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Rebranding gives businesses an image refresh and a competitive edge in an era of dynamic markets. Google underwent it in 2015, creating the Alphabet we now know as its parent company. Its birth has made it possible for its divisions to operate independently. Each remains a part of the company while handling projects beyond the internet search engine, advertising, or making new Google Pixel phones.

Google remains the Google you know but falls under Alphabet as a subsidiary and the largest shareholder. It runs alongside Waymo, Calico, and other companies in its diverse portfolio. Learn more about Alphabet, who runs it, and other information in this post.

Alphabet is a multinational technology company that Larry Page and Sergey Brin created on October 2, 2015. Page and Brin are Google's co-founders and restructured the popular technology company to expand and diversify their operations.

Alphabet and Google aren't the same. The former became the parent company, and the latter is now a subsidiary of it. Google shares have also converted to Alphabet stock and retain their ticker symbols as GOOG (Class C shares without voting rights) and GOOGL (Class A common stock) on the NASDAQ stock exchange and other platforms.

According to Page in an open letter, the name Alphabet fits the rebranding as it's a "collection of letters that represent language, one of humanity's most important innovations, and is the core of how we index with Google search." It also reflects in the website address as abc.xyz.

Nothing about how you use Google's products and services has changed. The Workspace apps, YouTube, and Maps, among others, remain intact. The difference is in the corporate structure. Current and future subsidiaries under Alphabet have more autonomy to chase separate goals and enter new markets.

Also, Alphabet began generating financial reports in three segments on a quarterly basis. They report the profit and losses for Google Services, Google Cloud, and Other Bets. Before that, there were reports for only Google and Other Bets. The segments operate as follows:

Google's overhaul makes the new company more accountable. Its introduction of the above divisions allows investors to monitor the financial performance of core services and startup projects. It also isolates the risk attached to each subsidiary, where one could fail or face roadblocks without affecting the others.

Different shareholders and investors own Alphabet as it's a public-traded company. Google's co-founders, Larry Page and Sergey Brin, hold its Class B shares. It gives them 10 times more voting rights, even though they own a small percentage of the total shares. Class A shares have only one vote per share, while Class C has none.

Also, B shares aren't public. Hence, they don't exist on stock exchanges and allow the founders and CEOs to control the company's direction and decision-making. In terms of executive positions, Sergey Brin was Google's President from the company's founding date in 1998 until 2019.

Meanwhile, Page acted as the CEO three times. First, from the founding date until 2001, then from 2011 to 2015. That same year, he became Alphabet's CEO and handed his position at Google over to Product Chief Sundar Pichai. Both co-founders stepped down from their positions in 2019 but retained board membership and are still major shareholders.

Pichai is now Google and Alphabet's CEO. He was the brain behind ChromeOS and played a pivotal role in Nest's acquisition, among other achievements.

Under the Alphabet umbrella are Google and Other Bets. Other Bets are companies still in their early or experimental stages and operate independently of the core internet services. Below are some of the subsidiaries Alphabet oversees:

Google's transformation story embraces change and progress, an effort that may continue to bring financial success and tackle public scrutiny concerning user data privacy. Post-restructuring, the new company has raised mixed reactions from supporters and critics. Some say that it may be setting unrealistic goals in the name of pursuing new horizons.

One includes Google Fiber and Webpass, two services meant to deliver fast internet and phone privileges to you via a physical line. Already, the company has had to pause operations in numerous cities and made massive layoffs. Speculations are abuzz about low demand and financial setbacks. But innovation is risky, and only time will tell if Alphabet's moonshot projects succeed.

Read more:
Alphabet: The complete guide to Google's parent company - Android Police

PRESS RELEASE

Published October 6, 2023

The impact of AI and ML on modern business environments is more than fascinating; it's critical in today's hyper-connected world. While AI and ML have far-reaching practical applications, their greatest disruptive influence may be in business revenue. In this piece, I'll break out why artificial intelligence and machine learning aren't just "nice to have" but a "must have" for any company serious about long-term success.

