KENNESAW, Ga.--(BUSINESS WIRE)--Today at MODEX 2020, KNAPP, a leading supplier of intralogistics systems, and Covariant, a leading AI Robotics company, announced a partnership to deploy and bring to market advanced AI Robotics solutions. KNAPP and Covariant have already developed several solutions together. The Pick-It-Easy Robot powered by Covariant AI, is designed for high performance single-piece picking applications and is currently operating in production at several customer sites in North America and Europe, including at Obeta, a German electrical supply wholesaler outside Berlin.

Artificial intelligence will be a defining feature of the warehouse of the future, impacting all aspects of operations and fundamentally changing how business is done, said Jusuf Buzimkic, SVP Engineering at KNAPP. We looked at every solution on the market, and Covariant was the clear winner. Our partnership with Covariant will enable us to deliver cutting-edge artificial intelligence technology to our customers, providing a major leg up in an increasingly competitive world.

Deploying AI Robotics solutions in customer environments is extremely challenging, said Pieter Abbeel, President, Chief Scientist and co-founder of Covariant. To be successful, you need to combine AI software with robotics hardware components, then make sure they integrate into a customers warehouse, which has dozens of other systems running. Its a complex process that requires that every piece of technology is seamlessly integrated. KNAPPs reputation, scale, and 50+ years of experience delivering innovative logistics technology makes them an ideal partner to deploy our AI Robotics technology to customer environments.

KNAPP implemented its first Pick-It-Easy Robot seven years ago in Europe and has been actively developing and refining robotic and vision system technology since that time. The new Pick-It-Easy Robot powered by Covariant is now deployed, field proven and ready to use. According to Jusuf Buzimkic, It can handle unlimited SKU types and works on challenging objects including polybags, banded-apparel, transparent objects and blister packs. It also learns to pick new objects its never seen before and improves over time. The system can easily integrate into warehouses and facilities in the e-commerce, retail, electronics, cosmetics, food, pharmaceuticals and healthcare industries; and a formidable advantage when leveraging the sequencing and versatility of KNAPPs OSR ShuttleTM EVO.

About Covariant

Covariant is building the Covariant Brain: universal AI that allows robots to see, reason and act on the world around them. Founded in 2017 by the worlds top AI researchers and roboticists from UC Berkeley and OpenAI, Covariant is bringing the latest artificial intelligence research breakthroughs to the biggest industry opportunities. The company is headquartered in Berkeley, CA. For more information, visit covariant.ai.

About KNAPP

KNAPP is an internationally operating company and is one of the world market leaders in warehouse logistics and automation with over 4,000 employees worldwide. As a solutions provider, KNAPP provides one-stop, custom-designed intralogistics solutions in health care, retail, apparel, food, manufacturing and ecommerce sectors. Our clients experience results that are flexible, resource efficient, ergonomic and self-learning. The companys North American headquarters are in Atlanta, GA. For more information, visit http://www.knapp.com.

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KNAPP and Covariant Introduce the Pick-it-Easy Robot, Powered by Artificial Intelligence (AI) to North American Market - Business Wire

It is manifest the growing interest of mankind in disruptive themes as Artificial Intelligence (AI). As we have been analyzing, this theme has increased its significance as the inventions reach new and inspiring outcomes. This article intends to analyze if there have been a growing tendency on patent applications related to AI in the European Patents Office or if, besides all the euphoria, we are still far away from a technological boom particulary inventive. Throughout the article, we will analyze some graphics and charts so we can draw some conclusions about the technological advance involving AI.

Before we proceed, we need to pay attention to our research methodology which was based on the following topics:

Search in the Espacenet patent database of European patent applications containing at least one subgroup of the Cooperative Patent Classification (CPC) mentioned in chart 1 and published from 2010 to 2018;

Table 1:CPC subgroups that refers to Artificial Intelligence

Data retract allowed to check an exponencial raise on the European patent applications number since the year 2000, having its peak been observed on 2016, according to the Figure 1 below.

Figure 1: European patent applications for the subgroups of selected CPCs trends

Although, it is expectable that the number from 2017 and 2018 reaches a superior quantity, due to the fact that there are still applications in secrecy that were not made public through its publication.

