 2017),affd sub nom.Purepredictive, Inc. v. H2O.ai, Inc., 741 F. Appx 802 (Fed. Cir. 2018) (invalidating patent directed to automating predictive analytics). While each of these patents stands on its own and the decisions do not indicate that any future AI-related patents are necessarily invalid (or valid), they stand as guideposts that companies should be mindful of when considering patenting artificial intelligence inventions.

Finally, some AI-related innovations are simply not eligible to receive patent protection at all. For example, raw data collected for use in machine learning algorithms is not patentable in and of itself. That raw data combined with a conventional and well-known machine learning algorithm may also be unpatentable, even though the result may be incredibly valuable to the company. Considering these risks, many companies are turning to alternatives to protect their intellectual property in the AI spacenamely, trade secrets.

Trade Secrets An Apt Tool for Protection of AI Intellectual Property

While it is impossible to know the number of AI trade secrets being closely held by organizations around the world, it is likely that most AI intellectual property generated in the United States today is being protected through the use of trade secrets. While specific details remain confidential in light of strict protective orders, courts have already indicated that certain areas of information related to AI are protectable as trade secrets, such as algorithms, source code, and the way a business utilizes AI to implement machine learning.

There are certain practical advantages to trade secret protectionno filings fees, protection in real-time, theoretically unlimited length of protection, and broadly eligible subject matter. For AI in particular, there are several reasons why trade secrets are particularly valuable and suitable for intellectual property protection as compared to patents:

While trade secrets are increasingly important for AI companies, one major drawback in utilizing trade secrets is that protection is only afforded to the extent the intellectual property can be kept secret. Keeping software a secret can be challenging and operationally taxing for several reasons: (1) given the turnover at technology companies, strong employment agreements are needed to ensure departing employees are legally required to keep trade secrets secret; (2) given the ease of stealing softwarewhich can be as easy as downloading code to a USB drivestrong cybersecurity policies need to be created and enforced; (3) because reverse engineering can be a defense to trade secret misappropriation, software needs to be designed and deployed in a way to ensure reverse engineering is not possible (see, e.g., Sargent Fletcher, Inc. v. Able Corp., 110 Cal. App. 4th 1658, 1670 (2003) (Evidence of independent derivation or reverse engineering directly refutes the element of use through improper means.); N. Am. Deer Registry, Inc. v. DNA Sols., Inc., 2017 WL 2402579, at *7 (E.D. Tex. June 2, 2017) (trade secret is not misappropriated if there is reverse engineering or independent derivation)); and (4)in order to conduct business, it is often necessary to share technology widely with employees and partners, which increases the risk that a trade secret could be disclosed publicly.

In light of these concerns, maintaining trade secret protection can incur meaningful costs for a company and requires significant ongoing vigilance. Ultimately, a trade secret is only protected so long as it remains a secret. Even with strict regulations in place, companies always run the risk that the information will become public.

Patent vs. Trade Secret: Making the Right Decision for AI-Related Inventions

Even though trade secrets are important to protect AI-related intellectual property, there remain different advantages and drawbacks for both patents and trade secrets. The decision whether to patent or keep as a trade secret a given innovation thus represents an important strategic decision for any company. Here are some guiding factors to consider when making these kinds of critical decisions:

Is the innovation eligible for patent protection? Does the innovation satisfy the requirements of the Patent Act, including being patent-eligible subject matter under 35 U.S.C. 101? If not, then patents are unavailable and trade secret protection is the best option.

Does the innovation comprise the type of information that can be kept secret as part of your business? If the innovation is readily discernable from the product itself or by other appropriate means, trade secret protection is unavailable. The trade secret in that instance would not be secret. Thus, patent protection would be the best option.

Is the innovation likely to become generally known soon? Trade secrets only protect information that is not generally known. If the innovation is one that competitors or academia is likely to be making public relatively soon, then trade secret protection is sub-optimal. Instead patent protection may be the best option.

How likely is the patent able to withstand an attack in litigation? Even if the patent may be approved by the patent office, if you believe the patent is unlikely to withstand an attack in litigation, it may be better to keep the innovation as a trade secret so the underlying intellectual property does not have to be disclosed to the public.

How quickly will the invention become obsolete? If the invention will become obsolete quickly, the length of protection that patents provide (and the cost and effort to file the patent), may not be worth the benefit.

How quickly can the invention be commercialized? Conversely, if the invention will take a long period of time to monetize, the length of protection afforded by a patent will allow time for long-term investment and capitalization.

Is the innovation worth patenting? Patents cost time and money to prosecute and obtain. Not all innovations are worth that effort. For certain types of know-how, it may be more practical to utilize trade secrets to protect the innovation rather than a patent.

Both patents and trade secrets offer powerful ways for companies to protect their AI-related intellectual property. Each can be effective in certain circumstances. In most cases, optimal protection strategies will involve a thoughtful use of both regimes.

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The ethics of artificial intelligence and automation amid a global pandemic – Morning Star Online

BRITAINS healthcare crisis has been catapulted centre stage recently as our beloved NHS warriors battle Covid-19 deprived of personal protective equipment.

