#engineerai

Microsoft co-founder Bill Gates has been attempting to improve the condition of worldwide wellbeing through his nonprofit foundation for 20 years, and today he told the nation’s premier scientific gathering that propels in artificial intelligence and gene editing could accelerate those enhancements exponentially in the years ahead.

"We have an open door with the advance of tools like artificial intelligence and gene-based editing technologies to fabricate this new age generation of health solutions so they are available to everybody on the planet. Furthermore, I'm very excited for this," Gates said in Seattle during a keynote address at the annual meeting of the American Association for the Advancement of Science.

Such tools promise to have a dramatic impact on several of the biggest challenges on the agenda for the Bill & Melinda Gates Foundation, created by the tech guru and his wife in 2000.

When it comes to fighting malaria and other mosquito-borne diseases, for example, CRISPR-Cas9 and other gene-editing tools are being used to change the insects’ genome to ensure that they can’t pass along the parasites that cause those diseases. The Gates Foundation is investing tens of millions of dollars in technologies to spread those genomic changes rapidly through mosquito populations.

Millions more are being spent to find new ways fighting sickle-cell disease and HIV in humans. Gates said techniques now in development could leapfrog beyond the current state of the art for immunological treatments, which require the costly extraction of cells for genetic engineering, followed by the re-infusion of those modified cells in hopes that they’ll take hold.

For sickle-cell disease, “the vision is to have in-vivo gene editing techniques, that you just do a single injection using vectors that target and edit these blood-forming cells which are down in the bone marrow, with very high efficiency and very few off-target edits,” Gates said. A similar in-vivo therapy could provide a “functional cure” for HIV patients, he said.

The rapid rise of artificial intelligence gives Gates further cause for hope. He noted that that the computational power available for AI applications has been doubling every three and a half months on average, dramatically improving on the two-year doubling rate for chip density that’s described by Moore’s Law.

One project is using AI to look for links between maternal nutrition and infant birth weight. Other projects focus on measuring the balance of different types of microbes in the human gut, using high-throughput gene sequencing. The gut microbiome is thought to play a role in health issues ranging from digestive problems to autoimmune diseases to neurological conditions.

“This is an area that needed these sequencing tools and the high-scale data processing, including AI, to be able to find the patterns,” Gates said. “There’s just too much going on there if you had to do it, say, with paper and pencil to understand the 100 trillion organisms and the large amount of genetic material there. This is a fantastic application for the latest AI technology.”

Similarly, “organs on a chip” could accelerate the pace of biomedical research without putting human experimental subjects at risk.

“In simple terms, the technology allows in-vitro modeling of human organs in a way that mimics how they work in the human body,” Gates said. “There’s some degree of simplification. Most of these systems are single-organ systems. They don’t reproduce everything, but some of the key elements we do see there, including some of the disease states — for example, with the intestine, the liver, the kidney. It lets us understand drug kinetics and drug activity.”

The Gates Foundation has backed a number of organ-on-a-chip projects over the years, including one experiment that’s using lymph-node organoids to evaluate the safety and efficacy of vaccines. At least one organ-on-a-chip venture based in the Seattle area, Nortis, has gone commercial thanks in part to Gates’ support.

High-tech health research tends to come at a high cost, but Gates argues that these technologies will eventually drive down the cost of biomedical innovation.

He also argues that funding from governments and nonprofits will have to play a role in the world’s poorer countries, where those who need advanced medical technologies “essentially have no voice in the marketplace.”

“If the solution of the rich country doesn’t scale down … then there’s this awful thing where it might never happen,” Gates said during a Q&A with Margaret Hamburg, who chairs the AAAS board of directors.

But if the acceleration of medical technologies does manage to happen around the world, Gates insists that could have repercussions on the world’s other great challenges, including the growing inequality between rich and poor.

“Disease is not only a symptom of inequality,” he said, “but it’s a huge cause.”

Other tidbits from Gates’ talk:

When it comes to agriculture, climate change is making the challenges facing farmers in developing countries even more acute, Gates said. More extreme weather conditions could bring more floods, more droughts and more pests and plant diseases capable of wiping out crops. Gates pointed to efforts at CGIAR to develop more resilient strains of corn, rice and other crops, and at the University of Cambridge to build healthier soil. The Gates Foundation recently established a new initiative called Gates Ag One to support such innovations.

