Category Archives: Artificial Intelligence
Artificial intelligence and the super app – McKinsey
QuantumBlack, AI by McKinsey recently sat down with Selim Turki, head of data and AI at Uber-owned mobility company Careem, to discuss the latest trends in advanced analytics and artificial intelligence. Far from a dry discussion of theory, the conversation coalesced around several fascinating use cases in which Careem is using AI to make a difference in peoples lives. We discussed how AI is being leveraged to improve customer and driver security through targeted facial-recognition checks to ensure drivers (captains) are who they say they are. We also discussed how AI is being used to provide customers with the most accurate and up-to-date estimated times of arrival (ETAs) by factoring in a host of conditions, including local weather conditions, prayer times, and even iftar times during Ramadan. Along the way, we discussed what it means to be an AI first company and the outlook for AI techand talentin the region.
QuantumBlack: Was AI always an important part of Careems growth journey? How has AIs role evolved since Careems inception?
Selim Turki: We started our journey as a ride-hailing company booking journeys for corporate clients. We were initially booking cars manually, without a data server, before introducing more advanced systems to deliver more efficient, personalized experiences. Since day one, our mission has been to simplify and improve the lives of peopleparticularly our customers and captains. We quickly understood that maintaining high reliability for our dynamic marketplace 24/7 was a complex process that needed to be driven by instant decision making through continuous automation at scale.
We began processing real-time data, using algorithms and machine learning [ML] models to solve some of the core issues for our ride-hailing marketplace, including matching customers and captains efficiently, shaping our demand and supply via surge pricing, calculating accurate ETAs for our captains, and improving our maps and location search functionality.
Today, we are scaling the Middle East, North Africa, and Pakistan [MENAP] regions first super app. AI is in our DNA as we invest more in platform capabilities and team skill sets. Our hiring strategy is focused on growing a diverse team of data and machine learning scientists to build out our in-house experimentation and machine learning platforms.
QuantumBlack: Has the adoption of these new AI techniques changed the way Careem works to serve its customers? How has this affected business teams within the organization?
Selim Turki: We use several AI techniques depending on the type of service we offer in our super app. All of these techniques are directed at three particular needs:
We use AI to factor in prayer times, iftar time during Ramadan, and weather conditions to better predict the ETA accuracy of when the food will be delivered to our customers.
QuantumBlack: How many AI practitioners work with Careem today?
Selim Turki: We have dozens of AI and machine learning experts who are driving forward our strategy of being an AI-first company. Part of our plan is to educate the entire organization on the topic, inviting our engineers and business counterparts to use AI to solve some of their challenges. We have also designed a program dedicated to new college graduates to ensure future talent is up to date with the latest AI techniques and to encourage them to further develop their skills.
QuantumBlack: How do you integrate AI into your decision making now? How do you stay ahead of competition in the market?
Selim Turki: AI is part of Careems decision-making framework. We set quarterly goals to measure and assess the usage and impact of our ML models on the different business streams.
We use rigorous statistical methodologies, taking confounding effects into account, to accurately estimate the models impact on different areas of the business.
To help our data and AI teams stay on top of the changes happening in the industry, we have started collaborating with regional academic institutions to solve some of the most significant super-app challenges and to identify exciting new opportunities for AI innovation.
We publish our progress on the Careem engineering blog and invite third parties to collaborate with us on specific areas related to AI.
We also contribute to open-source data communities and offer our work to other AI and ML professionals.
QuantumBlack: Can you share a recent instance of how AI fundamentally changed the way Careem does business with its customers or captains?
Selim Turki: With any digital platform, fraudsters will look for loopholes to exploit, whether through creating fake-identity accounts or exploring ways to hijack open accounts. Our team uses advanced AI techniques focusing on the identity of users to detect and prevent losses stemming from fraud. One system we use, called Crazy Wall, uses a relational graph convolutional network to map different data points of a customers identity. It also identifies characteristics shared across different identities to detect and mass-block fraudulent patterns across customer or captain activities.
QuantumBlack: AI talent has been a key challenge for companies in the region. How have you dealt with the regions structural talent issues?
Selim Turki: The regions tech talent is growing rapidly, and its exciting to see more specialists choosing to come to the region to make an impact in some of the fastest-growing countries in the world. Its also exciting to see a growing number of local university graduates specializing in AI. Were fortunate to have attracted a strong community of AI talent both locally and from surrounding markets to Careem. Our teams are building tech across various areas, including e-commerce, technology-enabled logistics, maps, identity, and fintech. They can solve complex and meaningful challenges at scale thanks to Careems deep tech expertise, strong regulatory relationships, local presence, and increasingly specialized global teams that are structured to operate as autonomous start-ups. Our team of more than 400 engineers and developers are empowered to develop cutting-edge technology every day. Being a remote-first company allows us to attract talent from across the world who want to have an impact on the MENAP region. This means that the opportunities to gain new perspectives and solve complex, real-world challenges alongside talented peers are endless.
QuantumBlack: Do you think the talent-supply challenges are here to stay? What is your ambition for attracting cutting-edge AI practitioners to Careem in the next three to five years?
Selim Turki: As AI becomes more widely used across industries, the demand for specialists will continue to rise. We need to inspire the next generation of data and AI specialists to be curious and gain exposure to the workplace at an earlier age.
At Careem, we are focused on building an AI culture where opportunities to learn and thrive are fostered by adapting, mentoring, and sharing within our AI communities and beyond. We are also hoping to make AI more accessible to stakeholders across Careem with initiatives like no-code AI, where AI is accessible without existing coding skill sets, as well as partnerships with AI labs to democratize AI usage across the company.