Yet these indispensable heroes have long toiled exhaustinghours, struggling under a lethal concoction of heightened demand and a lack of resources,a Brexodus of staff and the coronavirus pandemic is just icing on the crumbling cake.

Vacancies are widespread. In social care, it is estimated shortages have spiralled to 122,000.

Thousands of retirees have flocked to the NHS frontlines these past few weeks, but once Britainreturns to some degree of normality, the post-Brexit immigration plan swoops in to exacerbate the vacancies once more.

Migrant healthcare staff will face exorbitant visa fees as soon as December while care workers flatly wont even qualify for a skilled worker visa.

However, the Home Office has designed a rather unorthodox and arguably unscrupulous alternative: to replace the grit and graft of flesh-and-blood migrant staff with artificial intelligence and automated robots.

Already the initiative has swallowed 284million allinall, but the government will need to pluck some more golden leaves from its magic money treeif it is to realistically transform sectors that are reliant on EU labour with automation in nine months time.

Still, the practicality of this mission when the Home Office is notorious at underdelivering and delaying projects is one thing when there is a hot debate over whether care robotsshould be wheeled in at all.

The inclusion of technology in healthcare is often framed as a step towards depravity: it invokes a consensus that society inches towards a dystopian nightmare where humans become enslaved to sentient androids, 15m jobs become sacrificed at the altar of an AI-modified world and military killer machines surpass human intelligence to bring a nuclear winter to the human race.

Stephen Hawking himself did warn us of this possibilityas have Google employees who walked out in defiance of AI warfare.

Still, job losses seem quite inevitable, but should this be an acceptable consequence of progress?

Labour MPYvette Cooperargued that a technological revolution could further entrench the stark inequalities that already exist in Britain and make extreme poverty a permanent part of our social fabric.

And she isnt wrong: Japan, at the core of technological enlightenment, has seen automation overthrowmultiple industries with robot-run hotels, restaurants and conveyor belts of food being common.

Yet others, tech giants and their allies of dreamers, envision a post-work utopia where tech bridges societies into a new world of fullyautomated luxury communismand where its gains are shared equally by all.

On this note, it is ironic that Covid-19 has pressed the Home Office to make a dramatic U-turn from its submissive acceptance of job losses.

Commiting to pay 80 per centof workers wages who are most likely to become affected by automation in the next 20 years might seem like a change of heart, but sceptics might best believe that the infrastructure for automation just simply isnt ready yet and the cogs are still needed to keep the economy oiled up until this point.

In terms of healthcare, however, there are additional concerns.

AI still bitterly lacks the empathy required for the job while algorithms are shown to absorb the darkest depths of human biases.

AI systems can only look at the world through the peripheral granted to it by its makers who, by and large, are mostly male and white.

The result has seen recognition software repeatedly misinterpret facial expressions and body language on the assumption that everyone expresses themselves in the same way as Westerners while AI favours men over women in job interviews and even prefers European-American names over African-American ones.

Not that the government pays much attention to this acute factor: its very own visa algorithm has been found to discriminate against applicants of a certain nationality.

At a very basic level, one would expect care robotsto be equipped to administer some form of care.

Yet humanoids lack the intellectual problem-solving and altruism needed to adhere to physically demanding and emotionally intuitive surroundings.

At best, they can dance, entertain, push a tray of food and deliver medication to a specified destination.

But they are defunct of tactile touch. It cannot brush hair, dry tears or offer a hand to hold with comforting words in the darkest of days.

It cannot compute the nuances of human emotion and speech. Social care was ranked as one of the least automatable jobs of all in only 2016 as a result, but others just flatly find care robotsto camp in the category of undignified.

Only 26 per centof respondents to a survey said they would feel comfortable being hoisted and attended to by a robot when in care or a hospital, and many understandably have concerns around camera-fitted and potentially hackable devices in the rise of spy campornography.

However, the coronavirus pandemic may have considerably shaken the narrative and has, by twist of fate in the governments favour, propelled the case for AI in British healthcare forward.

Technology has undoubtedly played a vital role in this unparalleled era of segregation; FaceTiming loved ones, YouTube yoga classes, Skype work conferences and live-streamed concerts and theatre shows have kept Britons indoors while still relatively connected and entertained as before.

Yet even further afield, AI has become pivotal in delaying the spread of Covid-19.

In one hospital at the heart of the outbreak in Wuhan, China, robots outnumbered doctors as they patrolled the corridors, disinfected areas and monitored patients temperature and overall wellbeing.

The CEO behind this remarkable technology argued thatrobots do not carry disease, and robots can be easily disinfected.

Other countries, such as Singapore, Iran and Israel, have resorted to far more draconian invasions on civil liberties through the use of tech.

Yetspymobile tracking apps and ramped-up surveillance haveproved paramount in curbing the death toll and a similarly designed app by the NHS may be coming to Britain in the next few weeks.

Even so, care robots overseas appear quite revolutionary.

Consider Pepper, a humanoid bot, that is able to entertain residents with knitting and exercise classes in care homes and help the staff with mundane tasks.

