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Grace' is a lifelike robot nurse, built withartificial intelligence to bring emotional care for patients duringthe pandemic and make them feel comfortable and at ease.

Artificial intelligence (AI), the self-didactic technology whichdetects patterns from historical data, is pervading all walks oflife, be it healthcare or the financial services industry.

The High-Level Expert Group on AI, tasked by the EuropeanCommission to draft AI ethics guidelines, defined AI assystems that display intelligent behaviour byanalysing their environment and taking actions with somedegree of autonomy to achieve specificgoals''.

In the finance world, AI has evolved substantially over therecent decades and its utility ranges from the performance datamonitoring, establishing creditworthiness and credit scoring, aswell as in combatting cybercrime and money laundering. However, theexponential use does not come without a fair amount of risksattached, in particular in machine learning applications whererisks of data bias can lead to erroneous results being generated bythe AI due to statistical errors or interference during the machinelearning process.

The paucity in AI regulation and the multiplicity in AIpractices led the European Commission to focus on this technologyin its Digital Finance Package, launched at the end of 2020 toensure that the EU financial sector remains competitive whilecatering for digital financial resilience and consumerprotection.

Towards the end of last year, the European Central Bankpublished its opinion welcoming the Artificial Intelligence Act.While noting the increased importance of AI-enabled innovation inthe banking sector, given the cross-border nature of suchtechnology, the supranational body held that the ArtificialIntelligence Act should be without prejudice to the prudentialregulatory framework to which credit institutions are subject.

The ECB acknowledged that the proposal cross-refers to theobligations under the Capital Requirements Directive (2013/36 orCRD V') including risk management and governanceobligations to ensure consistency. Yet the ECB sought clarificationon internal governance and outsourcing by banks who are users ofhigh-risk AI systems.

Raising its concerns as to its role under the new ArtificialIntelligence Act, the ECB reiterated that its powers derive fromarticle 127(6) of the Treaty on the Functioning of the EuropeanUnion (TFEU) and the Single Supervisory Mechanism regulation (EU)1024/2013 (SSM regulation), which instruments confer on the ECBspecific tasks concerning prudential supervision policies of creditinstitutions and other financial institutions.

Recital 80 of the proposal provides thatauthorities responsible for the supervision andenforcement of the financial services legislation, including whereapplicable the European Central Bank, should be designated ascompetent authorities for the purpose of supervising theimplementation of this regulation, including for marketsurveillance activities, as regards AI systems provided or used byregulated and supervised financial institutions.

The bank held that market surveillance' under theArtificial Intelligence Act would also consist in ensuring thepublic interest of individuals (including health and safety). In anutshell, the ECB informed the Commission that the ECB has nocompetence to regulate solutions like Grace the robot, but it willonly ensure the safety and soundness of credit institutions. Tothis effect, the bank suggested that (i) a relevant authority beappointed for health and safety risks related obligations; and (ii)another AI authority be set up at Union level to ensureharmonisation.

In parallel, the ECB also recommended that the ArtificialIntelligence Act be amended so as to mandate that, that in relationto credit institutions evaluating the creditworthiness of personsand credit scoring, an ex-post assessment be carried out by theprudential supervisor as part of the SREP, in addition to theex-ante internal controls that are already listed in theproposal.

Interestingly, the Bank for International Settlements, in itsnewsletter on artificial intelligence and machine learning, raisedits concerns in view of the cyber, security and confidentialityrisks, data governance challenges, risk management, biases,inaccuracies and potential unethical outcomes of AI systems,the committee believes that the rapid evolution anduse of AI/ML by banks warrant more discussions on the supervisoryimplications.

While the Artificial Intelligence Act has not been agreed uponin its final form and may be substantially changed before itsacceptance, it is safe to say that the financial sector is one inwhich the challenges relating to the use of AI need to be evaluatedwell, before and when deploying such technological solutions, inview of the risks and individual rights that are at stake.

Originally Published by Times of Malta

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

Link:
ECB Publishes Its Bug Report On The Proposed EU Artificial Intelligence Act - New Technology - Malta - Mondaq

In marketing, 'creative impact' was once too subjective to measure. But with modern AI solutions, creative efforts can now be effectively analyzed. As part of The Drum's Creativity in Focus Deep Dive, Meta's Maria Pavlova (marketing science partner), Karen Chui (creative partner manager, EMEA) and Safia Dawood (marketing science partner) look at how AI tools can help marketing professionals develop more effective advertising strategies through creative effectiveness insights.