The Importance of AI and ML in Generating Revenue

In today's data-driven and rapidly evolving environment, tried and true money-generation techniques are no longer sufficient. McKinsey reports that companies using AI in their operations boost revenue by 20% and save expenses by 30%.ChatGPT, GitHub Copilot, Stable Diffusion, and other generative AI applications have captivated global interest due to their widespread accessibility and user-friendly interfaces.

Unlike AlphaGo, which had a more specialized focus, these tools offer almost anyone the ability to communicate, create, and engage in uncanny discussions with a user. It's not merely a wave of the future; it's today's currency.

Practical Applications of AI and ML in Revenue Generation

Several revenue-generating uses for AI and ML exist:

These features may be added to your company model incrementally over time rather than all at once.

Challenges to Adoption and Solutions

The apparent complexity of the technology, concerns over data privacy, and the early expense of deployment are the most prevalent obstacles to AI/ML adoption. Based on my expertise in Turn-Key Design and Systems Integration, I would suggest a staged adoption, beginning with smaller projects to show rapid wins and ROI. In addition, working with other IT companies helps soften the change and save startup expenses.

Increasing Productivity While Lowering Expenses

AI/ML is a tool for improving the efficiency of an organization in addition to helping it make more money. With machine learning, everyday tasks are taken care of by computers. This frees up people to work on more complicated tasks and reduces technical debt. The production can also benefit from AI's ability to simplify back-end activities.

Tendencies and Prospects for the Future

The mutually beneficial connection between AI and ML and their earning potential will deepen as technology advances. Companies that don't change with the times will likely fail in today's fiercely competitive economy.

Final Thoughts

No company that wants to expand its income in a scalable and sustainable way can afford to ignore artificial intelligence and machine learning. It's not a matter of 'if,' but 'when,' AI/ML will become essential to your company's operations.

Who is Waleed Nasir?

Throughout his career, visionary builder and technology specialist Waleed Nasir has launched over a hundred platforms and led countless system deployments and workflow integrations. Dr. Waleed has extensive technical expertise in AI and ML and practical experience building and expanding technology companies. Notable examples of his work include the COVID-19 Crisis Management System, the Paycheck Protection Plan's Programmatic Loan Forgiveness System, and the Emergency Rent Relief Administration System. His wide-ranging skillset includes not just Turn-Key Design but also Process Automation and High-Performance Infrastructure, making him an industry leader in areas beyond only technological innovation. Currently, Dr. Waleed is working with Qult Technologies as the CPO, leading the company to new fronts.

Additional Resources

For those interested in diving deeper into this subject, I recommend:

Media Contact Company Name: qult.ai Contact Person: Hassan Tariq Malik Email: Send Email Country: United Kingdom Website: https://www.qult.ai/about-us/

Excerpt from:
How AI and ML Can Drive Sustainable Revenue Growth by Waleed ... - Digital Journal

Hong Kong based Insilico Medicine, a pioneer in AI-based drug discovery, has made significant strides in recent years. Two of their candidates have reached clinical trials, with INS018-055 leading the pack as the first AI-discovered drug designed by generative AI to enter phase 2 clinical trials for idiopathic pulmonary fibrosis (IPF). Back in 2014, when the company began, AI for drug discovery was relatively unheard of, but now it's an indispensable part of the drug discovery process. Insilico's partnerships with major pharmaceutical firms like Janssen underscore the growing importance of AI in this field. Dr Alex Zhavoronkov, Founder and CEO of Insilico Medicine, sheds light on the industry's evolving response to AI in drug discovery, partnerships, regulatory reforms etc. and also shares the company's future plans.

Insilico Medicine has garnered attention for its innovative utilisation of artificial intelligence (AI) in drug discovery. Could you provide insights into how the industry's response to AI-based drug discovery has evolved since your inception in 2014?

In the early days, when I presented at conferences on how generative AI technology could be applied to chemistry, there was a lot of scepticism. I had discovered through my research that generative adversarial networks (GANs) combined with deep reinforcement learning (the same AI learning strategy used in AlphaGo) could generate novel molecules that could be used to treat disease. Since that time, AI drug discovery has undergone enormous acceleration, fueled both by advances in AI technology and in massive stores of data. While there are still no AI-designed drugs on the market, there are a number of companies with these drugs in advanced clinical trials, including our own lead drug for idiopathic pulmonary fibrosis, the drug with an AI-discovered target and designed by generative AI now in Phase II trials with patients.