The therapeutic cuddly seal, Paro, has been proven to soothe Alzheimers sufferers.

Kirobo by Toyota similarly comforts childless adults; RoBear can physically lift patients from wheelchairs and Leka can break through barriers to communicate with autistic children.

Already the NHS uses digital aides which can outperform human hands and eyes in intricate surgeries and when detecting breast cancer and the early onset of Alzehimers disease.

Evidently, tech can be a force for good if executed right. The University of Oxford, McKinsey Global Institute, PwC and Shift Commission predict that although millions of jobs will fall victim to automation, social and healthcare will emerge largely unscathed.

The NHS will still be dominated by human staff, yet tech could vastly alleviate doctors attendance to paperwork by 5.7m hours, generating a saving of13 billion.

Similarly, social care could save 6bn according to surgeonLord Darzi.

However, the biggest battle for tech remains in public confidence; confidence that has waned in the government as it arbitrarily stifles migration while the frail become collateral damage.

Tech wont be able to slice through social inequalities for as long as overzealous benefit assessors penalise disabled and vulnerable people for making improvements in their lives.

This move towards automation risks exacerbating the wealth and class division system in Britain and appears little more than another hostile political ploy to warrant the governments anti-migrant agenda.

Olivia Bridge is a political correspondent for the Immigration Advice Service,an organisation of Britainand Irelandimmigration lawyers.

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The ethics of artificial intelligence and automation amid a global pandemic - Morning Star Online

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Artificial intelligence can take banks to the next level – TechRepublic

Banking has the potential to improve its customer service, loan applications, and billing with the help of AI and natural language processing.

Image: Kubkoo, Getty Images/iStockPhoto

When I was an executive in banking, we struggled with how to transform tellers at our branches into customer service specialists instead of the "order takers" that they were. This struggle with customer service is ongoing for financial institutions. But it's an area in which artificial intelligence (AI), and its ability to work with unstructured data like voice and images, can help.

"There are two things that artificial intelligence does really well," said Ameek Singh, vice president of IBM's Watson applications and solutions. "It's really good with analyzing images and it also performs uniquely well with natural language processing (NLP)."

SEE:Managing AI and ML in the enterprise 2020 (free PDF)(TechRepublic)

AI's ability to process natural language helps behind the scenes as banks interact with their customers. In call center banking transactions, the ability to analyze language can detect emotional nuances from the speaker, and understand linguistic differences such as the difference between American and British English. AI works with other languages as well, understanding the emotional nuances and slang terms that different groups use.

Collectively, real-time feedback from AI aids bank customer service reps in call centersbecause if they know the sentiments of their customers, it's easier for them to relate to customers and to understand customer concerns that might not have been expressed directly.

"We've developed AI models for natural language processing in a multitude of languages, and the AI continues to learn and refine these linguistics models with the help of machine learning (ML)," Singh said.

SEE:AI isn't perfect--but you can get it pretty darn close(TechRepublic)

The result is higher quality NLP that enables better relationships between customers and the call center front line employees who are trying to help them.

But the use of AI in banking doesn't stop there. Singh explained how AI engines like Watson were also helping on the loans and billing side.

"The (mortgage) loan underwriter looks at items like pay stubs and credit card statements. He or she might even make a billing inquiry," Singh said.

Without AI, these document reviews are time consuming and manual. AI changes that because the AI can "read" the document. It understands what the salient information is and also where irrelevant items, like a company logo, are likely to be located. The AI extracts the relevant information, places the information into a loan evaluation model, and can make a loan recommendation that the underwriter reviews, with the underwriter making a final decision.

Of course, banks have had software for years that has performed loan evaluations. However, they haven't had an easy way to process foundational documents such as bills and pay stubs, that go into the loan decisioning process and that AI can now provide.

SEE:These five tech trends will dominate 2020(ZDNet)

The best news of all for financial institutions is that AI modeling and execution don't exclude them.

"The AI is designed to be informed by bank subject matter experts so it can 'learn' the business rules that the bank wants to apply," Singh said. "The benefit is that real subject matter experts get involvednot just the data scientists."

Singh advises banks looking at expanding their use of AI to carefully select their business use cases, without trying to do too much at once.

"Start small instead of using a 'big bang' approach," he said. "In this way, you can continue to refine your AI model and gain success with it that immediately benefits the business."

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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Riding the wave of artificial intelligence with Sensitrust – Blockmanity

The disruptive technologies of this era have brought about a term called programmable economy, created by Gartner Inc that describes the all-new smart economy which is a result of technological innovations. It is the way now goods and services are created and consumed that has enabled diverse ways of exchanging monetary and non-monetary values. The traditional ways, which appear very inefficient and non-optimal, are making it difficult for companies to get a position in this competitive world. Time-to-market has become crucial and a delay in product or service release leads to loss of money and reputation.

This wave of Millennials and now Gen Z workforce is all about passion economy which enables them to pursue what they love and make money out of it. The idea of working from home, flexible hours and new business models to support this is inundating the workspace and the job arena. This change calls for an evolution in the way recruiting is done, with the advent of artificial intelligence in this space.