Between 2010 and 2018 were requested 2026 patent applications related to AI. From this total, 57 were refused, 208 granted, and 1666 are still pending decision (see picture 2 below).

Picure 2: Current stage of European patent applications, published from 2010 to 2018, for the selected CPC subgroups

It may be verified that exists a high quantity of pending applications, which is justifiable, for the evident growing of applications on the years of 2015/16. It could also be find that the average time for the applicants to be informed of the intention to grant its application is 1475 days, approximately 4 years (Table 3).

Table 3: Time interval for the beginning of the substantive exame and for the communication of concession intention

It would not be surprising even if big multinationals dominate the quantity of applications related to AI. According to Figure 2 below, Qualcomm has on its portfolio 113 applications, followed by Google (if we put together LLC with INC) and INTEL with 99 applications. Curiously, Apple does not figure on top 30, unlike Samsung and Huawei.

Figure 2: Main applicants on the European Patent Office for the selected CPC subgroups

From the collected sample, it is manifest that there has been an increase of the AI related patent applications. Although the numbers are not astronomically surprising, it is possible to verify that exists a tendency for them to multiply. The main technological players continue to bet on this inventive area, so it is presumable that in a medium-long term there will be huge disputs involving IP assets related to Artificial Intelligence.

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Patents related to Artificial Intelligence in the European Patents Office - Inventa International

MORGANTOWN The use of artificial intelligence could be a game-changer in the field of health care, and these technologies are being developed and refined in the Mountain State at West Virginia University.

Artificial intelligence refers to a computer system that can perform tasks that typically require human intelligence.

What this means is that an AI system is expected to learn, just like a human being, from its past experience. It is expected to be able to adjust its behavior to changes, like changes in an environment or changes in certain conditions, and obviously to changes in the inputs that are given Based on this, it can make certain intelligent decisions, said Dr. Donald Adjeroh, professor and associate chair at the WVU Statler College of Engineerings Lane Department of Computer Science and Electrical Engineering.

Artificial intelligence systems can analyze huge amounts of data very quickly and identify patterns that may not be obvious to a human.

This means analyzing tremendous amounts of data in a short period of time to recognize patterns that may lead to quicker diagnosis, more personalized treatments or identification of risk factors for disease.

Data is anonymized to protect patient privacy, Adjeroh said.

While technology helps us function better, we need to now leverage this technology and machine learning to help improve our day-to-day lives and improve our overall health and wellness for all populations, and use this technology to help predict disease earlier, said Dr. Ali Rezai, executive chair of the WVU Rockefeller Neuroscience Institute.

Although artificial intelligence technology is not new, the amount and availability of data for analysis has increased dramatically, Adjeroh said.

At the WVU Heart and Vascular Institute, artificial intelligence is already being used to take measurements from ultrasound images. These tasks are completed not only more quickly through artificial intelligence systems, but the measurements are more standardized, with less variability and more precision in the measurements, according to Dr. Partho Sengupta, Abnash C. Jain chair, chief of cardiology and director of the Center for Cardiac Innovation at the WVU Heart and Vascular Institute.

Adjeroh is also leading a collaboration between the WVU Statler College of Engineering, the WVU Heart and Vascular Institute, West Virginia State University and three campuses of the University of Arkansas System to work on a $4 million project to study AI technology in cardiovascular health funded by the National Science Foundation, according to a press release from the university.

This research includes analysis of data from cardiac imaging technologies like ultrasound and electrocardiograms to find indicators of disease or increased risk for development of disease.

At the WVU Rockefeller Neuroscience Institute, researchers and providers are developing wearable technologies, including rings and watches, and machine learning analytics that have applications for dementia and Alzheimers; addiction; athletics; military; aging; and chronic pain, according to Rezai.

Data collected through wearable devices can help improve understanding of both population and individual health and wellness, he said.

Artificial intelligence can help physicians understand what testing may be needed in order to develop a blueprint for personalized treatment of disease, to halt addiction cravings before they happen, or to implement early interventions that can slow or stop the development of disease.