Although the pharma industry has moved cautiously, the inherent risks in drug discovery (99 per cent of the drugs fail in the early discovery phase and 90 per cent of the drugs fail in clinical trials) and the validation of AI developed drugs to reach advanced trials, means that pharma companies are more actively pursuing partnerships and developing their own internal AI programmes. We have major partnerships with Exelixis, Sanofi and Fosun Pharma to develop new therapies, for instance.

Recently, your two candidates INS018_055, ISM8207 have entered phase II and phase I respectively. Can you share the significance of reaching these stages in the drug development process, and what key milestones do you hope to achieve during these trials?

To our knowledge, Insilicos lead drug for IPF INS018-055 - is the first drug for an AI-discovered target and designed by generative AI to reach Phase 2 clinical trials with patients.

AI was used in every stage of the process. Insilico Medicine used its AI target-discovery engine, https://insilico.com/pandaomics, to process large amounts of data including omics data samples, compounds and biologics, patents, grants, clinical trials, and publications to discover a new target (called Target X) relevant for a broad range of fibrosis indications. We then used this newly discovered target as the basis for the design of a potentially first-in-class novel small molecule inhibitor using its generative AI drug design platform, Chemistry42.

Insilicos molecule INS018_055 - demonstrated highly promising results in multiple preclinical studies including in vitro biological studies, pharmacokinetic, and safety studies. The compound improved myofibroblast activation, a contributor to the development of fibrosis, with a novel mechanism and was shown to have potential relevance in a broad range of fibrotic indications, not just IPF.

The current phase II study is a randomised, double-blind, placebo-controlled trial to assess the safety, tolerability, pharmacokinetics and preliminary efficacy of 12-week oral INS018_055 dosage in subjects with IPF divided into 4 parallel cohorts. To further evaluate the candidate in wider populations, the company plans to recruit 60 subjects with IPF at about 40 sites in both the US and China.

If our phase IIa study is successful, the drug will then go to phase IIb with a larger cohort. This is also the stage where our primary objective would be to determine whether there is significant response to the drug. The drug will go on to be evaluated in a much larger group of patients typically hundreds in phase III studies to confirm safety and effectiveness before it can be approved by the FDA as a new treatment for patients with that condition. We expect to have results from the current phase II trials next year.

Advancing ISM8207 is also significant both because it is the first clinical milestone reached in our partnership with Fosun, and also because it is the first of our cancer drugs to advance to the clinic, and cancer represents the largest disease category in Insilicos pipeline. This drug is a novel QPCTL inhibitor, designed to treat advanced malignant tumours, and works by blocking the tumour cells dont eat me signal. We entered into phase I clinical trials to assess the drugs safety in healthy volunteers in July 2023.

You have had quite successful partnerships with Exelixis, Fosun etc. Can you provide insights into Insilicos approach to forming strategic partnerships? How do you approach deal making?

We have the advantage of being able to produce and advance new, high quality small molecules that have been optimised to treat diseases much more quickly than traditional drug discovery methods. Thats because our generative AI system can optimise across 30 parameters at once based on desired criteria when generating molecules, rather than the traditional method of screening libraries to find a potential compound, and then working to optimise it for each desired property in a linear fashion. As we speed up the drug discovery process on these high-quality molecules we now have 31 in our pipeline we look to find partners who have specific disease expertise and clinical experience to advance these molecules into later stage clinical studies, and, we hope, to market where they can begin helping patients.

Our most recent partnership with Exelixis is a perfect example. We just announced an exclusive global licence agreement with Exelixis with $80 million upfront granting Exelixis the right to develop and commercialise ISM3091, an AI designed cancer drug and potentially best-in-class small molecule inhibitor of USP1 that received IND approval from the FDA in April 2023. This company is expert in cancer and cancer drug development and discovery, and has an expert drug hunting team. Because its an extremely innovative company, they already have substantial revenue coming from best-in-class cancer therapeutics and they are strengthening this pipeline and making bets on innovative cancer drugs.

If we were to look at one of your AI-designed drugs versus a traditionally designed drug candidate, is there a telltale signature?