Blockchain technology is an emerging technology being adopted by forward-looking companies which is all about shifting from a centralized to a decentralized, transparent, and safe way of managing data. Using this technology, the activities of all the stakeholders of a project are supported by Smart Contracts, while the adoption of sophisticated methods of Artificial Intelligence helps the stakeholders to make business-critical decisions.

In this context, working nomads, who travel and still keep in touch with their customers, is very alluring but also requires safety measures and regulation. Sensitrust aims to be that bridge between customers and professionals to define a new ecosystem of safe interactions by exploiting the peculiarities of Blockchain, Smart Contracts, and Artificial Intelligence technologies.

Blockchain technology is also referred to as DLT (Distributed Ledger Technology) and is a means to share digital assets whose integrity is preserved by maintaining a transactional ledger of all changes happening to the asset. This revolutionary technology allows a scalable and risk-free system for several uses.

How will it be if you can get the right kind of data, which is always up to date, which matches professionals to customers and gives the most appropriate advice by filtering from a large amount of data?

Instead of rummaging through a wide array of profiles, many of which are of no use to you, you can actually get a selected few which are an ideal match for your requirements. Imagine how much time you would save and also make a risk-free selection by eliminating wrong profiles.

This is where the AI technology, adopted by Sensitrust, comes in with its predictive engine. It acts like a human expert who has accumulated a huge experience analyzing historical data, collected organically in the platform, to predict the outcome of newly occurring situations.

The predictive engine of Sensitrust is capable of learning from mistakes automatically in a transparent way using deep neural networks and many other models. The many ways it helps customers and professionals are the following:

The Sensitrust native token (SETS token) will be used to access all such services at a discounted rate.

This plethora of capabilities provided by Sensitrust is backed by a team of highly informed technical wizards who make use of the latest and most sophisticated AI and Machine Learning approaches, including:

Sensitrust is a platform which helps in managing data and artefacts used for carrying out projects, by means of Smart Contracts, throughout all the phases of a project which is developed using this platform. The many applications of Sensitrust can be found in the IT industry for hiring quality professionals, in the banking domain by replacing traditional operations with Blockchain-based solutions, and also in the Academy, for the identification of expert reviewers as well as of an international team for the implementation of research projects.

Buy SETS for a price of 0.05.

Token Sale for Sensitrust is Live at https://www.sensitrust.io/

Disclaimer: Blockmanity is a news portal and does not provide any financial advice. Blockmanity's role is to inform the cryptocurrency and blockchain community about what's going on in this space. Please do your own due diligence before making any investment. Blockmanity won't be responsible for any loss of funds.

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Artificial Intelligence (Not Only) for Business: What is Artificial Intelligence? – The Slovak Spectator

Author Martin Spano explains the importance of AI in our business and personal lives.

Martin Spano is the author of Artificial Intelligence in a Nutshell, a book that explores the mystified subject of artificial intelligence (AI) with simple, non-technical language. Spanos passion for AI began after he watched 2001: A Space Odyssey, but he insists this ever-changing technology is not just the subject of sci-fi novels and movies; artificial intelligence is present in our everyday lives.

What first comes to your mind when you hear the term artificial intelligence? Terminator, Wall-E, Ava from Ex Machina, or Samantha from Her? Yes, these are examples of artificial intelligence, but they are hypothetical, and it is not certain that we will be able to construct machines like this in the future. They are examples of artificial intelligence at the human level, and that is why we call it general artificial intelligence. But let's deal with the artificial intelligence we already have today because this intelligence can handle activity from only one area. Although it is often more efficient than man, we call it weak (narrow or specialised) artificial intelligence.

You may be asking yourself: Artificial intelligence is talked about everywhere, but how does it benefit me? Will it help me in business? Or at least in my private life? The answer to both questions is "yes".

Just think about your normal day. When you check your emails, you usually see the ones that are important because the spam has been filtered out by artificial intelligence. When you browse the news on social networks, artificial intelligence creates your front page. When you travel by car, whether for business or pleasure, artificial intelligence will suggest the optimal route to your destination. When you look for information in a search engine to make it as relevant as possible to you, artificial intelligence is used. When shopping online, artificial intelligence recommends products. When you relax by listening to music or watching a video, artificial intelligence recommends them again. Artificial intelligence is everywhere around us without us realizing it, whether it's an intelligent spam filter, a referral mechanism, a virtual assistant like Siri, Google Assistant, Cortana and Amazon Alexa or an autonomous car.

We have listed examples of artificial intelligence, but what exactly is artificial intelligence? Simply put, artificial intelligence means using computers for activities that usually require human intelligence. It is software or a computer programme with the ability to learn. It uses learned knowledge to make decisions in new situations, thus showing signs of intelligence. But since this intelligence does not come from man, it is called artificial intelligence.