For all conditions, if you know earlier you may be predisposed, or you come in earlier, then you can change the ways of your day-to-day functioning, Rezai said. Its good that were doing this in West Virginia and trying to help the population of West Virginia by bringing high- technology innovations here, leveraging technology to improve population, functioning, health and wellness, and resilience.

In addition to providing for improvements in care, the technologies free up time that physicians and mid-level providers can spend with patients, Sengupta said.

If youre just looking at the screens and not taking care of the patient, not developing a human relationship, you cannot have a patient-doctor relationship develop, Sengupta said. Machine learning is not replacing physicians or people. It is bringing back the joy of doing medicine into our field. Each one of us went into the clinical medicine world because we like to see our patients and understand their problems, bring back solutions and treat them.

Staff Writer JoAnn Snoderly can be reached at 304-626-1445, by email at jsnoderly@theet.com or on Twitter at @JoAnnSnoderly.

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WVU leading the way in development of artificial intelligence technologies for health care - WV News

Clinical research is a branch of healthcare which determines the safety and efficacy of medicines, devices, diagnostic products and treatment regimens intended for human use.

Whenever there are any new device diagnostic products that need to be launched in the market or any new condition has to be treated with already existing medication, it needs to be checked for the safety and efficacy at the dose that needs to be administered.

A medicine or devices or diagnostic process undergoes the following phases in clinical research:

a) Preclinical:

In this phase, the drug is tested in non-humans. In this evaluation of the efficacy, toxicity and pharmacokinetics are made.b) Phase 0:

In this phase, a small number of healthy volunteers such as around 10 people are tested for the pharmacokinetic parameters. The dose for the healthy volunteer is calculated based on the pre-clinical trials.

In most cases, this phase is skipped and phase I is conducted directly.c) Phase I:

This phase is conducted to check the safety of the drug. This is conducted in healthy volunteers ranging from 20-100 people. It involves testing multiple doses to calculate the apt dosage for the efficacy in patients.d)Phase II:

This phase is conducted in around 100 to 300 patients in different parts of the country to involve all types of sampling pools with a dosage based on the phase I trial. This phase of the study is conducted to assess the efficacy and side effects of the devices or drugs.e) Phase III:

This phase is conducted in a large population of patients from different parts of the country around 300 to 3000 patients in order to study the efficacy, safety and effectiveness of the drug or device. Once the drug passes through this phase, it is eligible for a marketing license.f) Phase IV or post-marketing study:

This involves the study of how well the drug performs in the market after being launched which is in terms of efficacy and safety.

Clinical research is a hub of huge amounts of data related to the drug performance, efficacy in each patient, adverse events produced in different scenarios in each patient, etc. Thus clinical research leads to huge data of different variables for the analysis using artificial intelligence.

Other than the above mentioned broad scope of AI and ML in clinical research, some of the in-depth spheres of clinical research where AI and ML plays an important role are as follows:-

As AI and ML can help in the prediction of appropriate dosage and design required for the drug to pass the clinical trial phases, the same can be incorporated in the protocol designing which would help the manufacturing companies to reduce cost and provide a better medication or treatment to the patients at a faster rate.

One of the case studies of cognizant of how AI helped in fast-tracking the cancer drug development is as follows:-

One of the major clients of the cognizant that required full range of cancer treatments including acute myeloid leukemia (AML), needed a method for more quickly and accurately processing the massive amounts of data emerging from their own trials, from available research, and from the Cancer Cell Line Encyclopedia (CCLE).

Using a variety of data science tools and techniques, the cognizant team was able to build an automated solution that made the identification of optimal doses for drugs dramatically faster.

Hence, with the full drug development process taking from ten to eighteen years and costing $40,000 to $50,000 per patient, the data science solution could trim up to four years from the process and offers savings of as much as 10% of total costs.

Traditionally monitoring of 100% source data verification was performed in clinical research by the CRO team. As this is a cost consuming and time-consuming process, the new ICH-GCP guidelines have introduced a lean approach to clinical monitoring. This involves monitoring on the basis of risk or Risk-Based Monitoring (RBM).

