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However, according to recent developments , Ai business school microsoft AI editors are already showing signs of inaccuracy. The AI reportedly used a photo of Leigh-Anne Pinnock on a story about her fellow bandmate Jade Thirlwall’s experiences with racism.


Artificial Intelligence ai business school microsoft

A third, particularly significant application of AI is in combating the massive global problem of climate change. Sometimes, a breakthrough may take as long as a decade, but machine learning can accelerate it in much the same way as drug development.

How Cortana help and enhance Business Intelligence?

How China’s AI experts can beat Google and Microsoft by 2030

Over 40 per cent of the top AI-related academic papers published worldwide in 2015 had at least one or more Chinese researchers. Chinese AI-based patent applications grew 186 per cent between 2010 and 2014, a huge increase from the previous five-year period.

These favourable policies have inspired innovations from both smaller firms and internet giants in China.

Baidu, for example, has developed a cutting-edge neural-network-based machine translation system that has achieved a speech recognition accuracy higher than that of humans. It has also launched an open-source platform for autonomous driving solutions, namely Project Apollo, to speed up the -development of self-driving vehicles.

AI research in academia has spread from being a focus at a few elite universities to those across China. Chinese academics have built a robust research community, which allows them to tap AI resources in both Chinese and English. Large numbers of Chinese science and engineering graduates are now flocking to the industry.
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China is more than capable of becoming a leader in AI.

As of now, groundbreaking research is still mostly being done in the West, where the focus is on the science and infrastructure behind AI technology. Chinese academics, on the other hand, tend to research new applications of pre-existing technology.

Chinese companies are very good at launching new products and features quickly to the market, as they are well-versed in tapping newly identified opportunities. In the same vein as academia, Chinese companies primarily rely on new applications of pre-existing technologies rather than creating new ones.

China needs a fundamental change to truly become a leader in AI. There needs to be a greater emphasis on developing the science behind the technology rather than emphasising new applications.

To achieve the goal of becoming a global AI leader by 2030, China will need to take at least two essential steps. First, it should redraft its incentives policies to motivate local companies and academics to conduct research on new AI technologies.
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Andrew Ng, a leading American AI researcher, once said that AI would become the “new electricity” – transforming not just one industry, but all of them. AI can be and, in fact, is already being utilised across different sectors, creating unparalleled opportunities to “activate new businesses”.

Another failed attempt of AI replacing humans: Microsoft AI Editor already shows signs of inaccuracies

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Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors

In sum, our paper is a first step toward comprehensively conceptualizing and operationalizing organizational AI readiness. As such, we provide additional empirical groundwork for theorizing on technology adoption and readiness in general. Further, our AI readiness factors serve as the necessary foundation for purposeful decisions in the entire AI readiness and adoption process. Hence, we extend the body of descriptive knowledge on AI readiness and provide a first building block for prescriptive knowledge to guide organizations toward successful AI adoption.

This paper’s theoretical foundation is twofold: First, with AI being a technological innovation, the literature on innovation adoption provides the scaffold of our research. Second, research on organizational readiness for change emphasizes readiness as a necessary precursor for organizational change, such as AI adoption.

In sum, research so far provides fruitful theoretical groundwork but cannot provide relevant organizational AI readiness factors. Drawing on this previous work, we seek to provide a sound set of organizational AI readiness factors and corresponding indicators for AI readiness assessments.

The AI readiness categories and factors describe the organizational chassis for developing AI readiness. Besides, our explorative interviews provide insights that help to understand the hurdles for successful AI adoption. Consequently, our findings are a necessary precursor to indicate how organizations can explore AI’s potentials. However, owing to AI’s characteristics as GPT, AI adoption differentiates from previously discussed technology adoption. Organizations define and pursue individual AI adoption purposes that describe how they seek to accrue value from the wide range of AI’s potential application scenarios. Thus, AI adoption can have different facets depending on the distinct adoption purpose. The AI adoption purpose can span from single use-cases to self-contained AI-driven business models.

Building on prototypes, experiments, and preceding projects as steps of AI adoption, companies may then push their AI adoption purpose to shift over time. For example, they may opt to start with internal applications before involving the customer interface.

In the following, we will position the results of our exploratory interview study within the existing adoption and readiness literature. Thereby, we conceptualize AI readiness as a valuable addition to the scholarly knowledge base and a necessary foundation for successful AI adoption. Further, we discuss the interdependencies between AI readiness and AI adoption as intertwined concepts.
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First, AI readiness comprises 18 readiness factors along five categories that provide the organizational chassis for developing AI readiness. Second, beyond the specific factors, AI readiness entails the understanding of purposeful AI adoption.

Regarding practical implications, our paper grants insights into opportunities and challenges for AI adoption. The AI readiness factors provide comprehensive guidance to decision-makers on relevant managerial action fields. Based on an AI readiness assessment, decision-makers may reflect and adapt the factors to specific organizational needs. Setting and developing adequate AI readiness target levels is compulsory in order to derive actionable measures for successful AI adoption.