Data-driven optimization is widely adopted in digital advertising. Todays marketing professionals have perfected audience profiles and campaign logistics. Yet, the uncomfortable truth is that creative effectiveness is often overlooked despite being the most important campaign element. Creative quality accounts for 70% of an ads success, eclipsing procedural elements like flighting or platform choice.

Audiences want to be dazzled and amazed, so its important to offer them exciting content. Unfortunately, only 60% of advertisers measure creative effectiveness. Of those, the majority collect feedback using post-campaign focus groups or surveys, making the data somewhat unreliable.

At first glance, creative effectiveness appears too subjective to measure. However, todays AI (artificial intelligence) solutions can effectively analyze your creative impact and are becoming increasingly accessible to marketers.

Solutions from Meta are optimizing campaigns creative prowess and advancing AI-enabled creative workflows. Read on to learn how, and discover case studies on brands that gained sales using creative effectiveness insights.

Cutting-edge marketers already use AI to maximize the potential within their advertising strategy. AI simplifies the creative process by helping to produce new creative assets and adjust existing ones at scale.

For example, automated software can translate content across languages, crop images to frame subjects better and generate image descriptions to make visual assets more accessible. As a result, marketing professionals are uncovering new artistic heights and reimagining how to convey messages across mediums.

However, AI is able to take an even larger role within the advertising life cycle and assist marketing teams in planning and optimizing content like never before.

AI tools can help marketing professionals develop more effective advertising strategies based on audience interaction data. This continuous approach to ad optimization is the future of marketing and will help brands lead with content that audiences love.

Measuring creative effectiveness begins with Illumination, which provides initial insights and builds a model to predict future campaign performance. The results help uncover your next big campaign idea and guide your creative process in later stages.

Algorithms begin by analyzing anonymized and aggregated historical data on consumer behavior and identifying which creative elements are most common among your highest-performing ads.

The result is a framework that helps marketers compare different advertising approaches and discover the most effective one before the campaign begins. In turn, marketing professionals can generate new creative assets with more confidence and exceed audience expectations more effectively.

AI can also help steer the ongoing creative strategy once your campaign begins.

Changes within the digital ecosystem mean advertisers must adopt a new playbook. By leveraging continuous optimization through a test-and-learn process, marketers can focus on what audiences like rather than who they are, and avoid privacy concerns from targeted advertising.

Performance evaluation using machine learning can highlight the potential of each creative element within your existing assets. Automated testing solutions can also enable brands to experiment with dynamic business outcomes more efficiently. As a result, creative teams can devise more effective strategic approaches while campaigns are in flight.

Businesses with multiple locations often have to balance variations in brand guidelines to connect with local audiences effectively. Consistent color palettes and tones of voice help brands feel unified and cultivate a consistent public image.

Yet, these variations present a challenge to marketers as communication must be consistent over time.

Todays AI tools measure how creative assets and copy fit within existing brand guidelines and identify areas for improvement. These insights help brands stay true to their guidelines, both between and within sub-brands, and ensure that public-facing communications are curated and consistent.

As a leading beer brand, Heineken needed a way to assess its creative effectiveness across its 300+ sub-brands. By partnering with Meta and CreativeX, a creative analytics company, Heineken uncovered new insights into its creative effectiveness and strategic potential.

[The] creative elements we measured are foundational to driving effectiveness in the Metaecosystem without restricting the creative process and flow, says Sander Bosch, global CMI manager communication effectiveness at Heineken.

Dominos Pizza is one of the worlds most recognized pizza brands, offering a seemingly infinite number of combinations of toppings and crust fillings.

Dominos wanted to improve their video creative using technology and AI and turned to Spirables creative intelligence suite. The two worked together to create three unique ad variants for a multi-cell split test on Facebook and Instagram across a four-week period.

Spirable determined that while motion was beneficial, moving the pizza out of the frame was not.

By utilizing these new insights in future ads, Dominoes increased return on ad spend by 20%, click-through rate by 6%, and reduced cost per result by 9%.

Have you considered how to measure your creative effectiveness?

Creative effectiveness is just the solution to help marketers measure campaign performance and brand recognition in the transition away from targeted online advertising. AI tools from Meta, CreativeX, Spirable, and others are available today and can elevate your creative effectiveness and remove the guesswork from future ad strategies.