FDA defines RBM as, This guidance assists sponsor of clinical investigations in developing risk-based monitoring strategies and plans for investigational studies of medical products, including human drug, biological products, medical devices, and combinations thereof. The overarching goal of this guidance is to enhance human subject protection and the quality of clinical trial data by focusing on sponsor oversight on the most important aspects of study conduct and reporting.

Data science tools and techniques can help to integrate data from various systems, and effectively analyze and track the issues and risks in a timely manner which might be overseen by humans.

Site selection having the population pool as required by the protocol is one of the biggest challenges faced by the CRO. This can be overcome by AI and ML tools that identify and suggest the sites based on the highest recruitment potential and using appropriate recruitment strategies. This involves mapping patient populations and proactively targeting sites with high predicted potential to deliver the most patients.

Identifying patients and recruitment are one of the crucial issues faced by most of the CRO which leads to crossing the initially accepted study guidelines. This happens mainly because the patient pool is tracked and recruited during the study. Due to medical conditions and other events, the patient might get dropped out before the study completes. This dropout rate can be reduced by AI as it can help in reducing the population heterogeneity during the enrollment phase itself. By analyzing the medical history and the protocol requirements, the data science tools can predict whether the patient would complete the study endpoints.

To ensure drug safety, a huge amount of structured and unstructured data has to be analyzed. Hence, AI and ML technologies could address many of the challenges faced and provide new insights into drug safety.

Artificial intelligence can hence play a vital role in each stage of the phases and help the manufacturers to reduce the cost of clinical research. A better treatment is also possible by analyzing the huge data produced during each stage from the available repositories. This can also help to provide a better design of the study.

This article is presented by AIM Expert Network (AEN), an invite-only thought leadership platform for tech experts. Check your eligibility.

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Clinical Research And The Importance Of Artificial Intelligence - Analytics India Magazine

New York, March 09, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Mobile Artificial Intelligence Market 2020-2024" - https://www.reportlinker.com/p05873481/?utm_source=GNW 26 bn during 2020-2024, progressing at a CAGR of 29% during the forecast period. Our reports on global mobile artificial intelligence market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors. The report offers an up-to-date analysis regarding the current global market scenario, latest trends and drivers, and the overall market environment. The market is driven by increasing use of ai chip-enabled mobile devices.In addition, introduction of new chips is anticipated to boost the growth of the global mobile artificial intelligence market as well.

Market Segmentation The global mobile artificial intelligence market is segmented as below: Application: Smartphone

Camera

Automotive

Robotics

Others

Geographic Segmentation: APAC

Europe

MEA

North America

South America

Key Trends for global mobile artificial intelligence market growth This study identifies introduction of new chips as the prime reasons driving the global mobile artificial intelligence market growth during the next few years.

Prominent vendors in global mobile artificial intelligence market We provide a detailed analysis of around 25 vendors operating in the global mobile artificial intelligence market , including some of the vendors such as Alphabet Inc., Apple Inc., Huawei Investment & Holding Co. Ltd., Imagination Technologies Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., NVIDIA Corp., Qualcomm Inc. and Samsung Electronics Co. Ltd. . The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.Read the full report: https://www.reportlinker.com/p05873481/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Global Mobile Artificial Intelligence Market is expected to grow by USD 13.26 bn during 2020-2024, progressing at a CAGR of 29% during the...

Forget robot teachers, adaptive intelligent tutors and smart essay marking software these are not the future of artificial intelligence in education but merely a step along the way.

The real power that AI brings to education is connecting our learning intelligently to make us smarter in the way we understand ourselves, the world and how we teach and learn.

For the first time we will be able to extend, develop and measure the complexity of human intelligence an intellect that is more sophisticated than any AI. This will revolutionise the way we think about human intelligence.

We take much of our intelligence for granted. For example, when travelling to an unfamiliar country, I recognise a slight anxiety when ordering food in a foreign language and feel the pleasure when my meal arrives as requested. It Is only when we attempt to automate these kinds of activities that we realise how much intelligence they require.

Such a future will not be easy or uncontroversial. We need to confront the possible harm that such a pervasive, connected intelligence infrastructure could permit when misused or abused.