Our AI-designed drugs will often have a novel structure or work via a novel mechanism compared to existing drugs. By optimising across these 30 different parameters to design molecules with just the right structure and properties to provide the best likelihood of treatment without toxicity and minimal side effects, we are essentially designing ideal new drug-like molecules from scratch. There may be other drugs that are designed to act on those same targets, but ours are optimised through structure or mechanism to be most efficacious, first-in-class, or best-in-class.

Until recently perhaps, big pharma was somewhat sceptical or resistant to AI. What has been responsible for this growing appetite to embrace AI as a fundamental part of the drug discovery process?

There are a number of reasons pharma is now embracing AI. Traditional drug discovery is an incredibly slow and expensive process that fails in clinical trials 90 per cent of the time. AI improves all three of those roadblocks improving speed, lowering cost, and optimising molecules to have the greatest likelihood of clinical trial success. Our AI engine known as PandaOmics can sift through trillions of data points quickly to identify new targets for disease that humans might not find. Then, our generative AI Chemistry42 platform can design brand-new molecules that are optimised to interact with those targets without causing adverse effects, scoring them based on which are likely to work the best. Finally, using our InClinico tool, we can predict how these drugs will likely fare in clinical trials to reduce the time and money lost on failed trials.

There is also now significant validation that this method of developing new drugs is producing very high quality new drugs for hard-to-treat diseases and even diseases that were considered undruggable. And a number of these AI-designed drugs are now in later stage clinical trials.

Finally, the technology is itself progressing and improving with additional use and data via reinforcement learning and expert human feedback. The better the AI gets, the harder it is to ignore.

How sceptical are regulatory bodies towards AI-driven drug discovery? How are regulations evolving to support such developments?

Data privacy and protection are critical to any businesses utilising AI, as is compliance with all international laws and regulations. I expect that these measures will become more stringent in coming years and they are essential to building and maintaining public trust. Insilico Medicine uses only publicly available data and employs privacy by design and by default. We facilitate security of our systems by thorough security analysis on each phase of development. All Insilico data hubs are contained in Amazon Web Services (AWS) or Microsoft Azure cloud.

In addition, there are several checks and balances in place to ensure continuous data integrity, protection and privacy. For example, clients data is not used in any internal environments of the platform, and a firewall is separated for the clients access to the platform versus everyone elses access. All data is encrypted, and data privacy is managed according to Insilico Medicines privacy policy.

What does the future hold for Insilico over the next few years?

Were eager to see our clinical stage programmes progress, and the continued advancement of our lead drug for IPF. Its a terrible, chronic condition with a very poor prognosis and patients are in desperate need of new treatment options.

I also hope that our latest deal with Exelixis marks a trend of pharma companies partnering earlier in the drug development process with highly optimised AI-designed molecules as we continue to expand our pipeline, so that we can truly accelerate the process of delivering new treatments to patients in need.

We will also continue to expand the capabilities of our end-to-end generative AI platform, through new data, reinforcement learning, and expert human feedback; and augment those capabilities with our AI-powered robotics lab as well as incorporating the latest technological tools into our platform, including AlphaFold and quantum computing both of which weve published papers on.

Ayesha Siddiqui

Read the original here:
The better the AI gets, the harder it is to ignore - BSA bureau

But then, as statecraft in the real world came to favor game theory over traditional diplomacy, the metagame likewise shifted. Online players were no longer calling one another into solaria or billiards rooms to speechify about making the world safe for democracy. Games became shorter. Communication got blunter. Where someone playing Diplomacy by mail in the 1960s might have worked Iago-like angles to turn players against one another, a modern player might just text CON-BUL? (For Constantinople to Bulgaria?)

This is the current Diplomacy metagame. Game theory calculations undergird most utterances, and even humans communicate in code. Lerer joked that in modern-day online Diplomacy, even human players wouldnt pass the Turing test. Before Cicero, it seems, humans had already started playing like AIs. Perhaps, for an AI to win at Diplomacy, Diplomacy had to become a less human game.