QuantumBlack: How will AI specifically change the mobility space in MENAP? Are there any white spaces where MENAP companies could be global first movers?
Selim Turki: The global mobility space is at a very nascent phase, with considerable opportunities to solve using AI techniques. At Careem, we have the vision of creating an internet-like network to transport packages of atoms, like how the internet transports packets of bits, called the AtomNet.
The AtomNet provides an open-network platform that connects, manages, and routes multimode autonomous vehicles [AVs] to make transport ubiquitous. Similar to how packets can travel across multiple modalities of transport (Wi-Fi, DSL, cable, and fiber), packages on the AtomNet can travel in autonomous motorcycles, cars, vans, trucks, ships, drones, and airplanes. We foresee an AtomNet industry ecosystem with open package headers and protocols to allow package switching and efficient package mobility. With open protocols, coordination costs will drop significantly, and local, national, and international transport gaps will narrow over the years.
AtomNet will support Careems quick commerce, fulfillment centers, restaurants, groceries, dark stores, transportation, and cross-border commerce. We see the epicenter of AtomNet starting in the UAE due to its progressive regulation and culture of innovation.
QuantumBlack: AI is still in its nascency in the broader context of this region. How do you think this will change in the next five to ten years?
Selim Turki: A long and exciting journey is ahead of us in the wider Middle East. With the growing pace of technology, more and more regional corporations will use AI to enhance their products and offer a better experience to customers.
At Careem, our primary focus will continue to be building the internet platform of the Middle East to provide access to our servicesusing data and AI as a core to simplify and improve customers lives. The meta goal is to delight all our users and personalize their experience through data and AI in every service offered through our super app.
The current trend of making trade-offs by improving AI prediction will be strengthened at the cost of short-term factors such as ingestion costs, customer experience, and operational excellence. We will continue investing in our data streams to help our models learn, build, and manage algorithms at scale. Moreover, real-time feedback loops will continue to decipher customer behavior and how it evolves by using our services through leveraging more intelligent software and hardware. Some of the emerging machine learning models will be tailored more to our region, considering language, customer behavior, and product relevance.
Our goal is to provide the simplest and best possible customer experience. To make things simple, you have to make them intuitive. To make things intuitively simple, we need to:
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Artificial intelligence and the super app - McKinsey
What Quantum Computing Will Mean for the Future Artificial Intelligence – ReadWrite
Todays artificial intelligence (AI) systems are only as good as the data theyre trained on. The AI industry is currently taking advantage of large datasets to train AI models and make them more useful. However, as these datasets are becoming limited, researchers are exploring other ways to improve AI algorithms. One such way is quantum computing. It is a new frontier of computer science that will enable better AI algorithms shortly.
Atoms make up our world, and they and their constituents have baffling yet interesting properties. For example, electrons have spin and orbit that can be either up. In addition, they can be in any of the infinite discrete energy levels. These properties determine the quantum states of atoms. At a subatomic level, everything exists as quantum states rather than as traditional logical on or off values. This phenomenon gave rise to quantum computing. It has the potential to change how we see artificial intelligence forever.
Quantum computing is an entirely different way of studying the world around us. It does not just focus on the properties of atoms and molecules. It takes a look at the subatomic properties of atoms that are actually in superposition. That is, they exist in multiple states at the same time. This is one of the principles of quantum mechanics that enable subatomic particles to exist as both particles and waves at the same time.
These principles are strange and counterintuitive. According to them, a computing system cannot only store and process data in binary bits, 0s and 1s. Or in more electronic engineering terms, the state of off and on of an electronic switch. It can also store and process data in superposed states of not on or off but the combination thereof. By harnessing these principles, quantum computers can solve complex problems much faster than traditional computers.
Quantum computers are a variety of different supercomputers based on quantum mechanics. These quantum computers use the laws of quantum mechanics to process information. That means they can find patterns in big data that are almost impossible to find with conventional computers. This way, they are fundamentally different from the computers we use today.
When it comes to artificial intelligence, quantum computing can analyze a wider variety of data. At the same time, they can come to better conclusions than computers today. Conventional computers can only process information as either 1s or 0s. Quantum computers can process information in multiple states known as qubits at once. That enables them to analyze a wider variety of data and come to better conclusions than computers can today.
Artificial intelligence has come a long way in the past few years. It has been able to generate realistic 3D images and videos. In addition, it is beginning to embrace quantum computing. That has given rise to quantum AI. Artificial intelligence now leverages quantum computers. And their full integration will be a technological revolution of the century.
There are several benefits of using quantum AI in creative industries. I have already made it clear it can handle large data sets faster and more efficiently than traditional AI technologies. It can also identify patterns that are difficult for regular computers to spot. Furthermore, it can combine and rearrange existing ideas. Hence it can create new ideas in ways that any human cannot imagine possible.
One of the biggest hurdles for artificial intelligence today is training the machine to do something useful. For example, we might have a model that can correctly identify a dog in a photo. But the model will need to be trained with tens of thousands of images for it to recognize the subtle differences between a beagle, a poodle, and a Great Dane. This process is what AI researchers call training. They use it to teach AI algorithms to make predictions in new situations.
Quantum computing can make this training process faster and more accurate. It will allow AI researchers to use more data than they have ever used before. It can process large amounts of data in 1s and 0s and the combination thereof which will enable quantum computers to come to more accurate conclusions than traditional computers. In other words, AI researchers can use larger datasets to train AI models to be more accurate and better at decision-making.
One of the most exciting predictions for quantum computing in artificial intelligence is the potential to break through language barriers. AI models can currently understand one language the language used to train them. so if we need AI to understand a different language, we shall need to teach it from scratch. However, quantum computing can help AI models break through language barriers. It will allow us to train models in one language and translate them into a different language effortlessly.