We combined insights from interviews with 25 AI experts with findings from scientific and practitioner literature to compile 18 AI readiness factors and 58 illustrative indicators in five categories. Further, we discussed that organizations must continuously assess and develop their AI readiness in the AI adoption process and described relevant aspects to consider. This includes AI’s nature as a GPT, the context- and purpose-specifics, and the mutually reinforcing interplay of AI readiness and AI adoption.

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6 Resources To Help Improve Your Data Science Skills

Jobs in the field of data science are becoming increasingly popular. However, they require you to have a particular skillset that might not be built into the curriculum of your graduate program.

Many of the platforms below have implemented “digital badging” for their courses. Digital badges are certifications that you have completed a course or training module. LinkedIn and Twitter allow you to embed these badges into your profile, and you can also include them on your CV. Host platforms may maintain directories of all users who have been awarded badges so that companies can confirm your credentials.

Coursera offers many courses and certificates in data science including deep learning, data visualization, biostatistics in public health, and more. While courses are hosted by Coursera, they are developed by universities and companies such as IBM, Google Cloud Platform, and Johns Hopkins.

DataCamp offers courses and “skill tracks” designed to help sharpen your data science skills in R, Python, SQL, git, shell, spreadsheets, theory, Scala, and Tableau. The free subscription to DataCamp includes the first chapters in all courses, 100+ coding challenges and 7 projects.

The Data Incubator Data Science Fellowship Program is an 8-week data science bootcamp specifically for PhD and Master’s students and is free for admitted fellows. There are weekly mini projects as well as a capstone project in which you build a web application.

Insight’s program is a 7-week post-doctoral bootcamp that is tuition-free and provides need-based scholarships to help cover living expenses. During the bootcamp, you will undergo self-directed, project-based learning under the guidance of top industry data scientists. The program culminates in the completion of a capstone project that you present during job interviews with mentor companies.

AI Transparency & Explainability.

Pamela developed a proprietary Credit Derivative trading system for Deutsche Bank and a quantitative market risk VaR system for Nomura. Pamela is the CEO of Jasper Consulting Inc, a consulting firm through which she provides advisory and audit services for AI Ethics governance.

Renée Cummings is an AI ethicist, data activist, criminologist, criminal psychologist and therapeutic jurisprudence specialist. She’s also the historic first Data Activist in Residence, at the University of Virginia’s School of Data Science and a community scholar at Columbia University.

Renée also specializes in AI for social good, justice-oriented AI design, social justice in AI policy and governance, and using AI to save lives.

Nikita Lukianets, a Founder of the Open Ethics initiative that fosters the inclusive dialogue between experts and citizens to design systems where humans and AI successfully work together.

Nikita Lukianets has more than 10 years of experience in Human-Computer Interaction and has partnered with multiple organizations to help them build human-centered interfaces.

While human-level artificial intelligence has not been achieved yet, the implications that arise from the integration of AI into human societies are visible in the narrow fields already.

Open Ethics is about making clear how autonomous technologies make their decisions.

AI for social good: unlocking the opportunity for positive impact

Results from several recent studies hint at the potential benefits of using AI for social good. Amnesty International and ElementAI demonstrated how AI can be used to help trained human moderators with identifying and quantifying online abuse against women on Twitter19.

This wealth of projects, sometimes isolated, has led to several meta-initiatives. For example, the Oxford Initiative on AIxSDGs26, launched in September 2019, is a curated database of AI projects addressing SDGs that indexes close to 100 projects. Once publicly accessible, it should support a formal study of such projects’ characteristics, success factors, geographical repartition, gaps, and collaborations.

Related principles are encoded in the Montreal Declaration for Responsible AI35 and the Toronto Declaration36. The European Commission states that AI needs to be lawful, ethical and robust, to avoid causing unintended harm.

A recent UN report40 details how over 30 of its agencies and bodies are working towards integrating AI within their initiatives. According to the report, AI4SG projects need to be approached as a collaborative effort, bringing communities together to carefully assess the complexities of designing AI systems for SDGs. These initiatives should aim to involve NGOs, local authorities, businesses, the academic community, as well as the communities which these efforts support.

This process involved setting up focused working groups around key topics and repeatedly coming together to disseminate the results, obtain feedback and discuss within the wider group.

The results of the study have revealed worrying patterns of online abuse, estimating 1.1 million toxic tweets being sent to women in the study across the year, black women being 84% more likely than white women to experience abuse on the platform. The core of the analysis was based on using machine learning approaches to pre-filter the data, followed by applying computational statistics methods. The team has additionally evaluated the feasibility of using a fine-tuned deep learning model for automatic detection of abusive tweets61.

We encourage AI experts to actively seek out opportunities for delivering positive social impact. Ethics and inclusivity should be central to AI systems and application-domain experts should inform their design.

Top 8 open source AI technologies in machine learning

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Artificial Intelligence Holds Enticing Promise, Needs Framework, Say OECD, Microsoft, IEEE

As artificial intelligence technology spreads its wings, governance issues are emerging, as are international discussions, including a range of activities planned for 2018.

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Masahiko Tominaga, vice-minister for policy coordination at the MIC, presented artificial intelligence with the potential for solving various problems and with “enormous benefits” to be expected.