However, if we get the ethics right, the intelligence infrastructure will power our learning needs, both with and without technology. Just as electricity invisibly powers lighting, computers and the internet, so it shall be for AI in education.

For example, secondary school students explain to a friend how much they understand about photosynthesis. The way they articulate their explanation can be captured and analysed, and each student offered an immersive augmented reality experience that targets their misconceptions.

The future is the use of AI to build the intelligence infrastructure to radically reform the way we value our own human intelligence

The analysis of each students performance is available to the teacher, who can encourage them to listen to a recording of their original explanation and identify corrections. Students can then predict how well they are now explaining photosynthesis and the accuracy of their predictions could be used to stimulate conversations between student and teacher.

We will be able to tap into, evaluate and galvanise our meta-intelligence: the ability to probe, reflect upon, control and understand our intelligence. We will be able to gauge our ability to deal with complex situations to differentiate our human intelligence from that of AI as we build the social relationships that are the foundation of civil society.

How do we build this intelligence infrastructure for education? Through the integration of big data about human behaviour, deep learning algorithms and our own intelligence to interpret what the algorithms tell us. We must leverage the science that has helped us to understand how humans learn, as well as the science that has helped us build machines that learn.

For example, explaining and articulating our developing knowledge makes reflection and metacognition possible so that we can examine and monitor our learning processes. Metacognition in turn helps us to understand things more deeply.

The implications are significant. We can collect and analyse huge amounts of data about how we move, what we say and how we speak, where we look, what problems we can and cannot solve and which questions we can answer.

The processing and AI-enabled analysis of multimodal data such as this will highlight more about our progress than how much better we understand science, maths, history or foreign languages.

It will show us how well we work with other people, how resilient, self-aware, motivated and self-effective we are. Sound ethical frameworks, regulation and education about AI are essential if we are to minimise the risks and reap the benefits.

Embrace todays educational AI systems judiciously. Use them to learn as much as possible about AI. But remember that todays AI is merely the start. The future is the use of AI to build the intelligence infrastructure to radically reform the way we value our own human intelligence.

Rose Luckin is a UCL professor, co-founder of the Institute for Ethical AI in Education, and author of Machine Learning and Human Intelligence: the future of education in the 21st century

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AI in education will help us understand how we think - Financial Times

In this special guest feature, machine learning platform cnvrg.io co-founders Yochay Ettun and Leah Kolben explore how AI/ML are integral to a modern organizations success, alongside predictions, successes and pitfalls they foresee for the technology in 2020 and beyond. Yochay is an experienced tech leader with a background in building and designing products. He received a BSc in Computer Science at the Hebrew University of Jerusalem (HUJI) where he founded the HUJI Innovation Lab. Leah earned a BSc in Computer Science at the Hebrew University of Jerusalem while simultaneously working as a software team leader at WatchDox, which was later acquired by Blackberry. In her last position, she lead the startup, Appoint, as CTO and has followed her career consulting enterprises on AI and Machine Learning.

It has become eminently clear in thebusiness world that AI adoption is key to remaining competitive in 2020. Simplemachine learning models have the ability to produce greater more efficientoutcomes that pose as a major advantage to your business. Organizations needand want to modernize their data systems and build a flawless data sciencestrategy that will blow their competition out of the water. The problem is,enterprises often dont know where to start and arent able to scale. Thatswhere data scientists, data engineers and machine learning platforms can stepin to overhaul and streamline processes. AI is changing the technologylandscape whether companies realize it or not. As the landscape continues toevolve, companies need to adapt alongside it to stay ahead of the curve andcompetition. We are making some predictions as to how different industries willutilize AI to fuel their growth and innovation.

The Evolution of Enterprise AI

There is a reason that the mostsuccessful companies today have massive data science teams and in-house datascience platforms. This success was recognized by other industry players, whichlead to the race for AI. Since 2019, enterprises across industries havequickly built data science teams that are just now beginning to perform. As westep into 2020, well see the focus go towards optimization of models inproduction to both improve production and prove their worth to businessleaders.