Gates said he was concerned about two trends in the distribution of health information. “One is that titillating false information is more engaging than true information,” he said. The flap over the false linkage between vaccines and autism serves as an example of that, he said. “And then there’s this general notion of, hey, if the experts say something, are they somehow biased or naive?” he noted. “This is a fight. Will we go through a cycle where it’s not as acute as it is today? I don’t know. Right at the moment, it doesn’t feel that way.”

“Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include speech recognition, learning, planning, and problem solving.”

Engineer.ai is a Human-Assisted AI platform for building tailor-made software. The platform combines the power of humans, experts in their field with boundless creativity, with AI to create a variety of applications for a wide set of customers.

The platform creates much of the ‘app’ and then algorithmically sources human talent from our network to bring it to life, needless to say, by working together.

We don’t claim to fully automate App Development [the fully autonomous development process (program synthesis) is the holy grail and is many years away]. Rather, we rely on a Human and AI partnership, wherein the reusable software (in traditional software development, duplicated code makes up approximately 60% of the product) is machine produced and the other 40%; what makes most projects unique, is human produced.

From as early as 2017, our founder and CEO has coined the word “Human-Assisted AI” because at a core level, we believe that human creativity and talent will always be part of the idea creation and building journey.

Realistically, and rather pragmatically, there would be no benefit in Engineer.ai spending big bucks (we are talking tens of millions) developing fully autonomous development when we can deliver the customer benefit by focusing on making automatic what is otherwise repetitive, wasteful or inefficient

To run software, Engineer.ai built CloudOps — which uses a combination of data engineering and statistical models to make real time prediction, infrastructure purchases and exchanges (often running into millions of dollars) in an optimal way to benefit our customers.

Needless to say, whilst we can be proud of our technology and what were are continuing to build, the real truth is that because of what we have built, we have already helped entrepreneurs & small businesses to:

BUILD: Build your idea effortlessly with Builder instantly without any coding knowledge.

RUN: Never pay for building the same app again, get warranty for updates.

Engineer.ai make technical and creative knowledge accessible to every builder with a dream. This platform creates an assembly line for building and operating the software with the help of AI and a marketplace of teams comprising of people from across the globe.

Videocon and AEC partners which is a US equity firm have decided to invest in the start-up Nivio. Nivio which has been around for 7 years is a cloud computing.

Artificial Intelligence is developing at a quicker pace. With this quicker pace, it is likewise attracting a series of funding and monetary ventures. We should experience a portion of the significant interests in artificial intelligence organizations in June 2020.

There’s no secret that artificial intelligence or AI is an emerging technological trend. According to a report by Gartner, Artificial Intelligence is going to create 2.3 million jobs by 2020.

Job growth has already filled the industry, as demand for someone with AI skills has already doubled over the past few years. Therefore, in this article,  know about how to become an Artificial Intelligence (AI) Engineer in the following order:

What is Artificial Intelligence?

AI is a technology that enables machines to mimic human behavior. Artificial intelligence is the theory and development of computer systems capable of performing tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.

Who is an AI Engineer?

An AI Engineer works with algorithms, neural networks, and other tools to advance the field of Artificial Intelligence in some way. These professionals can work on different types of artificial intelligence in different industries.

How to Become an AI Engineer?

1. To become an AI Engineer, one needs to study following areas or subject i.e.

Computer Science

Mathematics

Information Technology

Statistics

Finance

Economics

2. Secondly, one needs to be good at programming but also good at Software Development techniques and practices. They need to be knowledgeable both theoretically and practically about the following topics: 

Software Development Life Cycle

Modularity, OOPS, Classes

Design Patterns

Statistics and Mathematics

Machine Learning

Deep Learning & Neural Networks

Electronics, Robotics, and Instrumentation (Not a Mandate)

3. Apart from technical Skills there are also must have Business Skills one must possess while planning on How to Become a Successful Artificial Intelligence Engineer. Some of these skills include:

Analytic Problem-Solving

Effective Communication

Creative Thinking

Industry Knowledge

Artificial intelligence (AI) should be democratized to assist more with peopling understand it and embrace its potential;

We have to create guidelines for AI that are agile and adapt to this quickly advancing innovation;

A focus on "Trustworthy AI" offers a promising model for advancement and the administration of AI.

Artificial intelligence (AI) has gained widespread attention in recent years. Artificial intelligence is seen as a vital innovation to lead us into what's to come. However, while academics, industry leaders and policy-makers alike, I have watched some growing concerns around the vulnerability of this technology.

In my observation, these concerns can be categorized into three perspectives:

Many people lack a full understanding of AI and therefore are more likely to view it as a nebulous cloud instead of a powerful driving force that can create a lot of value for society;

Some companies or individuals worry that they will fall behind as AI becomes more prevalent;

As is often the case with new technology, AI is increasingly used despite policy and regulation being behind the pace.