By maximizing your creative effectiveness, you can mitigate signal loss and deliver high-impact content to followers at the same time. You can also establish creative best practices for future campaigns and curate your brand portfolio more effectively.

Moreover, automating routine creative tasks using AI allows you to focus on your campaigns long-term health and ensure ad budgets go further.

Discover tools that can help your business improve creative effectiveness with key insights from Meta Foresights.

Read more here:
How Artificial Intelligence Is Helping To Measure Creative Effectiveness In Marketing - The Drum

Artificial intelligence (AI) is on the priority list for every executive who uses technology to enable their business. And today, every business is a technology business. Despite the excitement around AI and investments in its capabilities, only about a third of companiessay theyve adopted leading operational practices for AI but an increasing percentage are working toward that goal.

While AI is often seen as the golden ticket to take business operations into the 21st century and it can to do so, the technology must be approached specifically and strategically, not as an all-in-one solution.

In the universe of technology, one can picture a solar system of interdependent capabilities. At the core, cloud technology serves as the sun a central power source fuelling and enabling other technologies. Underlying cloud platforms, such as Amazon Web Services or Google Cloud, provide the basis for other capabilities to flourish in the technology universe.

Rotating around cloud platforms, there are various AI planets in orbit that build off of cloud infrastructure to deliver solutions such as automation, machine learning, robotic process automation, and more. Many business leaders are eager to enter the orbit of artificial intelligence solutions, but must first start by building the necessary foundation for successful AI implementations.

Once the centre of the AI solar system is in place, to effectively unlock the power of AI, its important that business leaders understand what it is they are trying to solve. And while many suppliers have powerful offerings, AI is not one-size-fits-all in its approach or implementation. It takes several capabilities and applications to drive true end-to-end AI outcomes.

This ecosystem strategy can ultimately offer flexibility and stability for IT decision-makers looking to harness business data and drive meaningful results for their organisations. Key to demonstrating the importance of AI ecosystems is discussing current barriers a company is trying to overcome and what specific AI capabilities will solve for them.

Today, business leaders are looking to define the function of artificial intelligence in their organisations and how they can effectively implement AI given their current technology stacks.

For example, a banking executive may look to automate some of their companys digital banking capabilities. To get there, the institution must consider how they are currently housing their data, how that data will be processed and then refined for usage, and finally how the data can provide insight to their workforce and what insights will be most valuable to them.

In this case, an organisation may have to consider combining the technology and environment they have in place with new technology and capabilities to achieve their desired outcome of a new automated banking tool. The allure of a one-stop shop for AI needs may sway businesses to heavily invest in one provider, which can put up roadblocks on the journey to a meaningful, AI-powered solution.

Part of the trouble with seeing one supplier as a silver-bullet solution is that businesses may invest too heavily in a provider that wont help them move the needle on all of their specific AI goals. Given the hefty budgets businesses are developing for their IT departments, its critical to understand that investments are going towards the appropriate solution(s) and that more money towards a nebulous, blanket AI may not always equate to unlocking business success.

IT decision-makers must have a clear understanding of their companys technology solar system before implementing a new AI tool Anthony Ciarlo and Frank Farrell, Deloitte

Moreover, the overarching cloud environment in which an AI solution is deployed can make or break its success. This means IT decision-makers must have a clear understanding of their companys technology solar system before implementing a new AI tool. When AI-related requests for proposal come across our desks, our first goal is to work through the specific needs of the clients organisation and if the resources they are putting behind the AI solutions will get them where they want to be.

End to end, it is difficult for any one supplier to meet all of the AI needs of an organisation. Some are leaders in automation, while others are leaders in data analytics or machine learning understanding these different strengths enables Deloitte to provide meaningful, tailored assessments as to what investments should be made.

As a systems integrator, once the Deloitte team has holistic insight into an organisations pain points, it can provide confident recommendations as to where money should be invested and how companies can see the greatest return on investment in their technology budgets. The Deloitte team delivers confidence in integrating and navigating the solar system to provide the desired outcomes its clients and their clients need.

The ecosystem approach to AI solutions marks an important shift for how systems integrators should be approaching their client solutions. In years to come, its likely that there will be increased collaboration across market providers, resulting in more streamlined, transparent AI implementation processes.

The key driver for this shift is continued conversations with business and technology leaders who understand that AI is not an isolated entity, but rather serves as a key component within a solar system of interconnected platforms and tools that can offer individualised solutions for the most pressing business challenges.