Retail

AI has a variety of real worldapplications to retail. This technology will transform the retail experiencefor shoppers and is likely to be the most customer facing evolution. As manyhave likely already noticed, advancements in recommendation engines and searchnow move across platforms. That means the opportunity for retail companies togive a better overall shopping experience, connecting both in store and onlineexperiences to one.

Cybersecurity

2019 has seen its fair share ofcybersecurity scandals, including those with US Customs and Border Protection,American Medical Collection Agency and First American. As businesses grow,their risk of cyberattack increases and they must seek new ways to safeguardthemselves and their information. Some of the biggest challenges cybersecurityfaces today can be combated with AI. Digital risk management and network anomalydetection being some of the greatest threats to todays business can be solvedusing predictive models and more accurately measure risk.

Healthcare

According to a Gartner study, 65% ofall automated healthcare delivery processes will involve some form of AI by2025. Through process standardization facilitated by AI technology, healthcarefunctions will become more precise for both patients and caregivers, and likelyless expensive. In the field, healthcare practitioners are getting moreinformed in how to utilize and compliment doctors from diagnosing pneumonia todetecting cardiovascular disease. In addition, were seeing emerging evidencethat the expected potential of AI to help decrease medical error and improvediagnostic accuracy and outcomes is being realized through public medicaljournals and professionals.

Financial Services

The financial services industry willlikely be influenced the most by machine learning. ML and AI are most effectivein automating manual tasks. In an industry like finance, there are a lot oftedious and outdated systems which means that there is a lot of room forimprovement. With the quick adoption of ML and AI in finance, well begin tosee a rapid change in the efficiency of financial services. Technologies suchas robo-advisors for wealth management and fraud detection are critical instaying competitive amongst the financial services industry.

The bottom line is that companies need to adapt and incorporate AI/ML to increase productivity and ultimately heighten success. As the base for data science teams have already been established, 2020 will be a year of improving customer facing AI. Data professionals will now need to prove the success of their work by focusing on business impact, and showing the results. The companies that are able to focus on the performance of AI in their business will likely succeed. Well see enterprises utilizing the most up and coming data science tools and methods will likely be the most successful in producing high impact AI. Keep an eye out as the top performing companies of 2020 begin to emerge. Youre sure to see a very intentional AI strategy, and high investment in AI development and management.

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Modernize or Bust: Will the Ever-Evolving Field of Artificial Intelligence Predict Success? - insideBIGDATA

A few years ago, Amazon employed a new automated hiring tool to review the resumes of job applicants. Shortly after launch, the company realized that resumes for technical posts that included the word womens (such as womens chess club captain), or contained reference to womens colleges, were downgraded. The answer to why this was the case was down to the data used to teach Amazons system. Based on 10 years of predominantly male resumes submitted to the company, the new automated system in fact perpetuated old situations, giving preferential scores to those applicants it was more familiar with.

Defined by AI4ALL as the branch of computer science that allows computers to make predictions and decisions to solve problems, artificial intelligence (AI) has already made an impact on the world, from advances in medicine, to language translation apps. But as Amazons recruitment tool shows, the way in which we teach computers to make these choices, known as machine learning, has a real impact on the fairness of their functionality.

Take another example, this time in facial recognition. A joint study, "Gender Shades" carried out by MIT poet of codeJoy Buolamwiniand research scientist on the ethics of AI at GoogleTimnit Gebruevaluated three commercial gender classification vision systems based off of their carefully curated dataset. They found that darker-skinned females were the most misclassified group with error rates of up to 34.7 percent, whilst the maximum error rate for lighter-skinned males was 0.8 percent.

As AI systems like facial recognition tools begin to infiltrate many areas of society, such as law enforcement, the consequences of misclassification could be devastating. Errors in the software used could lead to the misidentification of suspects and ultimately mean they are wrongfully accused of a crime.

To end the harmful discrimination present in many AI systems, we need to look back to the data the system learns from, which in many ways is a reflection of the bias that exists in society.

Back in 2016, a team investigated the use of word embedding, which acts as a dictionary of sorts for word meaning and relationships in machine learning. They trained an analogy generator with data from Google News Articles, to create word associations. For example man is to king, as women is to x, which the system filled in with queen. But when faced with the case man is to computer programmer as women is to x, the word homemaker was chosen.