That will enable AI to understand and interpret different languages simultaneously. What this will do is create a global AI that can speak multiple languages. Another exciting prediction for the future of AI with quantum computing is the potential to build models with more accurate decision-making skills: Quantum computing will allow using larger datasets to train models. Hence AI will be able to make more accurate decisions that will be especially helpful for financial models, which often have a high rate of inaccuracy because of the limited data used to train them.
Artificial intelligence is already improving the performance of quantum computers. This trend will only continue in the future. The following are some reasons why:
The potential of quantum computing is limitless, but its integration into artificial intelligence will produce a technology that will be rather powerful than anything we have today. The new technology will enable machines to learn and self-evolve. It will make them exponentially better at solving complex problems and developing self-learning algorithms that will drive efficiency in sectors such as finance or healthcare.
Quantum AI systems will be able to process large amounts of information quickly and accurately. That will open up a new world of possibilities for businesses and individuals. They will also be able to solve complex problems that are impossible for even the most advanced conventional computer systems.
Nevertheless, we must remember that these technologies are relatively new; we are still discovering new ways to use quantum computing. Therefore, we must be aware of the latest technology to take advantage of new opportunities as they come along.
The rise of quantum computing will change the way we interact with AI in the future. That means we must stay informed so we can prepare for the changes and make the most of this exciting technology.
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Chisom is a physicist and a tech enthusiast interested in web3, cybersecurity, blockchain application, and quantum information science; he writes about the advancements therein. When he is not writing about tech, he is either crafting absorbing copy for blogs, marketing, and PR, reading his favorite books, or singing at the top of his lungs. He is a shine-through writer with a massive readership.
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What Quantum Computing Will Mean for the Future Artificial Intelligence - ReadWrite
In the Global Race to Lead on Artificial Intelligence, America Must Win – uschamber.com
Across the country, artificial intelligence is powering machines and computers to help us solve problems and work more efficiently. Its assisting scientists to develop vaccines and treat patients more effectively, securing our nations networks and critical infrastructure against cyberattacks, alerting customers of bank fraud and expanding financial opportunities for underserved communities through access to credit, and much more. AI is rapidly changing how businesses operateand is foundational to a thriving 21st-century economy. By 2030, 70% of businessesglobally expect to use AI. Around the world, AI is estimated to boost global GDP by 14% over the same period, accounting for nearly $16 trillion of economic output.
From basic needs, such as food security and supply chain resiliency, to ensuring our nations competitive advantage through research and development and the intellectual property rights that underpin it, AI will shape the new economic era. Its no wonder that, according to a poll conducted by the U.S. Chamber Technology Engagement Center (C_TEC) 80% of Americans feel its vital for the U.S. to lead the world in AI. The reality before us is as simple as it is stark: whoever leads in the advancement of AI will lead the global economy.
To that end, were seeing allies and strategic competitors pursue AI leadership. Earlier this year, Russia and China announced they would work cooperatively to develop AI. Of course, China is already investing heavily in this space in parallel to engaging in IP theft and cyber espionage to steal American innovation. At the same time, our friends and partners in Europe are looking to write regulations around data and AI, some of which could disadvantage U.S. businesses if not carefully constructed. Nations worldwide are racing ahead and we must not fall behind.
We must get the policy environment right to enable American innovators to lead the AI revolution. With government and industry working together, we will ensure that becomes a reality. We will compete against nations in research and development, create an environment where AI is used responsibly, respect personal liberties, and ensure our workforce is prepared for an AI-driven future. The work of this Commission is a critical next step in the U.S. Chambers leadership on this issue, building on the AI principles we released in 2019.
Recently, the U.S. Chamber Artificial Intelligence Commission on Competition, Inclusion, and Innovation wrapped its final field hearing. The U.S. Chamber formed this Commission in January to better understand how our nation can lead the world in adopting AI technologies and enact sound regulations to harness its potential.
Co-chaired by former Congressman John Delaney and former Congressman Mike Ferguson, the Commission has held public hearings in Austin, Cleveland, Palo Alto, London, and Washington, DC, bringing together thought leaders, researchers, and experts in industry, academia, and civil society. Here is what the Commission found during those public hearings:
As AI grows increasingly ubiquitous in our everyday lives and crucial to our nations economic growth, these issues are inextricably linked. This Fall, we look forward to the Commissions final recommendations to help guide policymakers toward durable, bipartisan AI policy solutions. The U.S.Chamber is committed to ensuring our recommendations produce actions, and those actions produce results.
Executive Vice President, Center for Capital Markets Competitiveness (CCMC), U.S. Chamber of Commerce, Executive Vice President, Center for Technology Engagement (C_TEC), U.S. Chamber of Commerce, Executive Vice President, Global Innovation Policy Center (GIPC), U.S. Chamber of Commerce, Senior Advisor to the President and CEO, U.S. Chamber of Commerce
Tom Quaadman develops and executes strategic policies to implement a global corporate financial reporting system, address ongoing attempts of minority shareholder abuse of the proxy system, communicate the benefits of efficient American capital markets, and promote an innovation economy and the long-term interests of all investors.
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In the Global Race to Lead on Artificial Intelligence, America Must Win - uschamber.com
Artificial Intelligence Is Not the Same as Artificial Consciousness – ChristianHeadlines.com
In June, a Google employee who claimed the company had created a sentient artificial intelligence bot was placed on administrative leave.Blake Lemoine, part of Googles Responsible AI (artificial intelligence) program, had been interacting with a language AI known as Language Model for Dialogue Applications, or LaMDA. When the algorithm began talking about rights and personhood, Lemoine decided his superiors and eventually the public needed to know. To him, it was clear the program had become sentient, with the ability to feel, think, and experience life like a human.