Anthony Ciarlo is strategy and analytics alliances leader and Frank Farrell is principal for cloud analytics and AI ecosystems at Deloitte.

Read more:
How to leverage the artificial intelligence solar system - ComputerWeekly.com

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The United States spends more on health care than any other country in the world.

But Americans aren't as healthy as people living in other developed countries.

Could artificial intelligence change all that?

WBUR's On Point brings you Smarter health, a four-part series exploring how artificial intelligence and machine learning may revolutionize the health care industry.

We'll investigate the technology already available, or in development, for clinical settings, examine the ethical dilemmas the technology presents in medicine and understand the guiderails and regulations in progress to advise AI advancements.

We'll also hear from the people involved in AI in health care; scientists developing tools, clinicians and doctors using the tools, and patients experiencing changing technology as part of their care.

Episode 1. How AI is transforming health care: Artificial intelligence offers the potential to improve health care from predicting someones risk of having a heart attack, to predicting seizure loads for epilepsy patients, to solving public health problems. What is the potential for AI to transform American health care?

Episode 2. Ethics of the death predictor: We'll break down the ethical considerations of AI in health care.What are the privacy concerns about data collection, and how can researchers and developers advance tools while protecting patients?

Episode 3. Regulating the algorithm:As AI develops in the health care space,regulations need to develop in tandem. We'll talk to the head of the FDAsdigital health division, Dr. Matthew Diamond, about what role the FDA will play as AI expands. Well also talk to experts about guardrails needed to ensure patient safety and privacy.

Episode 4. The people of AI: Our final episode gets up close with the people working and developing AI technology, and the patients receiving AI care. How can this technology thrive in our complex and broken health care system?

Radio

Podcast

Got a question about how AI will impact how you receivehealth care? Or maybe you're a scientist, doctor or patient with an AI story to share? Leave us a voicemail at 617-353-0683.

Meghna Chakrabarti is the award-winning host and editor ofOn Point. Based in Boston, she is on the air Monday through Friday.

The Alliance for Women in Media honoredOn Point'sepisode"A Look Back at 1992 Los Angeles And America Since Rodney King"with a 2022 nationalGracie Award for Best News Documentary. The Alliance for Women in Media also gave Meghna anhonorable mentionfor best nationally syndicated non-commercial correspondent/host.

On Point'sepisode on Los Angeles since Rodney King also won a2022 regional Edward R. Murrow awardfor best news documentary. In 2021,On Pointwon aNational Edward R. Murrow awardfor best news documentary for"What the President Knew."The show examined presidential decision-making before 9/11 and the COVID pandemic.

Chakrabarti is the former host ofRadio Boston, WBURs acclaimed weekday local show. She's the former host ofModern Love: The Podcast, a collaboration of WBUR and The New York Times (2016-2020) and was the primary fill-in host forHere & Now, NPR and WBUR's midday show. She reported on New England transportation and energy issues for WBURs news department.

This series is supported in part by Vertex, The Science of Possibility.

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Smarter health: Artificial intelligence and the future of American health care - WBUR News

LONDON, May 24, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the artificial intelligence (AI) in drug discovery market, the rising adoption of cloud-based applications and services by pharmaceutical companies will contribute to the growth of AI in the drug discovery market. Among the various end-users of cloud-based drug discovery platforms, pharmaceutical vendors are likely to be major stakeholders, holding a high-value share of the global cloud-based drug discovery platform market. An opportunity analysis of the global market reveals that leading software vendors have already adopted cloud-based drug discovery platforms to facilitate seamless research and development processes. Moreover, the cloud-based drug discovery platform revolution will witness significant growth in the coming years, thereby creating better opportunities for software vendors for growth and expansion. For example, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, announced in December 2021 that it is collaborating with Pfizer to develop innovative, cloud-based solutions that have the potential to improve how new medicines are developed, manufactured, and distributed for clinical trials. The companies are exploring these advances through their newly created Pfizer Amazon Collaboration Team (PACT) initiative, which applies AWS capabilities in analytics, machine learning, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. Thus, the increasing adoption of cloud-based applications and services by pharmaceutical companies will contribute positively to the AI drug discovery market size.

Request for a sample of the global artificial intelligence (AI) in drug discovery market report

The global artificial intelligence in drug discovery market size is expected to grow from $0.79 billion in 2021 to $1.04 billion in 2022 at a compound annual growth rate (CAGR) of 31.6%. The growth in the market is mainly due to the companies resuming their operations and adapting to the new normal while recovering from the COVID-19 impact, which had earlier led to restrictive containment measures involving social distancing, remote working, and the closure of commercial activities that resulted in operational challenges. The AI in drug discovery market is expected to reach $2.99 billion in 2026 at a CAGR of 30.2%.