Kostick, who won a European grand prix Diplomacy event in 2000 and was on the Irish team that took the Diplomacy National World Cup in 2012, misses the old style of gameplay. The whole purpose of Allan Calhamers design of the game, he told me, is to create a dynamic where the players all fear a stab and yet must deploy a stab or a lie to be the only person to reach 18.

Kostick believes that while he would have been delighted with the practical results of Ciceros website play, Metas project misses the mark. Ciceros glitches, Kostick believes, would make it easy to outwit with spam and contradictory inputs. Moreover, in Kosticks opinion, Cicero doesnt play real Diplomacy. In the online blitz, low-stab game Cicero does play, the deck is stacked in its favor, because players dont have to lie, which Cicero does badly. (As Lerer told me, Cicero didnt really understand the long-term cost of lying, so we ended up mostly making it not lie.) Kostick believes Ciceros metagame is off because it never knowingly advocates to a human a set of moves that it knows are not in the humans best interest. Stabbing, Kostick believes, is integral to the game. A Diplomacy player who never stabs is like a grandmaster at chess who never checkmates.

With some trepidation, I mentioned Kosticks complaint to Goff.

Unsurprisingly, Goff scoffed. He thinks its Kostick and his generation who misunderstand the game and give it its unfair reputation for duplicity. Cicero does stab, just rarely, Goff said. I reject outright that [compelling players to stab] was Calhamers intent.

I could tell we were in metagame territory when Goff and Kostick began arguing about the intent of the games creator, as if they were a couple of biblical scholars or constitutional originalists. For good measure, Goff bolstered his case by citing an axiom from high-level theory and invoking an elite consensus.

Regardless of Calhamers intent, game theory says, Dont lie, he told me. This is not controversial among any of the top 20 players in the world.

For one person or another to claim that their metagame is the real onebecause the founder wanted it that way, or all the best people agree, or universal academic theory says x or yis a very human way to try to manage a destabilizing paradigm shift. But, to follow Kuhn, such shifts are actually caused when enough people or players happen to align with one vision of reality. Whether you share that vision is contingent on all the vagaries of existence, including your age and temperament and ideology. (Kostick, an anarchist, tends to be suspicious of everything Meta does; Goff, a CFO of a global content company, believes clear, non-duplicitous communications can advance social justice.)

Read this article:
What If the Robots Were Very Nice While They Took Over the World? - WIRED

AI isnt just about tech; it concerns morality, ethics, emotions, and more. The power to handle the potential threats of AI lies not with the experts but with all of us. Imagine a future where we might either live off-grid due to AI domination or freely enjoy nature due to AI convenience.

Gawdat shares a prophecy, acknowledging his role in the rise of AI and the consequential loss of human essence. He draws attention to how one AIs mistake becomes a lesson for all AI, and how by 2049, AI could be a billion times smarter than the smartest human, reaching a point of singularity, a moment we cant predict.

AI doesnt inherit values from the codes we write, but from the information we feed it. So how do we ensure AI values humanity? Many suggest control measures, but thats short-sighted; instead, we should aim not to contain AI at all, raising it like a good parent would raise a child.

The evolution of our intelligence is evident in human society itself. Variances in intelligence types across different societies result from what we call Compounded Intelligence.

Humans have fantasized about intelligent machines for millennia, seen in Greek myths, Middle Ages alchemical works, and legends from different cultures. By the 19th century, artificial beings were common in popular fiction.

The journey towards AI has been an incremental process with attempts at building animated humanoids throughout human history. From automata in ancient Egypt and Greece to the creative inventions of the Muslim polymath Ismail al-Jazari in the 12th century, humanity was drawn to imitating life artificially. Hoaxes like the Mechanical Turk in the 18th century also spurred interest.

Early computers werent smart; they just performed tasks faster. Google, Amazon, and Spotifys seemingly smart features were just results of algorithms summarizing collective human intelligence. However, the shift towards machines developing their own intelligence started around the turn of the 21st century.

With the ascension of machine learning and artificial intelligence into the mainstream conversation during the waning years of the 20th century, a trend emerged, accelerating into a widespread obsession as the new millennium dawned. After years of trial and failure, glimmers of hope began to sparkle in the form of a non-human, non-biological intelligence. Unless one has made a humble home amongst the primates in the secluded heart of Africa, the term AI likely rings through their auditory canals numerous times a week. Yet, the clamor of this phrase is by no means a recent phenomenon. The digital devotees among us have been immersed in fervent discourse about AI since the halcyon days of the 1950s.