Google denied the claim (which is exactly what they woulddo, isnt it?). There was no evidence that LaMDA was sentient (and lots of evidence against it), said a spokesperson. The Atlantics Stephen Marche agreed: The fact that LaMDA in particular has been the center of attention is, frankly, a little quaint. Convincing chatbots are far from groundbreaking tech at this point.
True, but they arethe plot of a thousand science fiction novels.So, the question remains, is a truly sentient AI even possible? How could code develop the capacity for feelings, experiences, or intentionality? Even if our best algorithms can one day perfectly mirror the behavior of people, would they be conscious?
How one answers such questions depends on ones anthropology. What are people? Are we merely computers made of flesh? Or is there something more to us than the sum of our parts, a true ghost in the machine?A true ghost in the shell?
These kinds of questions about humans and the things that humans make reflect what philosopher David Chalmers has called the hard problem of consciousness.In every age, even if strictly material evidence for the soul remains elusive, people have sensed that personhood, willpower, and first-person subjective experiences mean something. Christians are among those who believe that we are more than the stuff of our bodies, though Christians, unlike others, would be quick to add, but not less. There is something to us and the world that goes beyond the physical because there is a non-material, eternal God behind it all.
Christians also hold that there are qualitativedifferences between people and algorithms, between life and nonliving things like rocks and stars, between image bearers and other living creatures. Though much about sentience and consciousness remains a mystery, personhood rests on the solid metaphysical ground of a personal and powerful Creator.
Materialists have a much harder problem declaring such distinctions. By denying the existence of anything other than the physical stuff of the universe, they dont merely erase the substance of certain aspects of the human experience such as good, evil, purpose, and free will: Theres no real grounding for thinking of a person as unique, different, or valuable.
According to philosopher Thomas Metzinger, for example, in a conversation with Sam Harris, none of us ever was or had a self. Take brain surgery, Metzinger says. You peel back the skull and realize that there is only tissue, tissue made of the exact same components as everything else in the universe. Thus, he concludes, the concept of an individual person is meaningless, a purely linguistic construct designed to make sense of phenomena that arent there.
That kind of straightforward claim, though shocking to most people, is consistent within a purely materialist worldview. What quickly becomes inconsistent are claims of ethical norms or proper authority in a world without persons. In a world without a why or an ought, theres only is, which tends to be the prerogative of the powerful, a fact that Harris and Metzinger candidly acknowledge.
In a materialist world, any computational program could potentially become sentient simply by sufficiently mirroring (and even surpassing) human neurology. After all, in this worldview, theres no qualitative difference between people and robots, only degrees of complexity.This line of thinking, however, quickly collapses into dissonance. Are we really prepared to look at the ones and zeros of our computer programs the same way we look at a newborn baby? Are we prepared to extend human rights and privileges to our machines and programs?
In Marvels 2015 film Avengers: Age of Ultron,lightning from Thors hammer hits a synthetic body programmed with an AI algorithm.A new hero, Vision, comes to life and helps save the day. Its one of the more entertaining movie scenes to wrestle with questions of life and consciousness.
Even in the Marvel universe, no one would believe that a mere AI algorithm, even one designed by Tony Stark, could be sentient, no matter how sophisticated it was. In order to get to consciousness, there needed to be a secret sauce, in this case lightning from a Nordic hammer or power from an Infinity Stone.In the same way, as stunning as advances in artificial intelligence are, a consciousnessthat is truly human requires a spark of the Divine.
Publication date: August 19, 2022
Photo courtesy: Aideal Hwa/Unsplash
The views expressed in this commentary do not necessarily reflect those of Christian Headlines.
BreakPointis a program of the Colson Center for Christian Worldview. BreakPoint commentaries offer incisive content people can't find anywhere else; content that cuts through the fog of relativism and the news cycle with truth and compassion. Founded by Chuck Colson (1931 2012) in 1991 as a daily radio broadcast, BreakPoint provides a Christian perspective on today's news and trends. Today, you can get it in written and a variety of audio formats: on the web, the radio, or your favorite podcast app on the go.
John Stonestreet is President of the Colson Center for Christian Worldview, and radio host of BreakPoint, a daily national radio program providing thought-provoking commentaries on current events and life issues from a biblical worldview. John holds degrees from Trinity Evangelical Divinity School (IL) and Bryan College (TN),and is the co-author of Making Sense of Your World: A Biblical Worldview.
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Artificial Intelligence Is Not the Same as Artificial Consciousness - ChristianHeadlines.com
Conclusions drawn by many artificial intelligence studies cannot be replicated. Here’s why this is a concern – Genetic Literacy Project
History shows civil wars to be among the messiest, most horrifying of human affairs. So Princeton professor Arvind Narayanan and his PhD student Sayash Kapoor got suspicious last year when they discovered a strand of political science research claiming to predict when a civil war will break out with more than 90 percent accuracy, thanks to artificial intelligence Yet when the Princeton researchers looked more closely, many of the results turned out to be a mirage.
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They were claiming near-perfect accuracy, but we found that in each of these cases, there was an error in the machine-learning pipeline, says Kapoor. When he and Narayanan fixed those errors, in every instance they found that modern AI offered virtually no advantage.
That experience prompted the Princeton pair to investigate whether misapplication of machine learning was distorting results in other fieldsand to conclude that incorrect use of the technique is a widespread problem in modern science.
The idea that you can take a four-hour-long online course and then use machine learning in your scientific research has become so overblown, Kapoor says. People have not stopped to think about where things can potentially go wrong.