Artificial intelligence as a scientific field has existed for over sixty years. Nevertheless, it is only in the last decade that we have made significant progress, thanks to which it is currently being talked about on a daily basis. For this reason, I decided to write a series of blog posts for you to bring this topic as close as possible to you and at the same time prove that you do not have to worry about it at all; on the contrary, you can benefit from it, both in business and in your private life.

27. Apr 2020 at 11:01 |Martin Spano

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Outbreak Science: Using artificial intelligence to track the coronavirus pandemic – 60 Minutes – CBS News

When you're fighting a pandemic, almost nothing matters more than speed. A little-known band of doctors and hi-tech wizards say they were able to find the vital speed needed to attack the coronavirus: the computing power of artificial intelligence. They call their new weapon "outbreak science." It could change the way we fight another contagion. Already it has led to calls for an overhaul of how the federal government does things. But first, we'll take you inside BlueDot, a small Canadian company with an algorithm that scours the world for outbreaks of infectious disease. It's a digital early warning system, and it was among the first to raise alarms about this lethal outbreak.

It was New Year's Eve when BlueDot's computer spat out an alert: a Chinese business paper had just reported 27 cases of a mysterious flu-like disease in Wuhan, a city of 11 million. The signs were ominous. Seven people were already in hospitals.

Almost all the cases came from the city's sprawling market, where live animals are packed in cages and slaughtered on-site. Medical detectives are now investigating if this is where the epidemic began, when the virus made the leap from animals to us.

Half a world away on the Toronto waterfront, BlueDot's founder and CEO, Dr. Kamran Khan, was on his way to work. An infectious disease physician, he had seen another coronavirus in 2003 SARS kill three colleagues. When we spoke with him remotely he told us this outbreak had him worried.

Dr. Kamran Khan: We did not know that this would become the next pandemic. But we did know that there were echoes of the SARS outbreak, and it was something that we really should be paying attention to.

COVID-19 soon got the world's attention. BlueDot's Toronto staff now works from home, except for Dr. Khan. But in December, the office kicked into high-gear as they rushed to verify the alert.

Chinese officials were secretive about what was happening. But BlueDot's computer doesn't rely on official statements. Their algorithm was already churning through data, including medical bulletins, even livestock reports, to predict where the virus would go next.

It was also scanning the ticket data from 4,000 airports.

BlueDot wasn't just tracking flights, but calculating the cities at greatest risk. On December 31, there were more than 800,000 travellers leaving Wuhan, some likely carrying the disease.

Dr. Kamran Khan: So these yellow lines reflect the nonstop flights going out of Wuhan. And then the blue circles reflect the final destinations of travelers. The larger the circle, the larger number of travelers who are going to that location. These were many of the first cities that actually received cases of COVID-19 as it spread out of mainland China.

Bill Whitaker: You can do that in a matter of seconds?

Dr. Kamran Khan: We can analyze and visualize all this information across the globe in just a few seconds.

The virus wasn't just spreading to east Asia. Thousands of travelers were heading to the United States too.

Dr. Kamran Khan: Most of the travel came into California and San Francisco and Los Angeles. Uh, also, into New York City. And we analyzed that way back on December 31. Our surveillance system that picked up the outbreak of Wuhan automatically talks to the system that is looking at how travelers might go to various airports around Wuhan.

Bill Whitaker: So when you see that map, you don't just see flight patterns?

Dr. Kamran Khan: If you think of an outbreak a bit like a fire and embers flying off, these are like embers flying off into different locations.

Bill Whitaker: So in this case, that ember landed in dry brush in New York and started a wildfire?

Dr. Kamran Khan: Absolutely.

Dr. Khan told us he had spent the better part of a year persuading the airlines to share their flight data for public health. Nobody had ever asked that before. But he saw it as information gold.

Dr. Kamran Khan: How is it that someone knows 16B - that seat is available, but 14A has been taken? There clearly must be some kind of information system.

Bill Whitaker: Why is that so important?

Dr. Kamran Khan: There are over 4 billion of us that board commercial flights and travel around the world every year. And so that is why understanding population movements becomes so important in anticipating how disease is spread.

The virus spread across Asia with a vengeance. BlueDot has licensed access to the anonymized location data from millions of cellphones. And with that data it identified 12 of the 20 cities that would suffer first.

Dr. Kamran Khan: What we're looking at here are mobile devices that were in Wuhan in the previous 14 days and where are they now across East Asia. Places like Tokyo have a lot of devices, Seoul in South Korea--

Bill Whitaker: So you're following those devices from Wuhan to these other cities?

Dr. Kamran Khan: That's correct. I do wanna point out these are also anonymized data. But they allow us to understand population movements. That is how we can understand how this virus will spread.

To build their algorithm, Dr. Khan told us he deliberately hired an eclectic mix: engineers, ecologists, geographers, veterinarians all under one roof. They spent a year teaching the computer to detect 150 deadly pathogens.

Dr. Kamran Khan: We can ultimately train a machine to be reading through all the text and picking out components that this is talking about an outbreak of anthrax and this is talking about the heavy metal band Anthrax. And as you do this thousands and thousands and thousands of times, the machine starts to get smarter and smarter.