Ever since the year of 1951, the grand game of life has been played not only by humans, but machines as well. Today, these machines wear the crown of every game they partake in. The inaugural game a machine had a stab at was draughts, or checkers, courtesy of a program conceived by Christopher Strachey for the Ferranti Mark 1 machine stationed at the University of Manchester. Chess was the next domino to fall, thanks to the efforts of Dietrich Prinz. Then came Arthur Samuels checkers program, born in the cradle of the mid-1950s to early 1960s, which managed to accrue sufficient skill to test the mettle of a respectable amateur player. Though a humble intelligence, to say the least, the trajectory from these roots to our current reality is staggering. The human monopoly over games began to crumble, with backgammon in 1992, checkers in 1994, and by 1999, IBMs Deep Blue claimed a victory over the reigning chess world champion, Garry Kasparov.

Then, the floodgates opened in 2016, when humanity ceded the gaming realm entirely to a subsidiary of the technological behemoth, Google. For years, Googles DeepMind Technologies had sharpened the axe of artificial intelligence through the grindstone of gaming. In 2016, they unveiled AlphaGo a computer AI endowed with the capability to play Go, an ancient Chinese board game known for its complexity. This game harbors a virtually infinite array of strategies at any given juncture. To comprehend the sheer magnitude of this, consider that the number of potential moves in Go dwarfs the number of atoms in the entire cosmos. This renders it an insurmountable challenge for a computer to compute every possible move. Even if there were sufficient computational prowess to accomplish this feat, it would arguably be put to better use simulating the universe rather than playing a game. The victor in Go requires intuition and intelligent thinking, akin to a human but with a twist of added smarts. This is the formidable mountain that DeepMind managed to summit.

In March of 2016, a decade earlier than the most sanguine of AI analysts had anticipated, AlphaGo trounced champion Lee Sedol, the second-ranked player worldwide in Go, in a five-game match. Fast forward a year to 2017, and its successor, AlphaGo Master, bested Ke Jie, the then-worlds top-ranked player in Go, in a three-game series. Thus, AlphaGo Master ascended the throne as world champion. With no humans left to conquer, DeepMind spawned a new AI from scratch AlphaGo Zero to challenge AlphaGo Master. Within a mere training period, AlphaGo Zero clinched a flawless victory against the reigning champion. Its successor, the self-taught AlphaZero, is currently perceived as the world champion of Go. Moreover, the same algorithm was put to the test in chess and claimed the world championship title there as well.

Machine learning hasnt stopped at games. Its been expanding its understanding of human language since 1964. The first milestone was Daniel Bobrows program STUDENT, designed to comprehend and solve word problems of high school algebra caliber, an accomplishment many students still grapple with today. Simultaneously, Joseph Weizenbaums ELIZA, the inaugural chatbot, carried on conversations so realistic that users were occasionally fooled into thinking she was human. Her digital progeny, like Amazons Alexa, Google Assistant, Apples Siri, and Microsofts Cortana, have made enormous strides since then, not only understanding us humans but also passing the Turing test on occasion.

Computers today are not only capable of reading text via optical character recognition but can also identify objects in images or the real world through object recognition. They discern items plucked from the shelves in an Amazon Go store, provide information about historical monuments when you point your phone at them, detect vehicles crossing toll stations, and even identify abnormal cells in medical images. It is this ability to perceive and understand that makes computers the smartest visual observers, surpassing even human capabilities.

The purpose of recounting this progression is to underscore the trajectory of the trend. If one were to assume that we have arrived at this point after 75 years, they might predict it would take decades more to experience any meaningful implications of artificial intelligence in our lives. Yet, as with all technologies, progress starts at a crawl before breaking into a full sprint. The advancement of artificial intelligence, now moving at an exponential pace, is poised to deliver a future over the next decade that may seem more akin to fiction than the reality of our present day.

Read more:
From Draughts to DeepMind (Scary Smart) | by Sud Alogu | Aug, 2023 - Medium