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Conclusions drawn by many artificial intelligence studies cannot be replicated. Here's why this is a concern - Genetic Literacy Project
Insights on the Artificial Intelligence for Drug Discovery and Development Global Market to 2027 – AI Cloud to Create a Streamlined and Automated…
DUBLIN, Aug. 18, 2022 /PRNewswire/ -- The "Global Artificial Intelligence for Drug Discovery and Development Market (2022-2027) by Offering, Application, End User, Technology, Geography, Competitive Analysis and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.
The Global Artificial Intelligence for Drug Discovery and Development Market is estimated to be USD 1.22 Bn in 2022 and is expected to reach USD 4.8 Bn by 2027, growing at a CAGR of 31.54%.
Market dynamics are forces that impact the prices and behaviors of the stakeholders. These forces create pricing signals which result from the changes in the supply and demand curves for a given product or service. Forces of Market Dynamics may be related to macro-economic and micro-economic factors.
There are dynamic market forces other than price, demand, and supply. Human emotions can also drive decisions, influence the market, and create price signals. As the market dynamics impact the supply and demand curves, decision-makers aim to determine the best way to use various financial tools to stem various strategies for speeding the growth and reducing the risks.
Company Profiles
The report provides a detailed analysis of the competitors in the market. It covers the financial performance analysis for the publicly listed companies in the market. The report also offers detailed information on the companies' recent development and competitive scenario. Some of the companies covered in this report are Aria Pharmaceuticals Inc, Atomwise Inc., BenevolentAI, BioSymetrics, Cloud Pharmaceuticals, etc.
Countries Studied
Competitive Quadrant
The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.
Ansoff Analysis
The report presents a detailed Ansoff matrix analysis for the Global Artificial Intelligence for Drug Discovery and Development Market. Ansoff Matrix, also known as Product/Market Expansion Grid, is a strategic tool used to design strategies for the growth of the company. The matrix can be used to evaluate approaches in four strategies viz. Market Development, Market Penetration, Product Development and Diversification.
The matrix is also used for risk analysis to understand the risk involved with each approach. The analyst analyses the Global Artificial Intelligence for Drug Discovery and Development Market using the Ansoff Matrix to provide the best approaches a company can take to improve its market position. Based on the SWOT analysis conducted on the industry and industry players, the analyst has devised suitable strategies for market growth.
Why buy this report?
Key Topics Covered:
1 Report Description
2 Research Methodology
3 Executive Summary3.1 Introduction3.2 Market Size, Segmentations and Outlook
4 Market Dynamics4.1 Drivers4.1.1 Need for Control Drug Discovery Process and Cost Reduction 4.1.2 Increasing Need to Manage the Large Data Generated During Preclinical Studies4.1.3 Increasing Adoption Across Biopharmaceutical Companies4.2 Restraints4.2.1 Unavailability of Skilled Professionals4.3 Opportunities4.3.1 AI Cloud to Create a Streamlined and Automated Approach in Drug Discovery4.3.2 Increasingly Growing R&D Investments4.4 Challenges4.4.1 Limited Availability of Data Sets
5 Market Analysis5.1 Regulatory Scenario5.2 Porter's Five Forces Analysis5.3 Impact of COVID-195.4 Ansoff Matrix Analysis
6 Global Artificial Intelligence for Drug Discovery and Development Market, By Offering6.1 Introduction6.2 Services6.3 Software
7 Global Artificial Intelligence for Drug Discovery and Development Market, By Application7.1 Introduction7.2 Cardiovascular Disease7.3 Immuno-Oncology7.4 Metabolic Diseases7.5 Neurodegenerative Diseases
8 Global Artificial Intelligence for Drug Discovery and Development Market, By End User8.1 Introduction8.2 Contract Research Organizations8.3 Pharmaceutical & Biotechnology Companies8.4 Research Centers and Academic & Government Institutes
9 Global Artificial Intelligence for Drug Discovery and Development Market, By Technology9.1 Introduction9.2 Machine Learning9.2.1 Deep Learning9.2.2 Supervised Learning9.2.3 Reinforcement Learning9.2.4 Unsupervised Learning9.2.5 Other Machine Learning Technologies9.3 Other Technologies
10 Americas' Artificial Intelligence for Drug Discovery and Development Market10.1 Introduction10.2 Argentina10.3 Brazil10.4 Canada10.5 Chile10.6 Colombia10.7 Mexico10.8 Peru10.9 United States10.10 Rest of Americas
11 Europe's Artificial Intelligence for Drug Discovery and Development Market11.1 Introduction11.2 Austria11.3 Belgium11.4 Denmark11.5 Finland11.6 France11.7 Germany11.8 Italy11.9 Netherlands11.10 Norway11.11 Poland11.12 Russia11.13 Spain11.14 Sweden11.15 Switzerland11.16 United Kingdom11.17 Rest of Europe
12 Middle East and Africa's Artificial Intelligence for Drug Discovery and Development Market12.1 Introduction12.2 Egypt12.3 Israel12.4 Qatar12.5 Saudi Arabia12.6 South Africa12.7 United Arab Emirates12.8 Rest of MEA
13 APAC's Artificial Intelligence for Drug Discovery and Development Market13.1 Introduction13.2 Australia13.3 Bangladesh13.4 China13.5 India13.6 Indonesia13.7 Japan13.8 Malaysia13.9 Philippines13.10 Singapore13.11 South Korea13.12 Sri Lanka13.13 Thailand13.14 Taiwan13.15 Rest of Asia-Pacific
14 Competitive Landscape14.1 Competitive Quadrant14.2 Market Share Analysis14.3 Strategic Initiatives14.3.1 M&A and Investments14.3.2 Partnerships and Collaborations14.3.3 Product Developments and Improvements
15 Company Profiles15.1 Aria Pharmaceuticals Inc15.2 Atomwise Inc15.3 BenevolentAI15.4 BioSymetrics15.5 Cloud Pharmaceuticals15.6 Cyclica15.7 Deep Genomics15.8 Envisagenics15.9 Exscientia15.10 IBM15.11 Insitro15.12 Novartis AG15.13 Nvidia15.14 Owkin Inc15.15 Verge Genomics15.16 XtalPi
16 Appendix
For more information about this report visit https://www.researchandmarkets.com/r/awqyve
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Insights on the Artificial Intelligence for Drug Discovery and Development Global Market to 2027 - AI Cloud to Create a Streamlined and Automated...