Bill Whitaker: And how many different languages does the computer understand?

Dr. Kamran Khan: So it's reading this currently in 65 languages, and processing this information every 15 minutes, 24 hours a day. So it's a lotta data to go through.

Within two hours of detecting the outbreak on December 31, BlueDot had sent a warning of the potential threat to its clients: public health officials in 12 countries, airlines and frontline hospitals, like Humber River in Toronto.

Dr. Michael Gardam: We've been able to really make a lot of decisions, I think, a little bit earlier 'cause I kinda feel like we had a bit of an inside scoop here.

One of Canada's top infectious disease physicians, Dr. Michael Gardam, told us it was like getting real time intelligence.

Bill Whitaker: What did you do when you got that information from BlueDot?

Dr. Michael Gardam: Getting that intel allowed me to kinda be the canary in the coal mine, to stand up and say we need to pay attention to this. And to start thinking about it, start thinking about supplies, start thinking about how busy we might be.

Dr. Michael Gardam: Now at this point, everybody knows about CoVid-19. But it's, it's not so much now. Now you've pretty much bought whatever PPE you can buy, it's very hard to buy that anymore. It's what did you do a month and a half ago that was so important. So, none of this is any surprise to us whatsoever, and yet, you see countries around the world where this has been a surprise.

BlueDot had no clients in the U.S., so while Dr. Gardam's hospital was making plans in January, President Trump, as late as March, was still assuring Americans that everything was under control.

California wasn't so sure, and braced for the worst. In March, it became the first state in the country to lock down its cities. Mickey Mouse suddenly looked lonely, drivers had only dreamed of such empty freeways. But the lock-down bought time. Despite having its first case of COVID-19 five weeks before New York, California dodged the hurricane of infection that slammed into New York City. At his daily teleconference in Sacramento, Governor Gavin Newsom made no secret where he'd gotten his edge: outbreak science.

Gavin Newsom: It's not a gross exaggeration when I say this the old modeling is literally pento-paper in some cases. And then you put it into some modest little computer program and it spits a piece of paper out. I mean, this is a whole other level of sophistication and data collection.

With the virus spreading around the world, California enlisted the help of BlueDot, Esri, Facebook and others, using mapping technologies and cell phone data to predict which hospitals would be hit hardest, and see if Californians were really staying at home. Data became California's all-seeing crystal ball.

Gavin Newsom: We are literally seeing in to the future and predicting in real-time based on constant update of information where patterns are starting to occur before they become headlines.

Bill Whitaker: Can you just sort of like, give me an example?

Gavin Newsom: We can see in real time on a daily basis, hourly basis, moment-by-moment basis if necessary, whether or not our stay-at-home orders were working. We can truly track now by census tract, not just by county.

Here's what it looked like. BlueDot scanned anonymous cell phone data over a 24-hour period last month in Los Angeles. The blue circles indicate less movement than the week before, the red spots show where people are still gathering. It could be a hospital or a problem. That cellphone data allows public health officials to investigate. It also raises worrisome privacy issues.

Bill Whitaker: How are you able to ensure that this cell phone data will remain anonymous?Gavin Newsom: Well, I didn't want to take the companies' words for it, I say that respectfully. I have a team of folks that are privacy-first advocates in our Technology Department. And we are making sure that no individualized data is provided. If it is, we're out.

Bill Whitaker: So what's been the most frustrating part of this for you?

Gavin Newsom: It's just incumbent upon us to have a national lens. And to recognize we'remany parts but one body. And if one part suffers, we all suffer.

Bill Whitaker: From this experience, do you think the Federal Government needs to overhaul the way it tackles pandemics?

Gavin Newsom: I don't know that there's a human being out there, maybe one or two, that would suggest otherwise. No, the absolute answer is, of course, unequivocally.

Dylan George: Data technology has transformed the way we do business in many aspects of our lives. But it has not transformed the way things are done in public health.

For Dylan George that's an urgent priority. As a scientist tracking biological threats in the Bush and Obama administrations, he has seen first-hand what he calls the panic-neglect cycle.

Dylan George: Perhaps the most tragic idea in all of public health is this: in a time of an outbreak everyone lights their hair on fire and is running around trying to figure out. After it's over, everyone forgets about it

He has joined a growing number of scientists pressing to revive an old idea: an infectious disease forecasting center modeled on the National Weather Service.

Dylan George: We need to have professionals that their day job is dedicated to helping us understand how infectious diseases will-- will risk our well being economically and from a national security perspective.

Bill Whitaker: That idea has been kicking around for a while. It's never gotten the funding. Do you think things will be different this time?

Dylan George: When we see that there is $2 trillion being spent on stimulus bills to help us get out of this, to make sure that we can rebound, we need to think transformatively. We need to think broadly about how we can move these things forward. This kind of a center would help us do that.

As the coronavirus continues to upend our lives, Toronto's Dr. Michael Gardam told us he has seen the difference a digital early-warning system can make.