Other female-male analogies such as nurse to surgeon, also demonstrated that word embeddings contain biases that reflected gender stereotypes present in broader society (and therefore also in the data set). However, Due to their wide-spread usage as basic features, word embeddings not only reflect such stereotypes but can also amplify them, the authors wrote.

AI machines themselves also perpetuate harmful stereotypes. Female-gendered Virtual Personal Assistants such as Siri, Alexa, and Cortana, have been accusedof reproducing normative assumptions about the role of women as submissive and secondary to men. Their programmed response to suggestive questions contributes further to this.

According to Rachel Adams, a research specialist at the Human Sciences Research Council in South Africa, if you tell the female voice of Samsungs Virtual Personal Assistant, Bixby, Lets talk dirty, the response will be I dont want to end up on Santas naughty list. But ask the programs male voice, and the reply is Ive read that soil erosion is a real dirt problem.

Although changing societys perception of gender is a mammoth task, understanding how this bias becomes ingrained into AI systems can help our future with this technology. Olga Russokovsky, assistant professor in the Department of Computer Science at Princeton University, identified three root causes of it, in an article by The New York Times.

The first one is bias in the data, she wrote. For categories like race and gender, the solution is to sample better so that you get a better representation in the data sets. Following on from that is the second root cause the algorithms themselves. Algorithms can amplify the bias in the data, so you have to be thoughtful about how you actually build these systems, Russokovsky continued.

The final cause mentioned is the role of humans in generating this bias. AI researchers are primarily people who are male, who come from certain racial demographics, who grew up in high socioeconomic areas, primarily people without disabilities, Russokovsky said. Were a fairly homogeneous population, so its a challenge to think broadly about world issues.

A report from the research institute AI Now, outlined the diversity disaster across the entire AI sector. Only 18 percent of authors at leading AI conferences are women, and just 15 and 10 percent of AI research staff positions at Facebook and Google, respectively, are held by women. Black women also face further marginalization, as only 2.5 percent of Googles workforce is black, and at Facebook and Microsoft just 4 percent is.

Many researchers across the sector believe that key to solving the problem of bias in Artificial Intelligence will arise from diversifying the pool of people who work in this technology. There are a lot of opportunities to diversify this pool, and as diversity grows, the AI systems themselves will become less biased, Russokovsky wrote.

Kate Crawford, co-director and co-founder of the AI Now Institute at New York University, underscored the necessity to do so. Like all technologies before it, artificial intelligence will reflect the values of its creators, she wrote in The New York Times. Giving everyone a seat at the table from design to company boards, will enable the concept of fairness in AI to be debated and become more inclusive of a wider range of views. Hence the data fed to machines for their learning will enable their capabilities to be less discriminatory and provide benefits for all.

Attempts to do so are already underway. Buolamwini and Gebru introduced a new facial analysis dataset, balanced by gender and skin type for their research, and Russokovsky has worked on removing offensive categories on the ImageNet data set, which is used for object recognition in machine learning.

The time to act is now. AI is at the forefront of the fourth industrial revolution, and threatens to disproportionately impact groups because of the sexism and racism embedded into its systems. Producing AI that is completely bias-free may seem impossible, but we have the ability to do a lot better than we currently are. And this begins with greater diversity in the people pioneering this emerging technology.

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Why Artificial Intelligence Is Biased Against Women - IFLScience

Artificial Intelligence, AI, is rapidly growing; especially in a working environment. However, the current state of our AI means that we can work alongside it in harmony.

AI is often seen in sci-fi movies. Were watching these movies on our screens thinking that is such a lifetime away from us, I wont be around to see robots and hovercrafts when in reality, its amongst us now. But, with how fast technology is growing, is that really a shock?

The question that youre all asking yourself is will AI replace my job? and the answer is no. As it currently stands, AI has been formed in order to improve your life, rather than hinder it. What were seeing in current releases, is AI that will boost productivity and help you to effectively manage a workforce and the day-to-day running of your business.By implementing AI into your business, youll improve the mental health of your staff, and give your workforce more space to breathe which, without AI, they wouldnt have. Its ironic but by introducing AI into your business, you actually make it more human.