The Asia Pacific artificial intelligence market is expected to grow at the highest CAGR of 40.8% from 2022 to 2027 – GlobeNewswire
New York, Aug. 17, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence Market by Offering, Technology, Deployment Mode, Organization Size, Business Function, Vertical and Region - Global forecast to 2027" - https://www.reportlinker.com/p04412107/?utm_source=GNW In the upcoming years, it is anticipated that such developments in artificial intelligence technology would help the sector grow.Business innovators and executives are racing to achieve AIs promise of cost and time savings as well as a competitive advantage.Faster and more precise consumer behaviour data analysis empowers companies to plan their future marketing strategies and campaigns, fueling the expansion of the AI market.
Data management is assisted by AI in understanding which of their methods are inefficient and which are all the most effective. Additionally, it ensures that data reaches the intended user without being tampered by cybercriminals using man-in-the-middle, ransomware, or other forms of cyberattack.The major market players such as include IBM, Microsoft, AWS, Intel, Google, Oracle and Salesforce have adopted numerous growth strategies, which include acquisitions, new product launches, product enhancements, and business expansions, to enhance their market shares.
Based on deployment mode, cloud deployment mode to register for the largest market size during the forecast periodBased on the deployment mode, the artificial intelligence market is segmented into on-premises and cloud deployment mode.The market size of the cloud deployment mode segment is estimated to be the largest during the forecast period.
Scalability, speed, and IT security are all benefits of the cloud deployment approach.Data-driven innovation benefits greatly from the combination of AI and Cloud computing.
The popularity of the cloud deployment mode is facilitated by the cognitive powers of AI and machine learning, which thrive on massive volumes of data that are scalable and easily available in a cloud environment.
The Law segment to account for the highest CAGR during the forecast periodBased on business function, the artificial intelligence market is segmented into Finance, Security, Human Resources, Law, Marketing and Sales and other business functions.The Law segment is expected grow at a higher CAGR during the forecast period.
Large and small legal firms both are using AI technologies in growing numbers.Artificial intelligence technology, in particular Machine Learning and Natural Language Processing, are being used to boost efficiency, expand profit margins, and offer creative and effective legal counsel.
The market for AI is expanding as a result of rising litigation and rising demand to cut operational expenses.
Asia Pacific to hold highest CAGR during the forecast periodThe Asia Pacific artificial intelligence market is expected to grow at the highest CAGR of 40.8% from 2022 to 2027. In countries such as China, India, Japan, and others, the use of AI services in end user industries like manufacturing, healthcare, retail, and e-commerce can be responsible for this increase. In this region, the adoption of new and emerging technologies has gained momentum in recent years. The storage, processing, and data availability of computing systems have all risen, as well as their overall capacity. A new generation of more autonomous AI systems has been made possible by the convergence of complementary technologies, which has increased the demand for automating operations as a result of ongoing improvements in hardware and software.
Breakdown of primariesIn-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the artificial intelligence market. By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20% By Designation: C-Level Executives: 35%, D-Level Executives: 25%, and Managers: 40% By Region: APAC: 25%, Europe: 30%, North America: 30%, MEA: 10%, Latin America: 5%The report includes the study of key players offering artificial intelligence.It profiles major vendors in the artificial intelligence market.
The major players in the artificial intelligence market include Google Inc. (US), Microsoft Corporation (US), NVIDIA Corporation (US), Intel Corporation (US), Samsung Electronics Co., Ltd. (South Korea), IBM Corporation (US), Amazon Web Services, Inc. (US), Oracle (US), Meta (US), Salesforce (US), Cisco (US), Siemens (US), Huawei (China), SAP SE (Germany), SAS Institute (US), Baidu, Inc. (China), Alibaba Cloud (China), iFLYTEK (China), and Hewlett Packard Enterprise Development LP (US).
Research CoverageThe market study covers the artificial intelligence market across segments.It aims at estimating the market size and the growth potential of this market across different segments, such as offering, technology, organization size, deployment mode, business function, vertical, and region.
It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
Key Benefits of Buying the ReportThe report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall artificial intelligence market and its subsegments.It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies.
It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.Read the full report: https://www.reportlinker.com/p04412107/?utm_source=GNW
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The Asia Pacific artificial intelligence market is expected to grow at the highest CAGR of 40.8% from 2022 to 2027 - GlobeNewswire
Will Art Created By Artificial Intelligence Kill The Artist? – Fstoppers
Most of my photography friends have been playing around with some form of AI Art, and the results are pretty remarkable. However, as amazing as this technology is, I'm sure I am not the only one wondering if Artificial Intelligence will leave us all looking for new careers.
What exactly is artificial intelligent art? AI art is a brand new form of expression that allows users to string together a bunch of descriptive words, feed them into a machine learning program, and have the software export a one-of-a-kind, hyper-graphic image in seconds. The results aren't always what you might have imagined in your head, and more times than not, the efforts of the Ai algorithm are beyond your wildest imagination. On one hand, AI-generated art is one of the greatest inventions of modern history but on the other hand, it raises so many questions. Is AI art real art? Is the final image a creative product of the prompt writer? Who owns the rights to the final creation? Should we value it more than similar art that has taken much more time, effort, and skill?