Under the leadership of Japan, the OECD started an international, multi-stakeholder dialogue on AI, she explained, taking stock of who is doing what and what differences are emerging.

According to an OECD spokesperson, in 2018, the OECD is planning to produce and analytical/policy report on AI building on the OECD conference held last October.

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items.

The SPIRIT-AI and CONSORT-AI extensions were simultaneously developed for clinical trial protocols and trial reports. Both guidelines were developed in accordance with the EQUATOR Network’s methodological framework.29 The SPIRIT-AI and CONSORT-AI steering group, consisting of 15 international experts, was formed to oversee the conduct and methodology of the study.

Class activation map—Class activation maps are particularly relevant to image classification AI interventions.

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The title should be understandable by a wide audience; therefore, a broader umbrella term such as “artificial intelligence” or “machine learning” is encouraged. More precise terms should be used in the abstract, rather than the title, unless broadly recognised as being a form of artificial intelligence/machine learning.

Explanation: The intended use of the AI intervention should be made clear in the protocol’s title and/or abstract. This should describe the purpose of the AI intervention and the disease context.1936 Some AI interventions may have multiple intended uses, or the intended use may evolve over time.

Explanation: The measured performance of any AI system may be critically dependent on the nature and quality of the input data.40 The procedure for how input data will be handled—including data acquisition, selection, and pre-processing before analysis by the AI system—should be provided. Completeness and transparency of this process is integral to feasibility assessment and to future replication of the intervention beyond the clinical trial.

Poor quality or unavailable data can also affect non-AI interventions. For example, suboptimal quality of a scan could impact a radiologist’s ability to interpret it and make a diagnosis. It is therefore important that this information is reported equally for the control intervention, where relevant.

Explanation: A description of the human-AI interface and the requirements for successful interaction when handling input data should be described.21 A description of any planned user training and instructions for how users will handle the input data provides transparency and replicability of trial procedures. Poor clarity on the human-AI interface may lead to a lack of a standard approach and carry ethical implications, particularly in the event of harm.

Explanation: Reporting performance errors and failure case analysis is especially important for AI interventions. AI systems can make errors which may be hard to foresee but which, if allowed to be deployed at scale, could have catastrophic consequences.45 Therefore, identifying cases of error and defining risk mitigation strategies are important for informing when the intervention can be safely implemented and for which populations. The protocol should specify whether there are any plans to analyse performance errors.

The SPIRIT-AI extension provides international consensus-based guidance on AI-specific information that should be reported in clinical trial protocols alongside SPIRIT 2013 and other relevant SPIRIT extensions.446 It comprises 15 items: three elaborations to the existing SPIRIT 2013 guidance in the context of AI trials and 12 new extensions.

This study is set in the current context of AI in health; therefore, several limitations should be noted. First, at the time of SPIRIT-AI development there were only seven published trials and no published trial protocols in the field of AI for healthcare. Thus, the discussion and decisions made during the development of SPIRIT-AI are not always supported by existing real-world examples. As the science and study of AI evolves, we welcome collaboration with investigators to co-evolve these reporting standards to ensure their continued relevance. Third, the initial candidate items list was generated by a relatively small group of experts consisting of steering group members and additional international experts.

Currently, most applications of AI/ML involve disease detection, diagnosis, and triage, and this is likely to have influenced the nature and prioritisation of items within SPIRIT-AI. As wider applications that utilise “AI as therapy” emerge, it will be important to re-evaluate SPIRIT-AI in the light of such studies. Additionally, advances in computational techniques and the ability to integrate them into clinical workflows will bring new opportunities for innovation that benefits patients.

Not all data is created equal: the promise and peril of algorithms for inclusion at work

In 2016, Microsoft unveiled its first AI chatbot, Tay, developed to interact and converse with users in real-time on Twitter and engage Millennials.

On March 23, Tay took its first steps on Twitter, posting mostly innocuous messages and jokes, like “humans are super cool”.

First, machine learning algorithms are driven by the data they are fed. Consequently, their outcomes are only as unbiased as the data they are based on. Second, AI and machine learning models can learn and adapt over time as new data is incorporated.

The story of Tay also shows these algorithms can learn and adapt based on the data they are presented with. By implementing improved processes now, firms can reduce bias in datasets and set AI on a positive path of supporting inclusion, rather than perpetuating existing discrimination.

Teresa Almeida – LSE The Inclusion Initiative Teresa Almeida is a research officer at LSE’s The Inclusion Initiative. She has run B2B campaigns across some of the largest enterprise businesses in the area of information and communication technology. Teresa is fascinated with the world of behavioural science and decision-making, with an emphasis on applying insight to deliver tangible results.

Indeed, a plethora of sophisticated and successful tools support companies on this path. This roadmap is the synthesis of the multiple research on the subject and tries to draw the best from the practices of innovative companies. First, Unification of data sources, then Process optimisation and finally Metamorphosis. Unification – Optimisation – Metamorphosis The roadmap shows how learning algorithms should be oriented to transform the business in small incremental steps that together produce powerful change.

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