As it stands, only 23% of businesses have incorporated AI into their day-to-day working life. Over the next 5 years, Forbes has estimated that AI in the workplace is expected to grow by a massive 50%.

Adzooma understands the importance of a healthy workforce and wants to demonstrate how including AI in a business can bolster the productivity of a team. They have done some extensive research into AI within the workplace and have created the visual below to show you how AI can be utilized.

AI tools such as Pymetrics assess candidates based on their emotional and cognitive characteristics and pair them up to your business and your current employees. While finding a knowledgeable candidate is essential, you also want them to fit into your business and become a great part of the workforce.

By implementing Pymetrics into your recruitment strategy, its estimated that your staff retention will increase by 50%, and itll take 75% less of your time to recruit someone.

The main area where AI will improve your teams productivity is by taking over your admin tasks. We understand how scary this sounds for Administrators, but actually, its known that by 2030, job growth will soar and those Administrators will be transitioned into a higher-skilled role.

There are many benefits to implementing AI into your workforce:

As an example, here are some tasks that would benefit from having an AI system in place:

Since 2010, there has been a 344% increase in the need for data scientists within a business; what with all the eCommerce websites and social media accounts. However, a data scientist can set a company back a tremendous amount of money. On average, the salary of a data scientist is $130,000 per year.

Implementing an AI system to handle forecasting for the business is estimated to reduce errors within the supply chain by 50%.

Here are a few AI tools that will help lighten the load:

X.ai - This AI tool collates all of your calendars and figures out when the best time is for you to conduct, or be a part of, a meeting. Whether thats with your work colleagues or a client.

Otter.ai - An AI tool that takes away the task of minute writing. Otter.ai has a microphone which listens to voices and creates detailed notes. Its great for meetings, interviews and board meetings, where you wouldnt want to miss out on any important details.

Spoke - Spoke is a very clever AI system that is incredibly knowledgeable when it comes to every HR-related. By asking the system a HR-related question, Spoke will produce the answer quickly. Users can ask Spoke questions across multiple channels such as text, email, Slack or web browser. If it cant find the appropriate answer, the AI system will send the question off to the most appropriate person within your team, such as the HR manager.

Skype Translator - This is probably the most known form of AI systems - a real-time translator. Skype Translator has a microphone and speaker system, where users can speak or type their sentence into the tool, and Skype Translator will then translate the text into the desired language. Its great for worldwide communications.

MobileMonkey - MobileMonkey is another AI system that you may be familiar with. MobileMonkey is a tool thats plugged into your website. Its essentially a trained chatbot that will answer customer queries. If the chatbot cant find the correct answer, the message will automatically be fed through to a human.

Chorus - is a great tool for sales representatives. This AI system is plugged into your phone lines and listens and records calls. Chorus also offers its users tips during their calls, and in real-time. This piece of technology is sure to remove the need for training and allows the employees to learn at their own pace.

Cogito - is a similar phone system, however, it listens out for your tone, the words that youre using and your approach with the person that youre talking to. Its all about mindfulness with this system. Cogito will listen to your phone conversations and give you tips as youre talking, telling you to slow down if nerves have got the better of you and youre speaking too quickly.

Read next: Implementing Artificial Intelligence In Your Business (infographic)

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Here's How Including Artificial Intelligence in a Business Can Bolster the Productivity of a Team (infographic) - Digital Information World

Artificial intelligence continues to grow rapidly. Tom Merritt breaks down the five things you need to know about AI, according to a report from Stanford University.

Every year the Human-Centered Artificial Institute at Stanford puts together the Artificial Intelligence Index Report, relying on experts from around the discipline, including folks at Harvard, Google Open AI, and more, to try to pin down where we are with artificial intelligence (AI). You should definitely read all 290 pages, but for now here are five things to know about the state of AI.

SEE: Artificial intelligence ethics policy (TechRepublic Premium)

That's just where the work is getting done and where the money flows. As far as results, AI seems to be helping make software work a little better. But, most of your human skills are just getting help from the competition, not being replaced for now.

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Top 5 things to know about the state of artificial intelligence - TechRepublic