All of these questions led me to reach out to my good friend and fellow photographer/entrepreneur Pye Jirsa. Many of you know Pye as the creative face of SLR Lounge, but he also runs a multi-seven-figure wedding business (perhaps one of the most successful wedding photography businesses in the world), and has recently started a new business venture, 12 Week Relationships, which dives into the world of relationship psychology. Needless to say, Pye is an incredibly talented creative, has a brilliant approach to business marketing, and also understands how new technologies can lead to greater success for those who become early adopters.
Since both Pye and I have explored the early beta offerings of many AI art generators, I thought it would be great to record our early thoughts, arguments, and perspectives on this crazy new form of art. Throughout this extended podcast, we find ourselves both intrigued and horrified at what this new technology will bring to the art world. Some of the topics we cover include:
These are just a few of the concepts we freely talk about in our 90-minute conversation, and I have to say, after bouncing some of my own ideas off Pye, I found myself left with even more questions than I had entering this conversation. Pye brings up some interesting points about how technology shifts in the past have left 99% of nonadopting artists to ruin from a commercial and business standpoint. He also questions how future generations will value and dedicate time to learning any specific art form when artificial intelligence can simply create something far superior and intricate than decades of human practice and mastery of the same medium. Of course, there will always be value in learning an art for fun, emotional sanctuary, and to explore your own creativity. Still, the question remains, "how will AI art change the way we use, consume, and appreciate art in the future?"
Here are a few of the images featured in the podcast created through Mid Journey
Perhaps once I have even more time to form my own thoughts about artificial art and where it is going, I will write up a full opinion piece on Fstoppers. At the moment, if I'm honest with myself, I'm not exactly sure how I truly feel about AI art generators like Mid Journey, Nightcafe, StarryAI, and Dall-E Mini. Half of me absolutely loves seeing what crazy and wacky ideas I can come up with and the resulting images AI generators can produce. The other half of me truly sees the writing on the wall and expects to both see and use AI art more and more in the future.
What are your thoughts on this new form of creativity? The flood gates aren't truly opened yet as many of the programs listed above are still in their beta state and many still require invitations to use their services. Once AI art becomes even more malleable, realistic, and widespread, do you think it will under mind the careers of many creatives or will it always remain a novelty and not compromise the skills so many of us have worked our entire lives to perfect?
If you want to share your own AI art and participate in our latest Critique the Community, check out the CTC Ai Prompt Art Page and perhaps you can win a free tutorial from the Fstoppers Store!
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Will Art Created By Artificial Intelligence Kill The Artist? - Fstoppers
Artificial Intelligence and Inventorship: An Expected Decision with Uncertain Consequences – JD Supra
The top U.S. patent court has confirmed what many were expecting in the patent community that artificial intelligence (AI) is not considered an individual according to the Patent Act and thus AI cannot be named as an inventor on a patent.
The courts ruling was the latest roadblock encountered by Dr. Stephen Thaler, who filed patent applications in several jurisdictions on a technology developed by the Autonomous Bootstrapping of Unified Sentience (DABUS) an AI system created by Dr. Thaler that mimics the neural network of a human brain. These patent applications were filed as part of the Artificial Inventor Project and were focused on protecting two inventions that were conceived by DABUS without human intervention. As such, Dr. Thaler listed DABUS as the sole inventor.
Initially, the Patent Office denied the applications as failing to list a human inventor, relying on a provision of the Patent Act that defines an inventor as the individual . . . who invented or discovered the subject matter of the invention. According to the Patent Office, the current statutes, case law, and Patent Office regulations all limit inventorship to a human and preclude a broad interpretation that would encompass an AI machine.
In September 2021, a federal court in Virginia agreed with the Patent Office. The court provided a glimpse of hope for the future, however, stating [a]s technology evolves, there may come a time when artificial intelligence reaches a level of sophistication such that it might satisfy the accepted meaning of inventorship. But that time has not yet arrived, and, if it does, it will be up to Congress to decide how, if at all, it wants to expand the scope of patent law.
On appeal, the top patent court (The Court of Appeals for the Federal Circuit) confirmed that only humans can be considered inventors under current U.S. patent laws. The decision focused on whether AI could be listed as the sole inventor and did not address instances in which humans use AI to assist with conception of an invention. As such, we may see additional litigation on the latter issue involving parties attempting to invalidate a patent based on improper inventorship.
The Federal Circuit decision only reinforces what has been decided in foreign jurisdictions. Europe and the UK have taken a similar position on AI and inventorship, although Europe has indicated that it may be possible to name the AIs user or owner as the inventor instead. Australia initially seemed to allow for AI to be an inventor, but an Australian court overturned this position in April 2022. South Africa the only jurisdiction in which patents have been granted to DABUS does not substantively review patent applications and, therefore, provides little guidance on this issue.
For now, the legal systems of the world seem to agree that AI cannot be listed as an inventor on a patent application. Although it appears that Dr. Thaler intends on appealing the decision to the Supreme Court, the outcome is not expected to change. Instead, these decisions show that legislative action will be needed to adapt the current patent laws to the quickly evolving world of AI. In the meantime, industries that rely on AI should continue involving humans in the inventive process to ensure that the inventorship includes at least one individual according to current patent laws.