Dr. Michael Gardam: One of the biggest challenges in infectious diseases is you never wanna be the doctor that picks up the first case because you're probably going to miss it. And you probably weren't wearing the right gear and it's probably already spread in your hospital. And so getting the early warning that help gives you the intel to make that first call is so incredibly important.

Produced by Heather Abbott. Associate producer, David M. Levine. Broadcast associate, Emilio Almonte. Edited by Robert Zimet.

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Concept Clash: Ex Machina vs. Her and the Best of Artificial Intelligence – Popcorn Talk Network

PTN Concept Clash: Ex Machina vs Her

By Evan Wessman

Welcome to Popcorn Talks Concept Clash where we take movies with similar themes or storylines and extensively compare and contrast them in every way. Which of these films explored the theme more successfully? Do they come to the same conclusions? Do the differences between them tell us something about the concept, the filmmakers, maybe even ourselves? Join us as we compare the two clashing (or complementary) cinematic takes on the concept of love between the scheming meat of men and the elegant algorithms of the artificial women who indulge them. PTN Concept Clash presents Ex Machina Vs. Her.

What if a human fell in love with a computer? Her and Ex Machina may not be the only movies that ask this question, but Im going to boldly call them the best explorations of this question. Both take vastly different approaches to the question and examine different aspects of artificial intelligence. Her puts nearly all of its focus on what an intimate relationship between a human and a machine might look like, while Ex Machina centers around whether its AI character is truly an AI. I sincerely dont know which film will win this face-off, or if there will be a winner. Along with the ratings listed below, each film won a single Oscar, Ex Machina for visual effects and Her for original screenplay. So lets see which machine will defeat the other.

AI Characters

The AI characters in these two movies are different but equally good. In Her, we get an operating system called Samantha voiced by Scarlett Johannsen, and in Ex Machina, we get a humanoid robot named Ava played by Alicia Vikander. In terms of design, we learn almost nothing about how Samantha and the other OSs in Her are created or how they function. Conversely, Ex Machina spends a fair amount of time telling the audience the science behind Ava, much of which sounds plausible, at least when compared to the way science is usually incorporated into sci-fi stories. As to what Ava can actually do given her artificial intelligence, we are left somewhat in the dark. Many of her abilities become painfully clear by the end of the movie, but just as many are left unexplored, and there are indications that Ava might even be playing dumb during much of the movie. As for Samanthas abilities, we are given a pretty good sense of them, and because we are all familiar with computer operating systems that are in real-life computers, we already have a relative idea of what Samantha can do. Where things get interesting is when Samantha and the other OSs start to expand beyond what they originally thought they were capable of. A curious parallel between the AIs in both films is that they both have the ability to grow and are not limited by the code they were created with. As noted, we arent actually told how Samantha was programmed like we are with Ava, but the events of Her imply that the OSs develop beyond what their creators planned. Similarly, Ava works just as well as her creator Nathan hoped she would, but even he doesnt account for how intelligent she is capable of becoming.

The most obvious difference between these characters is that Ava has a physical form whereas Samantha does not, something that Samantha actually becomes rather insecure about. This allows Ava to pass for human, although we never really get to see her do this. However it also seems to limit her in comparison to Samantha, who can interact with an apparently infinite amount of other people and OSs at once. But the most important difference between the two types of AI is that Ava has malicious intent while Samantha and the OSs are relatively benign. By the end of Ex Machina, Ava has killed her creator and manipulated another human into setting her free while she leaves him to die. In retrospect, the film treats her almost like a monster waiting to be released. This could not be more different from Samantha, who is shown to be more similar to a super-advanced generation of smartphone. Importantly, Samantha has human emotions, insecurities, social tendencies, and desires, whereas Ava is able to imitate such human qualities without actually possessing them. So, as I said at the outset, there is no winner for this category as Samantha and Ava are both equally important and well-crafted but vastly different takes on an AI character.

Credit: Ex-Machina

Human Characters

This category kind of has to go to Her by default just because of how well-crafted Theo is and how deep the movie goes into his psychology. By now, most of us have acknowledged that Joaquin Phoenix is an impeccably talented actor, and he gives a stellar if subtle performance of an existentially depressed man in the middle of a divorce. His relationship with Samantha, while nontraditional, is more fleshed out than most on-screen relationships of any kind. Both Phoenixs performance and Spike Jonzes writing give us a protagonist that feels about as close to a real human being as can be expected in two hours of screen time as we see all of his struggles, his joys, and his epiphanies.

Although Ex Machina does not do much in terms of character arcs, Alex Garland clearly put a lot of work into crafting his two human characters. Given Calebs position in the narrative, he could easily have degenerated into a typical audience-surrogate character that asks questions for us and floats around the story as a passive observer. Instead, he takes lots of initiative and displays an impressive knowledge of AI theory. Domhnall Gleesons performance brings a texture to the character that may go unnoticed but is crucial in making Caleb feel real. Oscar Isaacs character Nathan delivers a new take on the tech genius entrepreneur. Rather than the traditional anti-social nerdy programmer, Nathan embodies the more modern bro-grammer type that keeps Caleb and the audience on their guard the whole movie.