It is understandable that people might have these concerns at this moment in time and we need to face them. As long as we do, I believe we don’t need to panic about AI and that society will benefit from embracing it. I propose we address these concerns as follows:

1. We should democratize AI

Instead of writing off AI as too complicated for the average person to understand, we should seek to make AI accessible to everyone in society. It shouldn’t be just the scientists and engineers who understand it; through adequate education, communication and collaboration, people will understand the potential value that AI can create for the community.

2. In AI no one will be “left behind”

We should democratize AI, meaning that the technology should belong to and benefit all of society; and we should be realistic about where we are in AI’s development.

We have made a lot of progress in AI. But if we think of it as a vast ocean, we are still only walking on the beach. Most of the achievements we have made are, in fact, based on having a huge amount of (labelled) data, rather than on AI’s ability to be intelligent on its own. Learning in a more natural way, including unsupervised or transfer learning, is still nascent and we are a long way from reaching AI supremacy.

From this point of view, society has only just started its long journey with AI and we are all pretty much starting from the same page. To achieve the next breakthroughs in AI, we need the global community to participate and engage in open collaboration and dialogue.

3. We should take an agile approach to the governance of AI

We can benefit from AI innovation while we are figuring out how to regulate the technology. Let me give you an example: Ford Motor produced the Model T car in 1908, but it took 60 years for the US to issue formal regulations on the use of seatbelts. This delay did not prevent people from benefitting significantly from this form of transportation. At the same time, however, we need regulations so society can reap sustainable benefits from new technologies like AI and we need to work together as a global community to establish and implement them.

By addressing the aforementioned concerns people may have regarding AI, I believe that “Trustworthy AI” will provide great benefits to society. There is already a consensus in the international community about the six dimensions of “Trustworthy AI”: fairness, accountability, value alignment, robustness, reproducibility and explainability. While fairness, accountability and value alignment embody our social responsibility; robustness, Reproducibility and explainability pose massive technical challenges to us.

At Facebook, Stan Chudnovsky oversees the Messenger chat app that is utilized by well more than 1 billion individuals every month. He's playing a key role in helping Facebook integrate that app with its other chat tools, WhatsApp and Instagram Direct.

The massive project has already gotten pushback from regulators worried about Facebook's size and power. Government officials also worry about Facebook's plans to extend end-to-end encryption to Messenger. Once that happens, Facebook wouldn't be able to respond to law enforcement subpoenas because it wouldn't have a way to unscramble messages.

Chudnovsky, who moved to the US from Russia in 1994, joined Facebook in 2015. He spoke with The Associated Press recently about his work and views on privacy. Questions and answers have been edited for length and clarity.

Q: What are the biggest roadblocks in bringing end-to-end encryption?

A: It's technologically hard to move from the system that is alive and functioning and has billions of messages being sent every day to where it's done completely differently architecturally. We also need to figure out how to do as much as we can on safety, while being the leaders on privacy. We are trying to go through that process slowly and very responsibly while talking to everyone.

Most messages in the US, where (Apple's) iMessage is leading, are already end-to-end encrypted. We want to make sure that we get to the point when we lead very strongly and we do as much on safety as we possibly can given the constraints of end-to-end encryption.

Q: How do you ensure that people are safe when you can't see bad things people are doing?

A: We are going to continue to work very closely with law enforcement on whatever we can provide. We also have connectivity to social networks. Whoever is a bad player on social networks, we will be able to see if those bad players exist on messaging services.

I don't want to go into details on how we are thinking about approaching that stuff. But we're just going to invest heavily in identifying threats earlier,

Q: You can send things in a private message that you can't post on Facebook, right?

A: Definitely. You should be able to send whatever you want to send in a private message.

Q: What if it's illegal or hurting someone?

A: Generally we believe that conversation between people should be private. We don't make a difference between the conversations that are happening in the living room or on the phone and conversations that are happening in a private chat.

Q: What if you try to sell a gun, despite Facebook's ban?

A: If you're trying to sell a gun, you are probably trying to sell a gun to many people. When someone reports that and someone provides the messages that from the point of that person are illegal, then definitely we will be able to look at that.

Q: What are the biggest things that you have to figure out before interoperability becomes reality?

A: Generally, just a features compatibility in the sense that, if I “like” some message on one app, how does it manifest itself in another? Or will I be able to also call people, not only send messages?

Q: Do you think scrutiny of Facebook will ease any time soon?