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Artificial Intelligence and Inventorship: An Expected Decision with Uncertain Consequences - JD Supra
Artificial Intelligence In Drug Discovery Global Market to Grow from $1.04 Billion to $2.99 Billion by 2026 – Yahoo Finance
DUBLIN, Aug. 16, 2022 /PRNewswire/ --The "Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022, By Technology, By Drug Type, By Therapeutic Type, By End-Users" report has been added to ResearchAndMarkets.com's offering.
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The global artificial intelligence (AI) in drug discovery market is expected to grow from $791.83 million in 2021 to $1042.30 million in 2022 at a compound annual growth rate (CAGR) of 31.6%. The market is expected to reach $2994.52 million in 2026 at a CAGR of 30.2%.
The artificial intelligence (AI) in drug discovery market consists of sales of AI for drug discovery and related services. Artificial Intelligence (AI) for drug discovery is a technology that uses a simulation of human intelligence process by machines to tackle complex problems in the drug discovery process. It helps to find new molecules to identify drug targets and develop personalized medicines in the pharmaceutical industry.
The main technologies in artificial intelligence (AI) in drug discovery are deep learning and machine learning. Deep learning is a machine learning and artificial intelligence (AI) technique that mimics how humans acquire knowledge. Data science, which covers statistics and predictive modelling, incorporates deep learning as a key component.
The different drug types include small molecule, large molecules and involves various types of therapies such as metabolic disease, cardiovascular disease, oncology, neurodegenerative diseases, others. It is implemented in several end-users including pharmaceutical companies, biopharmaceutical companies, academic and research institutes, others.
The rise in demand for a reduction in the overall time taken for the drug discovery process is a key driver propelling the growth of the artificial intelligence (AI) in drug discovery market. Traditionally, it takes three to five years for animal models to identify and optimize molecules before they are evaluated in humans whereas start-ups based on AI have been identifying and designing new drugs in a matter of few days or months.
For instance, in 2020, the British start-up Exscientia and Japan's Sumitomo Dainippon Pharma have used artificial intelligence to produce an obsessive-compulsive disorder (OCD) medication, decreasing the development time from four years to less than one year. The reduction in overall time taken for the drug discovery process drives the artificial intelligence (AI) in drug discovery market's growth.
The shortage of skilled professionals is expected to hamper the AI in drug discovery market. The employees have to re-train or learn new skill sets to work efficiently on the complex AI machines to get the desired results for the drug. The shortage of skills acts as a major hindrance to drug discovery through AI, discouraging companies from adopting AI-based machines for drug discovery.
ScopeMarkets Covered:1) By Technology: Deep Learning; Machine Learning2) By Drug Type: Small Molecule; Large Molecules3) By Therapeutic Type: Metabolic Disease; Cardiovascular Disease; Oncology; Neurodegenerative Diseases; Others4) By End-Users: Pharmaceutical Companies; Biopharmaceutical Companies; Academic And Research Institutes; Others
Key Topics Covered:
1. Executive Summary
2. Artificial Intelligence (AI) In Drug Discovery Market Characteristics
3. Artificial Intelligence (AI) In Drug Discovery Market Trends And Strategies
4. Impact Of COVID-19 On Artificial Intelligence (AI) In Drug Discovery
5. Artificial Intelligence (AI) In Drug Discovery Market Size And Growth
6. Artificial Intelligence (AI) In Drug Discovery Market Segmentation
7. Artificial Intelligence (AI) In Drug Discovery Market Regional And Country Analysis8. Asia-Pacific Artificial Intelligence (AI) In Drug Discovery Market
9. China Artificial Intelligence (AI) In Drug Discovery Market
10. India Artificial Intelligence (AI) In Drug Discovery Market
11. Japan Artificial Intelligence (AI) In Drug Discovery Market
12. Australia Artificial Intelligence (AI) In Drug Discovery Market
13. Indonesia Artificial Intelligence (AI) In Drug Discovery Market
14. South Korea Artificial Intelligence (AI) In Drug Discovery Market
15. Western Europe Artificial Intelligence (AI) In Drug Discovery Market
16. UK Artificial Intelligence (AI) In Drug Discovery Market
17. Germany Artificial Intelligence (AI) In Drug Discovery Market
18. France Artificial Intelligence (AI) In Drug Discovery Market
19. Eastern Europe Artificial Intelligence (AI) In Drug Discovery Market
20. Russia Artificial Intelligence (AI) In Drug Discovery Market
21. North America Artificial Intelligence (AI) In Drug Discovery Market
22. USA Artificial Intelligence (AI) In Drug Discovery Market
23. South America Artificial Intelligence (AI) In Drug Discovery Market
24. Brazil Artificial Intelligence (AI) In Drug Discovery Market
25. Middle East Artificial Intelligence (AI) In Drug Discovery Market
26. Africa Artificial Intelligence (AI) In Drug Discovery Market
27. Artificial Intelligence (AI) In Drug Discovery Market Competitive Landscape And Company Profiles
28. Key Mergers And Acquisitions In The Artificial Intelligence (AI) In Drug Discovery Market
29. Artificial Intelligence (AI) In Drug Discovery Market Future Outlook and Potential Analysis
30. Appendix
Companies Mentioned
IBM Corporation
Microsoft
Atomwise Inc.
Deep Genomics
Cloud Pharmaceuticals
Insilico Medicine
Benevolent AI
Exscientia
Cyclica
BIOAGE
Numerate
Numedii
Envisagenics
twoXAR
OWKIN Inc.
XtalPi
Berg LLC
Verge Genomics
For more information about this report visit https://www.researchandmarkets.com/r/5onrkw
Media Contact:
Research and MarketsLaura Wood, Senior Managerpress@researchandmarkets.com
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Artificial Intelligence In Drug Discovery Global Market to Grow from $1.04 Billion to $2.99 Billion by 2026 - Yahoo Finance