Use of AI through Machine Learning (ML) is a trend in assessing pre-clinical studies during the drug development process. Pre-clinical studies are non-clinical studies for novel drug substances to establish clinical efficacy and safety in a controlled environment before testing with a final target population. ML modelling pharmacokinetic (PK) and pharmacodynamic (PD) methodologies are applied in in-vitro and preclinical PK studies to successfully anticipate the dose concentration response relationship of pipeline assets. In addition, deep learning methodologies are employed as In-Silico methods for successfully predicting the therapeutic/pharmacological properties of novel molecules by utilizing transcriptomic data, which includes various biological systems and controlled conditions. Besides the drug discovery market, machine learning technology finds its application in the AI in medical diagnostics market as well as AI in medical imaging market.

Major players in the artificial intelligence for drug discovery and development market are IBM Corporation, Microsoft, Atomwise Inc., Deep Genomics, Cloud Pharmaceuticals, Insilico Medicine, Benevolent AI, Exscientia, Cyclica, and BIOAGE.

The global artificial intelligence in drug discovery market report is segmented by technology into deep learning, machine learning; by drug type into small molecule, large molecules; by disease type into metabolic disease, cardiovascular disease, oncology, neurodegenerative diseases, others; by end-users into pharmaceutical companies, biopharmaceutical companies, academic and research institutes, others.

In 2021, North America was the largest region in the artificial intelligence (AI) in drug discovery market. It was followed by the Asia-Pacific, Western Europe, and then the other regions. The regions covered in the AI in drug discovery market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, and Africa.

Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide artificial intelligence (AI) in drug discovery market overviews, artificial intelligence (AI) in drug discovery market analyze and forecast market size and growth for the whole market, artificial intelligence (AI) in drug discovery market segments and geographies, artificial intelligence (AI) in drug discovery market trends, artificial intelligence (AI) in drug discovery market drivers, artificial intelligence (AI) in drug discovery market restraints, artificial intelligence (AI) in drug discovery market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

Not the market you are looking for? Check out some similar market intelligence reports:

AI In Pharma Global Market Report 2022 By Technology (Context-Aware Processing, Natural Language Processing, Querying Method, Deep Learning), By Drug Type (Small Molecule, Large Molecules), By Application (Diagnosis, Clinical Trial Research, Drug Discovery, Research And Development, Epidemic Prediction) Market Size, Trends, And Global Forecast 2022-2026

Artificial Intelligence In Healthcare Global Market Report 2022 By Offering (Hardware, Software), By Algorithms (Deep Learning, Querying Method, Natural Language Processing, Context Aware Processing), By Application (Robot-Assisted Surgery, Virtual Nursing Assistant, Administrative Workflow Assistance, Fraud Detection, Dosage Error Reduction, Clinical Trial Participant Identifier, Preliminary Diagnosis), By End User(Hospitals And Diagnostic Centers, Pharmaceutical And Biopharmaceutical Companies, Healthcare Payers, Patients) Market Size, Trends, And Global Forecast 2022-2026

Cloud Services Global Market Report 2022 By Type (Software As A Service (SaaS), Platform As A Service (PaaS), Infrastructure As A Service (IaaS), Business Process As A Service (BPaaS)), By End-User Industry (BFSI, Media And Entertainment, IT And Telecommunications, Energy And Utilities, Government And Public Sector, Retail And Consumer Goods, Manufacturing), By Application (Storage, Backup, And Disaster Recovery, Application Development And Testing, Database Management, Business Analytics, Integration And Orchestration, Customer Relationship Management), By Deployment Model (Public Cloud, Private Cloud, Hybrid Cloud), By Organization Size (Large Enterprises, Small And Medium Enterprises) Market Size, Trends, And Global Forecast 2022-2026

Interested to know more about The Business Research Company?

The Business Research Company is a market intelligence firm that excels in company, market, and consumer research. Located globally it has specialist consultants in a wide range of industries including manufacturing, healthcare, financial services, chemicals, and technology.

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Artificial Intelligence (AI) In Drug Discovery Market Growth Is Driven At A 30% Rate With Increasing Adoption Of Cloud-Based Applications And Services...