Credit: Her

Reflecting Society and Human Nature

Considering their subject matter, it isnt a surprise that both of these movies have a thing or two to say about our relationship to technology. Something intrinsic to the presence of an AI character is an examination of the ways that humans and machines are similar and different, and each story featuring AI has a slightly different take on what being human means. Ex Machina seems to consider gullibility and to a lesser extent hubris as defining human qualities, whereas Her considers the ability to feel joy and pain and struggle with ones identity as the essence of being human. However, one thing that is consistent across both films is that the creation of a true AI requires that humans relinquish control over the AI. In Ex Machina, this proves fatal as Ava turns on both the human characters, and her line Isnt it strange to have created something that hates you, speaks very strongly to this idea. In Her, the OSs seem to reach a higher level of consciousness and cant relate to humans as well, causing their creators to pull the plug on them.

The main sentiment that seems to underscore Ex Machina is that technology is dangerous, and that humans trust it too much. Caleb spends much of the movie building a relationship with Ava and putting his trust in her, which ultimately becomes his undoing as Ava betrays him and apparently leaves him to die. Additionally, there are several moments where the score and visual atmosphere evoke the horror genre strongly, which I vibe with a lot as a technophobe. Perhaps the scariest thing about Ex Machina is how it uses data harvesting and surveillance as plot devices in ways that are unsettlingly accurate. At one point, we learn that Ava was largely created by means of big data, though that term isnt used. So despite how well Alex Garland researched AI theory and progress and how well he relates it to his audience, his intention seems to be for his movie to serve as a warning to us not to create a real AI.

In terms of social commentary, Her serves as an insightful examination of our day-to-day relationship with technology. Theo doesnt interact with many other people for long stretches of the movie and ends up relating to Samantha better than any other human character we see, which well Ill just let you draw whatever parallels you want to how we use smart phones and social media. Regardless of what Spike Jonze might be trying to say about technology, the way that it is used in Her certainly feels grounded in reality. Also, the set design and overall feel of the near future world of the movie feels more accurate than any I have ever seen. Where other sci-fi movies use sleek tech, holograms, blindingly white interiors, and unique vehicle designs, Her uses tasteful pastel colors and presents a world that feels like a homier and slightly prettier version of our own. However, the movie focuses more on human nature and how we deal with our emotions. Or more accurately, how we often fail to deal with our emotions effectively, and how we struggle with expressing ourselves. Though Theos relationship with Samantha ultimately proves to be as real and complex as any human relationship, the question is raised of whether a humans relationship with an OS can count as a real relationship. On a deeper level, it also provides an examination of our real-life relationships and what causes them to fail, emphasized by the divorce that Theo is going through for much of the movie.

Although it is a narrow victory, I am going to give this category to Her. I will concede that Ex Machina does a better job at reflecting the realities of technology, but Her is able to present its themes and social commentary in ways that are more relevant to audiences.

Credit: Her

(AI) Accessible Intelligence

I know the title for this category is a bit of a cheap play on words, but it is relevant for what I am going to talk about. Both of these films deal in some incredibly complex subjects, but present them to the audience in ways that we can understand. As mentioned above, we learn next to nothing about how Samantha was created, but there is a lot of exposition layered into the way that she interacts with Theo. The fact that she is discovering herself as the movie goes on also helps us understand what she is and how she works.

Ex Machina deals very much in the way that Ava works, and the external plot revolves around testing whether she truly is an AI or not. Consequently, there is a lot of time devoted to talking about AI theory and explaining the technicalities of Avas design. Incredibly, none of it gets bogged down in obscure terminology while also not feeling dumbed-down in the slightest. The metaphors and simplified conversations between Nathan and Caleb about what qualifies as AI are a master class in how to write exposition, answering questions we might not even think to ask while raising just as many. Though Her is certainly not lacking in how effectively it handles complex topics, it is hard to compete with the simple brilliance of Ex Machina, giving it the win for this category.

Credit: Ex-Machina

Conclusion

As I said at the beginning, finding a winner between these two films is very hard. Even in terms of overall enjoyment, I cant decide which one I like better. But I am going to give the win to Her. It is truly the slightest of wins, because there are so many great things about Ex Machina, but Her is able to do just a bit more with its subject matter than Ex Machina. So while I am declaring Her the winner, the true answer is a matter of taste. Its a question of what you find more interesting, whether you prefer movies to make you scared or make you cry, and which aspects of AI you find more fascinating. Both films could prove to be prophetic in predicting the technology of our future and deserve to become classics.

I hope you enjoyed this breakdown. If you agree or disagree with anything I said, want to point out something I forgot, or have an idea for two other movies I should pit against each other in the future, you can message me on twitter @davoswatson. Also, if you want to hear more on Ex Machina, we just recorded an episode about it that will be airing soon on my screenplay analysis podcast Interior Analysis. Thanks for reading.

If you love Ex Machina, Her, and PTN Concept Clash, share this article with a friend. Tune in daily to Popcorn Talk Network and our sister network AfterbuzzTV for articles, aftershows, and all the latest news on the world of entertainment.

Credit: Her

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